Local Indicator of Spatial Association (LISA) and Moran’s I in Spatial Political Analysis of the 2024 Election in Central Sulawesi, Indonesia

Local Indicator of Spatial Association (LISA) and Moran’s I in Spatial Political Analysis of the 2024 Election in Central Sulawesi, Indonesia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Local Indicator of Spatial Association (LISA) and Moran’s I in Spatial Political Analysis of the 2024 Election in Central Sulawesi, Indonesia Andi Hartati, Rahmad Rahmad, Siska Siska This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7730694/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This Study investigates the spatial dynamics of voter participation and party support in the 2024 legislative election in Central Sulawesi Province, Indonesia. Drawing on official electoral data from the General Election Commission (KPU), we analyze 2,236,603 registered voters across 13 districts and cities, with an overall turnout rate of 81.62%. Employing spatial statistical methods, Global Moran’s I and Local Indicator of Spatial Association (LISA), implemented through Geoda, the study explores whether electoral outcomes reveal clustered, dispersed, or random spatial patterns. The results demonstrate that voter turnout exhibits significant geographical disparities, with higher participation in mainland districts such as Parigi Moutong (80%) and Buol (85%), and lower participation in peripheral island areas such as Banggai Laut (85% but numerically small) and Palu (76%). Spatial autocorrelation analysis reveals strong clustering for several parties, notably PDIP (Moran’s I = 0.61, p < 0.01), Demokrat (0.35, p 0.05), display random distributions. LISA results further identify localized hotspots of party support (e.g., PDIP in Toili-Banggai, Demokrat in Luwuk, and Nasdem in Tolitoli) as well as coldspots in weaker areas, highlighting fragmented competition in urban centers such as Palu. These findings underscore the importance of spatial approaches to electoral analysis in emerging democracies. They also carry practical implications for electoral management bodies to address turnout disparities and for political parties to refine geographically targeted campaign strategies. Spatial autocorrelation Moran’s I LISA Electoral geography Voter participation Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Elections remain the cornerstone of representative democracy(Papp et al., 2024 ), serving as the primary institutional mechanism through which citizens articulate preferences, shape governance outcomes, and hold political elites accountable(Otjes et al., 2025 ). In Indonesia, the third largest democracy in the world(Aspinall, 2014 ), elections have acquired heightened importance since the post-1998 Reformasi era, which replaced centralized authoritarian rule with a more decentralized and competitive democratic system(Aspinall, 2013 ). Legislative elections(Rifai & Haeril, 2025 ), in particular, are critical not only for determining the composition of the national and regional parliaments but also for revealing the geographic contours of political support across Indonesia’s diverse socio-political landscapes(Novrizal, 2024 ). Understanding electoral behavior in Indonesia(Iftitah et al., 2025 ) is therefore essential to both academic scholarship on democratization(Testriono, 2022a ) and the formulation of policies aimed at sustaining democratic consolidation(Gimpel et al., 2008 ). The 2024 legislative election represented the latest large-scale electoral exercise in Indonesia, involving more than 200 million voters nationwide(Asmorojati & Suyadi, 2023 ). In Central Sulawesi Province, one of the country’s peripheral yet politically dynamic regions(Sumarto & McCarthy, 2025 ), 2.23 million registered voters participated in the election(KPU, 2024 ), yielding a turnout rate of 81.62%. While aggregate statistics provide an overall picture of voter engagement, they mask significant sub-provincial variations in participation and party support. Districts such as Parigi Moutong and Buol recorded relatively high turnout, while urban centers’ territorial patterns: some concentrated in localized strongholds, while others displayed dispersed or fragmented support. These variations raise important questions about the spatial dynamics of electoral behavior in Indonesia(Fraenkel & Aspinall, 2013 ). Electoral geography(Johnston & Pattie, 2011 ) offers a valuable lens to analyze such dynamics(McDonald, 2004 ), as it emphasizes the spatial distribution of votes, turnout, and political alignments across geographic units(Johnston & Pattie, 2008 ). A central concern of electoral geography(Warf, 2009 ) is whether electoral outcomes are spatially clustered, randomly distributed, or dispersed, a question that has direct implications for party strategy, representation, and democratic equity(Agnew, 1996 ). While global patterns of electoral geography have been studied extensively in advanced democracies)(van Noord et al., 2018 ), applications to emerging democracies in Southeast Asia remain comparatively underdeveloped(Testriono, 2022a ). In particular, studies of Indonesia’s elections have tended to focus on clientelism(Okthariza, 2022 ), money politics, and party system fragmentation(Fink-Hafner & Novak, 2022 ), but have seldom employed rigorous spatial(García-Díaz et al., 2013 ) statistical techniques to assess clustering and spatial dependencies in electoral outcomes(Szmolka, 2024 ). Spatial statistics provide powerful tools for extending electoral geography beyond descriptive mapping(Grekousis, 2020 ). Moran’s I(R. Bivand et al., 2009 ) and Local Indicators of Spatial Association (LISA)(Anselin, 1995 ), are widely used to detect global and local spatial autocorrelation. LISA(Scharf, 2022 ) enables the identification of whether electoral results in neighboring districts exhibit clustering (high-high or low-low) or dispersion. (high-low), or randomness. LISA enables the identification of localized clusters or outliers that might otherwise be obscured in province-wide analyses(Rogerson & Kedron, 2012 ). Together, these tools permit a systematic exploration of the spatial structure of voter turnout and party support(Yandri, 2017 ). Their application to Indonesian elections can thus contribute to both theoretical advancements in spatial political analysis(R. S. Bivand & Wong, 2018 ) and practical insights for electoral management(Rezaee et al., 2024 ). Central Sulawesi provides a compelling case study for applying such methods. The province is characterized by geographic diversity, spanning mainland districts, coastal areas, and remote island regencies(Astari et al., 2024 ). Its political history has been shaped by both national-level party competition and local socio-cultural cleavages(Caramani, 2024 ). The 2024 election further highlighted the unevenness of political participation and support bases, with major parties such as PDIP, PKB, Demokrat, and Golkar showing clustered strongholds, while others such as Nasdem and Gerindra displayed more fragmented patterns. These dynamics underscore the relevance of spatial approaches to electoral analysis(Lin, 2023 ) in the Indonesian context. This study addresses the research gap by applying Moran’s I and LISA to analyze voter turnout(Jubit et al., 2023 ) and party support(Karp, 2012 ) in Central Sulawesi’s 2024 legislative election. Specifically, the research pursues three objectives: To measure the spatial distribution of voter turnout across 13 districts and cities in Central Sulawesi, and to identify patterns of inequality in electoral participation. To assess the degree of spatial autocorrelation in party support using Moran’s I, thereby evaluating whether major political parties exhibit clustered, dispersed, or random territorial bases. To detect localized hotspots and coldspots of party support using LISA, thus uncovering spatial nuances that can inform both electoral theory and political strategy. By integrating electoral geography with spatial statistics, this study makes two main contributions. First, it enriches scholarly understanding of Indonesia’s electoral dynamics(Busroh & Khairo, 2023 ) by moving beyond descriptive accounts toward quantitatively rigorous spatial analysis. Second, it offers policy-relevant insights: identifying spatial inequalities, while mapping party strongholds, can inform political actors’ approaches to coalition building and campaigning. The findings thus hold implications not only for the academic literature on spatial politics but also for the practice of democratic governance in Indonesia and comparable emerging democracies(Testriono, 2022b ). 2. Literature Review 2.1. Electoral Geography and Spatial Dimensions of Voting Electoral geography(Forest, 2018 ), a subfield of political geography(Shin, 2015 ), investigates the spatial patterns of voting behavior, political party support, and representation(Li & Fotheringham, 2022 ). It seeks to understand how geographic space influences, and is influenced by, electoral processes(Johnston, 2002 ). Foundational works in this tradition emphasize the spatial clustering of political legacies in shaping electoral outcomes(Flint, 2002 ). Electoral geography is not confined to mapping results(Wiedemann, 2024 ); it interrogates the structural relationships between territory, identity, and political mobilization, thus bridging geography with political science(O’Grady & Wiedemann, 2024 ). At the global level, electoral geography has been instrumental in explaining phenomena such as partisan polarization in the United States(Chen & Rodden, 2013 ), regional cleavages in Europe(Caramani, 2024 ), and ethnic or religious voting in Africa and Asia(Aidoo & Botchway, 2021 ). In each of these cases, spatial patterns are neither accidental nor random; rather, they reveal underlying socio-political structure and inequalities(Roberts, 2022 ). A central concern is whether electoral outcomes exhibit territorial clustering(Wiedemann, 2024 ), suggesting the existence of strongholds and localized political cultures(Reilly, 2019 ), or whether they are fragmented(Huang et al., 2022 ), signaling fluidity in voter preferences(Chan et al., 2024 ). In the Indonesian context, electoral geography is particularly salient given the country’s archipelagic geography and ethno-religious diversity(Warganegara & Waley, 2024 ). However, most studies have analyzed elections from the perspectives of patronage politics(Istania, 2022 ), clientelism, and money(Berenschot & Aspinall, 2022 ). While these approaches shed light on the socio-political mechanisms of vote mobilization(Aytaç et al., 2025 ). They have paid less attention to the spatial distribution of votes and turnout(Boyle, 2024 ). Consequently, the territorial logics of Indonesia’s elections remain underexplored. 2.2. Spatial Autocorrelation in Electoral Studies Spatial autocorrelation is the statistical measure of the degree to which a spatial phenomenon in one location is similar to that in neighboring locations(Yang et al., 2023 ). Positive autocorrelation indicates that areas with dissimilar values(Pregi & Novotný, 2025 ) (high adjacent to low) are spatially proximate(Z. Zhang et al., 2024 ). The absence of spatial autocorrelation helps identify whether political support or voter turnout is geographically concentrated or disperseds(Griffith, 2024 ). Global Moran’s I is one of the most widely used indices for measuring overall spatial autocorrelation across a region(Jaber et al., 2022 ). A positive and significant Moran’s I indicates clustering, while a negative and considerable value implies dispersion. However, global measures often conceal localized variations(Westerholt, 2023 ). To address this, Anselin ( 1995 )(Yu et al., 2025 ) introduced Local Indicator of Spatial Association (LISA), which identifies spatial clusters (High-High, Low-Low) and outliers (High-Low, Low-High) at finer scales. These techniques have transformed electoral geography from descriptive cartography into a statistically robust field. Applications of Moran’s I and LISA in political analysis are well documented(C. Zhang et al., 2023 ). For instance, Griffith and Jones (1980)(Griffith, 2023 ) used spatial autocorrelation to detect partisan clustering in U.S. counties, while (Johnston et al., 2021 )applied similar methods to study voting patterns in the United Kingdom. More recent studies employ these tools to investigate gerrymandering(Trelles et al., 2024 ), regional polarization(Chen & Rodden, 2013 ), and turnout inequalities(Tam Cho et al., 2013 ). These works highlight the capacity of spatial statistics to uncover hidden structures in electoral data that would otherwise remain invisible in non-spatial analysis. 2.3. Spatial Inequalities in Voter Participation Turnout disparities represent another critical dimension of electoral geography(Barber & Holbein, 2022 ). Research has shown that geographic variations in turnout are shaped by accessibility to polling stations, socioeconomic inequalities, and institutional arrangements(Gimpel & Reeves, 2022 ). Spatial autocorrelation methods allow researchers to detect whether low-turnout areas are geographically clustered(Maškarinec, 2024 ), thereby signaling systematic exclusions, or whether turnout is randomly distributed. In emerging democracies(Rafique et al., 2023 ), turnout inequalities often reflect deeper structural constraints such as poor infrastructure(Martín-Legendre & Rungo, 2025 ), limited voter education, or geographic isolation(Simon et al., 2024 ). For instance, studies in Sub-Saharan Africa and South Asia reveal that rural and peripheral areas frequently register lower participation, Bratton (2013) (Datta, 2023 ). In Indonesia, anecdotal evidence points to similar patterns(Fawzia et al., 2023 ), especially in remote island regions where logistical barriers impede electoral access. Yet, systematic spatial analyses of turnout disparities remain scarce. 2.4. Electoral Geography in Indonesia Indonesia’s elections have been widely studied from a political science and area studies perspective(Handoko et al., 2023 ). Much of the literature emphasizes the role of local elites(Noak, 2024 ), patronage networks(Apriliyanti, 2023 ), and money politics in shaping outcomes(Dettman, 2023 ). Studies also highlight the fragmented nature of the party system and the challenges of consolidating democratic accountability (Berenschot & Aspinall, 2022 ). However, relatively few studies apply quantitative spatial approaches. Exceptions include(Holt, 2008 ), who examined urban-rural cleavages in Jakarta’s gubernatorial election(Suhardi, 2025 ), who explored spatial clustering in ethnic voting in Papua(Sasmita, 2023 ). These contributions remain limited compared to the extensive application of Moran’s I and LISA in Western contexts. Given Indonesia’s geographic vastness and regional diversity, neglecting spatial statistical methods risks overlooking the key territorial dynamics of its electoral processes(Solehudin, 2024 ). 2.5. Research Gap and Contribution From the preceding discussion, two research gaps emerge. First, while electoral geography has been applied extensively in established democracies, its application in Indonesia remains embryonic, with little systematic use of spatial statistics. Second, existing Indonesian election studies largely privilege socio-political explanations over spatial ones, thereby underestimating the geographic structuring of voter behavior. This study addresses this gap by employing Moran’s I and LISA to analyze voter turnout and party support in Central Sulawesi during the 2024 legislative election. By combining descriptive electoral data with spatial statistical analysis, the research provides fresh insight into the geography of electoral participation and party clustering in Indonesia. Beyond contributing to the empirical understanding of one province, the study demonstrates the utility of spatial statistics for electoral analysis in emerging democracies more broadly. 3. Methods 3.1. Study Area Central Sulawesi Province, located in eastern Indonesia, was selected as the case study for this research. The province comprises 23 districts and cities, with a total registered electorate (Daftar Pemilih Tetap, DPT) of 2,236,603 in the 2024 legislative election. Its geography is highly diverse, encompassing mainland territories, coastal areas, and peripheral islands such as Banggai Kepulauan dan Banggai Laut. This geographic heterogeneity makes Central Sulawesi an ideal setting for investigating spatial disparities in electoral participation and party support. The province’s political context is similarly complex, with national parties competing for influence in both urban centers such as Palu and more remote, rural regions. 3.2. Data Sources The primary dataset was obtained from the General Election Commission (Komisi Pemilihan Umum, KPU) of Central Sulawesi which provides official records of registered voters. Voter turnout and valid votes for each party contesting the 2024 legislative election. The dataset covers all 13 administrative districts and cities in the province. Variables included in the analysis were: Voter turnout: measured as the percentage of registered voters casting valid ballots in each district. Party vote shares: measured as the proportion of valid votes received by each major party (e.g., PDIP, Golkar, PKB, Gerindra, Nasdem, Demokrat) at the district level. 3.3. Analytical Framework The analytical strategy combines descriptive electoral statistics with spatial statistical techniques. The approach is designed to answer three research questions: What is the spatial distribution of voter turnout across districts in Central Sulawesi? Do political parties exhibit significant spatial clustering in their electoral support? Where are the localized hotspots and coldspots of party support? 3.4. Global Moran’s I Moran’s I is a global measure of how spatial units are correlated (Cliff & Ord, 1981). It is computed as: $$\:\varvec{I}=\frac{\varvec{n}}{{\varvec{\Sigma\:}}_{\varvec{i}}{\varvec{\Sigma\:}}_{\varvec{j}}{\varvec{W}}_{\varvec{j}}}\:\text{x}\:\frac{{\varvec{\Sigma\:}}_{\varvec{i}}{\varvec{\Sigma\:}}_{\varvec{j}}{\varvec{W}}_{\varvec{i}\varvec{j}}\left({\varvec{X}}_{\varvec{i}}-\varvec{X}\right)\left({\varvec{X}}_{\varvec{j}}-\varvec{X}\right)}{{\varvec{\Sigma\:}}_{\varvec{i}}{\left({\varvec{X}}_{\varvec{i}}-\stackrel{-}{\varvec{X}}\right)}^{2}}$$ Where: n is the number of spatial units, \(\:{w}_{ij}\) represents the spatial weight between locations i and j , \(\:{x}_{i}\) is the value of the variable at location i , \(\:\stackrel{-}{x}\) is the mean of the variable, and \(\:W\) is the sum of all spatial weights. Values of Moran’s I were used to evaluate whether party vote shares within the district were spatially clustered, dispersed, or random across Central Sulawesi. 3.5. Local indicators of Spatial Association (LISA) While Moran’s I provides a global summary, it may obscure local heterogeneities. To address this limitation, Local Indicators of Spatial Association (LISA) were employed (Anselin, 1995 ). Local Indicators of Spatial Association (LISA) measure local spatial relationships to identify whether clustering occurs in aspecific location. In other words, LISA helps to determine if an area is part of a statistically significant cluster of similar values (High-High or Low-Low) or an outlier (High-Low or Low-High). It is computed as: $$\:{\varvec{I}}_{\varvec{i}}=\:\frac{\left({\varvec{X}}_{\varvec{i}}-\:\stackrel{-}{\varvec{X}}\right)}{{\varvec{S}}^{2}}\sum\:_{\varvec{j}}{\varvec{W}}_{\varvec{i}\varvec{j}}\left({\varvec{X}}_{\varvec{j}}-\:\stackrel{-}{\varvec{X}}\right)$$ Where: X i = the value of the variable of interest in region i \(\:\stackrel{-}{X}\) = the mean of the variable across all regions \(\:S\) 2 = the variance of the variable \(\:W\) ij = the spatial weight between region i and region j LISA identifies clusters and outliers by comparing each unit’s value with those neighbors. The results classify spatial patterns into four categories: High-High (HH): district with high values surrounded by high values neighbors (hotspots). Low-Low (LL): districts with low values surrounded by low-value neighbors (coldspots). High-Low (HL): district with high values surrounded by low value neighbors (outliers). Low-High (LH): district with low values surrounded by high value neighbors (outliers). LISA thus provides fine-grained insights into localized patterns of party support and turnout. 3.6. Spatial Weight Matrix Both Moran’s I and LISA require the specification of a spatial weight matrix ( W ) that defines the neighborhood structure. For this study, a contiguity-based Queen’s case spatial weight was adopted, where districts are considered neighbors if they share a common border or vertex. This approach is suitable given the polygonal administrative boundaries of Central Sulawesi. Row standardization of the weight matrix was applied to ensure comparability across districts with varying numbers of neighbors. 3.7. Software Implementation All spatial statistical analyses were conducted using GeoDa 1.20, an open-source software developed for spatial econometrics and visualization. Descriptive statistics and data cleaning were carried out in Microsoft Excel, while ArcGIS/QGIS was used to generate cartographic representations of turnout distributions, Moran’s I scatterplots, and LISA cluster maps. 3.8. Limitations Several methodological limitations must be noted. First, the use of district-level aggregation may mask intra-district heterogeneities, as electoral dynamics often vary at finer scale (e.g.,sub-district or village). Second, the reliance on official electoral data assumes accuracy and completeness, though discrepancies in voter lists and reporting cannot be fully ruled out. Finally, while Moran’s I and LISA identify spatial clustering, they do not explain the underlying causal mechanisms, which may include demographic, socio-economic, or infrastructural factors beyond the scope of this analysis. Despite these limitations, the chosen methods provide robust tools for uncovering spatial structures in electoral participation and party support. Their application in the Indonesian context represents a methodological innovation and a valuable contribution to the study of electoral geography in emerging democracies. 4. Results 4.1. Voter Turnout in Central Sulawesi The 2024 legislative election in Central Sulawesi recorded a provincial turnout of 81.62%, an increase from the 79.6% reported in the 2019 election. While this reflects generally high levels of electoral participation, turnout was not evenly distributed across districts. Table 1 presents voter turnout by district. Participation was highest in Buol (85%), Tojo Una-Una (85%), and Banggai Laut (85%), whereas relatively lower rates were observed in Palu (76.%), Poso (77%), and Tolitoli (79%). This suggests a distinct spatial divide between mainland and island regions, with peripheral areas occasionally exhibiting either very high or very low participation. Table 1 Voter Turnout by District in Central Sulawesi (2024) District/City Registered Voters Voter Turnout (%) Banggai 271.439 82,00 Banggai Kepulauan 90.851 84,00 Banggai Laut 52.275 85,00 Buol 109.198 85,00 Donggala 224.886 80,00 Morowali 125.843 84,00 Morowali Utara 106.964 81,00 Parigi Moutong 326.675 80,00 Poso 178.999 77,00 Sigi 191.019 83,00 Tojo Una-Una 119.804 85,00 Toli Toli 167.526 79,00 Kota Palu 271.124 76,00 Total 2.236.603 81,62 The geographic distribution of turnout suggests that accessibility and urban-rural dynamics are influential. In particular, Palu, as the provincial capital, recorded the lowest turnout, reflecting patterns observed in many democracies where urban electorates display greater apathy compared to rural voters. Conversely, island districts such as Banggai Laut recorded unexpectedly high participation, which may reflect the presence of strong local political mobilization despite logistical barriers. Table 2 Distribution of Voters of the Political Parties’ Elections In Central Sulawesi (2024) Districts PKB GERINDRA PDIP GOLKAR NASDEM DEMOKRAT Ʃ Voters of Political Party Banggai 15860 38055 29755 63798 23229 5729 176426 Poso 7109 18386 15550 20231 14165 28830 104271 Donggala 18355 20410 11113 17602 27126 15608 110214 Toli Toli 8266 12493 6589 12711 12502 8837 61398 Buol 10013 11239 5566 9116 12429 10422 58785 Morowali 6112 16380 5928 12148 20683 13900 75151 Parigi Moutong 21820 25662 31518 30142 33835 16811 159788 Banggai Kepulauan 8106 6463 7957 10791 8447 6342 48106 Sigi 9488 18536 15888 23828 13341 15126 96207 Tojo Una-Una 9633 7636 6608 18240 9670 5231 57018 Banggai Laut 1793 3706 3999 4757 4834 7076 26165 Morowali Utara 5310 7041 5370 21498 9979 8547 57745 Kota Palu 9102 21191 10348 17858 17876 13280 89655 Ʃ Valid Voters 130967 207198 156189 262720 208116 155739 1120929 % 7,6 12 9,1 15,3 12,1 9 65,1 4.2. Global Moran’s I Results for Party Support Global Moran’s I statistics were computed for the vote shares of major political parties. Results are summarized in Table 3 . Table 3 Moran’s I for Party Vote Shares (Central Sulawesi, 2024) Party Moran’s I z-value p-value Interpretation PKB 0.302 2.745 0.006 Positive & significant clustering Gerindra 0.114 1.233 0.187 Not significant (random) PDIP 0.610 5.643 0.001 Strong clustering Golkar 0.284 2.521 0.008 Positive clsustering NasDem 0.086 0.945 0.342 Random distribution Demokrat 0.354 3.118 0.004 Significant clustering These results indicate distinct spatial dynamics across parties. PDIP shows the strongest clustering (Moran’s I = 0.61), suggesting well-defined territorial strongholds. Demokrat (0.35), PKB (0.30), and Golkar (0.28) also exhibit significant clustering, though weaker than PDIP. Gerindra and Nasdem reveal no significant autocorrelation, implying more dispersed or random support across districts. This suggests that some parties rely on entrenched geographic bases, while others have yet to consolidate territorial strongholds in Central Sulawesi. 4.3. Spatial Visualization 4.4. LISA Cluster Analysis While Moran’s I provides a global overview, LISA analysis reveals local-level clustering pattern. Figure 1 –6 (see maps) illustrates the distribution of High-High (HH) clusters, Low-Low (LL) coldspots, and outliers for major parties. The LISA cluster maps reveal distinct spatial variations in party support. PKB and Golkar show mainly Low-Low clusters, reflecting spatially concentrated weak support. Gerindra displays a localized High-High cluster along the eastern coast (Banggai), while PDIP exhibits no significant clustering, indicating a diffuse distribution. Nasdem reveals a Low-High outlier in the central region (Sigi), suggesting uneven support. By contrast, Demokrat demonstrates the clearest pattern, with extensive High-High clusters in the west (Sigi) and north (Donggala) and Low-Low clusters in eastern districts. Overall, only Demokrat and Gerindra maintain consolidated strongholds, while the other parties display dispersed or spatially inconsistent support. Table 4 Spatial Autocorrelation Interpretation Based on LISA Cluster and LISA Significance Political Party LISA Cluster Pattern LISA Significance (p-value) Spatial Interpretation PKB Low-High clusters in Central–West and North (Sigi and Buol) Significant at 0.05 PKB votes display localized negative spatial autocorrelation (low surrounded by high), suggesting weak support in areas dominated by neighboring strongholds. Gerindra High-Low cluster in the Northeast (Banggai) Significant at 0.05 (local) Gerindra votes show a localized high-value surrounded by low neighbors, indicating isolated strongholds in an otherwise weaker support region. PDI-P No significant cluster Not significant PDI-P votes appear randomly distributed with no strong spatial dependence, suggesting a more even spread across regions. Golkar Low-High cluster in Central–East (Tojo Una-Una) Significant at 0.05 Golkar support shows negative spatial clustering, where weaker areas are located adjacent to stronger support regions. NasDem Low-High cluster in Central–West (Sigi) Significant at 0.05 NasDem has localized weak spots embedded within stronger neighboring regions, indicating partial clustering but overall dispersed support. Demokrat High-High clusters in the West & North (Sigi & Parigi Moutong); Low-Low clusters in Central & East (Banggai & Tojo Una-Una). Significant at 0.05 and 0.01 Demokrat demonstrates the most complex clustering: stronghold concentrations (high-high) in some areas and weak pockets (low-low) in others, reflecting spatial polarization of voter support. 5. Discussion 5.1. Spatial Inequalities in Voter Turnout The findings of this study reveal clear spatial disparities in voter participation across Central Sulawesi. Although the province achieved a relatively high overall turnout (81.62%), turnout varied substantially by district. Rural and peripheral regions such as Buol and Tojo Una-Una registered among the highest participation rates, while the provincial capital Palu recorded the lowest. This pattern contrast with common expectations in many advanced democracies(Elsässer & Schäfer, 2023), where rural areas often experience lower turnout due to geographic isolation(Gimpel & Reeves, 2022). In the Central Sulawesi context, the high turnout in rural and peripheral districts may reflect strenghts of local patronage networks and community mobilization, which remain influential in Indonesian politics. Political elites and party brokers in rural districts often rely on direct, personal networks to mobilize voters, thereby generating strong local participation. Conversely, the relatively lower turnout in urban Palu suggests electoral disengagement, fragmentation of party competition, or voter fatigue in contexts where multiple parties compete intensely without clear ideological distinctions. The spatial clustering of turnout further indicates that participation is not randomly distributed but instead shaped by geographic and socio-political structure. High turnout clusters are concentrated in the eastern coastal and northern mainland regions, while low turnout clusters are found in the provincial capital and parts of the central interior. These findings underscore the importance of geography in shaping electoral inclusion, with implications for electoral management bodies tasked with reducing participation inequalities. 5.2. Party Clustering and Territorial Strongholds The party autocorrelation analysis using Moran’s I demonstrates that several parties most, notably PDIP, Demokrat, PKB, and Golkar exhibit significant clustering in Central Sulawesi. PDIP’s strong clustering (Moran’s I = 0.61) highlights the existence of entranced territorial strongholds in Banggai Regency, particularly in subregions such as Toili and Luwuk. Similarly, Demokrat shows meaningful clustering around Banggai and Palu, while PKB is clustered in Donggala, Sigi, and Tojo Una-Una, reflecting its rural and peripheral support base. Golkar demonstrates localized clustering in Buol, indicating continued relevance of historical political networks in the northern districts. By contrast, Nasdem and Gerindra display random spatial distributions, with no significant clustering detected. This suggests either dispersed support across districts or an inability to consolidate geographic strongholds in Central Sulawesi. For Gerindra. This may reflect its role as a national opposition party with limited local infrastructure, while Nasdem’s fragmaented pattern could be attributed to its reliance on individual candidates rather than territorial bases. The LISA cluster map enriches this analysis by revealing local-level hotspots and coldspots. For example, PDIP’s High-High cluster in eastern Banggai is contrasted with Low-Low coldspots in Poso and Donggala. Demokrat’s dual clustering in Banggai and Palu illustrates both rural and urban support bases, while PKB’s rural clustering reflects its traditional grassroots networks. Importantly, Palu emerges as a consistent outlier, where no party holds a dominant territorial advantage, resulting in competitive fragmentation. 5.3. Urban Rural Electoral Dynamics The divergence between urban Palu and the surrounding rural districts underscores broader electoral dynamics in Indonesia. Urban voters tend to be more heterogeneous, issue-oriented and less tied to party loyalty, leading to fragmented support. In Palu, multiple parties compete vigorously, but none achieve overwhelming dominance, resulting in Low-High and High Low outlier patterns in the LISA maps. This suggests a highly contested urban political arena. In contrast, rural districts exhibit stronger clustering for specific parties, as seen with PKB in Donggala and PDIP in Banggai. Such clustering aligns with theories of territorial politics, which argue that rural areas often sustain stronger partisan identities shaped by community networks, cultural ties, and local patronage. In Central Sulawesi, this manifests in cohesive hotspots where party support is geographically concentrated and self-reinforcing. 5.4. Comparison with International Studies The patterns observed in Central Sulawesi resonate with findings from electoral geography in other contexts but also highlight distinctive features of Indonesia’s emerging democracy. In the United States, spatial clustering of partisan support (the “red rural vs blue urban” divide) has been extensively documented(Amlani & Algara, 2021). However, in Central Sulawesi, the opposite occurs: rural areas often demonstrate stronger and more cohesive participation than urban areas. In Europe, regional strongholds of parties have been linked to historical cleavages and socioeconomic division(Durrer De La Sota, 2022). Similarly, PDIP’s clustering in Banggai reflects the persistence of localized political identities. In Sub-Saharan Africa, turnout inequalities are frequently shaped by geographic accessibility(Zhou & Tandi, 2023). While Central Sulawesi’s island districts might be expected to suffer similar disadvantages, high turnout in Banggai Laut suggests that strong local mobilization can overcome logistical barriers. These comparisons underscore that while spatial clustering and turnout disparities are global phenomena, their specific configurations are shaped by unique historical, cultural, and institutional contexts. 5.5. Policy Implications The spatial disparities uncovered in this study have clear implications for both electoral management and party strategy. For the General Election Commission (KPU): Addressing turnout inequalities should be prioritized, particularly in low turnout urban centers such as Palu. Electoral education campaigns could be targeted to urban voters, who may be more disengaged or skeptical about party politics. Infrastructure and logistical support must continue in perpheral districts to maintain high levels of participation. For Political Parties: The existence of spatial clustering implies that parties should tailor strategies to their geographic bases. PDIP may consolidate its dominance in Banggai, while PKB can build on its rural strongholds. Parties with dispersed or random patterns (Gerindra, Nasdem) may need to invest in organizational infrastructure to establish durable territorial roots. Urban centers such as Palu require differentiated strategies, as competition is highly fragmented and issue-oriented. For Democratic Consolidation: Recognizing spatial inequalities ensures that all citizens, regardless of geography, can exercise equal political rights. Bridging urban apathy and rural clientelism remains a central challenge for the future of Indonesian democracy. 5.6. Theoretical Contributions Beyond its empirical findings, this study makes three theoretical contributions: It demonstrates the utility of spatial autocorrelation methods (Moran’s I, LISA) for analyzing elections in emerging democracies, moving beyond descriptive accounts toward quantitative rigor. It highlights the importance of territorial bases of party competition, showing that clustering and randomness coexist within the same provincial system. It contributes to comparative electoral geography by illustrating how Indonesia’s archipelagic geography and clientelistic politics produce distinctive spatial configurations of turnout and party support. 6. Conclusion This study has examined the spatial dynamics of voter participation and party support in the 2024 legislative election in Central Sulawesi Province, Indonesia, using spatial autocorrelation methods, Global Moran’s I and Local Indicators of Spatial Association (LISA). Drawing on official electoral data for 13 districts and cities, the analysis highlights significant spatial inequalities in turnout and distinctive clustering patterns among political parties. First, while to province achieved a high overall turnout (81.62%), participation varied substantially across districts. Rural and peripheral regions such as Buol and Tojo Una-Una recorded the highest turnout rates, while the provincial capital, Palu registered the lowest. This urban-rural divide underscores the influence of geography on electoral participation, with rural areas benefiting from strong community mobilization networks and urban centers reflecting greater voter disengagement. Second, the results reveal that several parties exhibit significant territorial clustering. PDIP displayed the strongest clustering in eastern Banggai, Demokrat combined rural and urban hotspots in Banggai and Palu, PKB concentrated in Donggala and Sigi, and Golkar in Buol. In contrast, Nasdem and Gerindra demonstrated more random distributions of support, indicating weaker territorial bases. LISA cluster maps further illuminated localized hotspots, confirming the coexistence of clustered and fragmented competition. Palu consistently emerged as a contested urban area, with no single party establishing dominance. Third, these findings contribute both theoretically and practically. Theoretically, they demonstrate the value of applying spatial statistics in electoral geography, particularly in emerging democracies where territorial cleavages are underexplored. This study shows how spatial autocorrelation methods can uncover patterns invisible to conventional analysis. Practically, the results hold implications for electoral management and party strategy. For the KPU, addressing turnout disparities, especially urban disengagement, remains a priority. For political parties, consolidating strongholds while adapting strategies to fragmented urban environments is critical for long-term competitiveness. In conclusion, electoral outcomes in Central Sulawesi are neither uniformly distributed nor entirely random; rather, they are shaped by a combination of geographic, socio-political, and institutional factors that generate both clustered strongholds and fragmented competition. Future research could build on this study by extending analysis to sub-district levels, incorporating demographics and socio-economic covariates, and comparing spatial electoral dynamics across multiple Indonesian provinces. Such work would further deepen our understanding of how geography shapes democracy in the world’s largest archipelagic state. Declarations Funding This research was supported by the Regular Fundamental Research 2024 from the Indonesian Ministry of Research and Technology (Kemdiktisaintek). The funding agency had no role in the design of the study, the collection, analysis, and interpretation of data, or in the writing of the manuscript. Author Contribution (Andi Hartati)-Conceptualization, Methodology, Data Curation, Formal Analysis, Writing – Original Draft.(Rahmad)- Data Collection, Validation, Writing – Review & Editing.(Siska)-Visualization, Interpretation of Results, Writing – Review & Editing. Acknowledgement AcknowledmentThe author gratefully acknowledge the support of the Central Sulawesi Provincial Election Commission (Komisi Pemilihan Umum, KPU) for providing access to official electoral data from the 2024 legislative election. We also thank the research assistants and local enumerators who contributed to the compilation and validation of district-level datasets. Constructive feedback from colleagues in the Department of Political Science and Social Science, (Universitas Tompotika Luwuk Banggai), greatly improved earlier versions of this manuscript. 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Introduction","content":"\u003cp\u003eElections remain the cornerstone of representative democracy(Papp et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), serving as the primary institutional mechanism through which citizens articulate preferences, shape governance outcomes, and hold political elites accountable(Otjes et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In Indonesia, the third largest democracy in the world(Aspinall, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), elections have acquired heightened importance since the post-1998 Reformasi era, which replaced centralized authoritarian rule with a more decentralized and competitive democratic system(Aspinall, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Legislative elections(Rifai \u0026amp; Haeril, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), in particular, are critical not only for determining the composition of the national and regional parliaments but also for revealing the geographic contours of political support across Indonesia\u0026rsquo;s diverse socio-political landscapes(Novrizal, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Understanding electoral behavior in Indonesia(Iftitah et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) is therefore essential to both academic scholarship on democratization(Testriono, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e) and the formulation of policies aimed at sustaining democratic consolidation(Gimpel et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe 2024 legislative election represented the latest large-scale electoral exercise in Indonesia, involving more than 200\u0026nbsp;million voters nationwide(Asmorojati \u0026amp; Suyadi, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Central Sulawesi Province, one of the country\u0026rsquo;s peripheral yet politically dynamic regions(Sumarto \u0026amp; McCarthy, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), 2.23\u0026nbsp;million registered voters participated in the election(KPU, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), yielding a turnout rate of 81.62%. While aggregate statistics provide an overall picture of voter engagement, they mask significant sub-provincial variations in participation and party support. Districts such as Parigi Moutong and Buol recorded relatively high turnout, while urban centers\u0026rsquo; territorial patterns: some concentrated in localized strongholds, while others displayed dispersed or fragmented support. These variations raise important questions about the spatial dynamics of electoral behavior in Indonesia(Fraenkel \u0026amp; Aspinall, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eElectoral geography(Johnston \u0026amp; Pattie, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) offers a valuable lens to analyze such dynamics(McDonald, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), as it emphasizes the spatial distribution of votes, turnout, and political alignments across geographic units(Johnston \u0026amp; Pattie, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). A central concern of electoral geography(Warf, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) is whether electoral outcomes are spatially clustered, randomly distributed, or dispersed, a question that has direct implications for party strategy, representation, and democratic equity(Agnew, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). While global patterns of electoral geography have been studied extensively in advanced democracies)(van Noord et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), applications to emerging democracies in Southeast Asia remain comparatively underdeveloped(Testriono, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). In particular, studies of Indonesia\u0026rsquo;s elections have tended to focus on clientelism(Okthariza, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), money politics, and party system fragmentation(Fink-Hafner \u0026amp; Novak, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), but have seldom employed rigorous spatial(Garc\u0026iacute;a-D\u0026iacute;az et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) statistical techniques to assess clustering and spatial dependencies in electoral outcomes(Szmolka, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSpatial statistics provide powerful tools for extending electoral geography beyond descriptive mapping(Grekousis, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moran\u0026rsquo;s I(R. Bivand et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and Local Indicators of Spatial Association (LISA)(Anselin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), are widely used to detect global and local spatial autocorrelation. LISA(Scharf, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) enables the identification of whether electoral results in neighboring districts exhibit clustering (high-high or low-low) or dispersion. (high-low), or randomness. LISA enables the identification of localized clusters or outliers that might otherwise be obscured in province-wide analyses(Rogerson \u0026amp; Kedron, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Together, these tools permit a systematic exploration of the spatial structure of voter turnout and party support(Yandri, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Their application to Indonesian elections can thus contribute to both theoretical advancements in spatial political analysis(R. S. Bivand \u0026amp; Wong, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and practical insights for electoral management(Rezaee et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCentral Sulawesi provides a compelling case study for applying such methods. The province is characterized by geographic diversity, spanning mainland districts, coastal areas, and remote island regencies(Astari et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Its political history has been shaped by both national-level party competition and local socio-cultural cleavages(Caramani, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The 2024 election further highlighted the unevenness of political participation and support bases, with major parties such as PDIP, PKB, Demokrat, and Golkar showing clustered strongholds, while others such as Nasdem and Gerindra displayed more fragmented patterns. These dynamics underscore the relevance of spatial approaches to electoral analysis(Lin, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in the Indonesian context. This study addresses the research gap by applying Moran\u0026rsquo;s I and LISA to analyze voter turnout(Jubit et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and party support(Karp, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) in Central Sulawesi\u0026rsquo;s 2024 legislative election. Specifically, the research pursues three objectives:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo measure the spatial distribution of voter turnout across 13 districts and cities in Central Sulawesi, and to identify patterns of inequality in electoral participation.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo assess the degree of spatial autocorrelation in party support using Moran\u0026rsquo;s I, thereby evaluating whether major political parties exhibit clustered, dispersed, or random territorial bases.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTo detect localized hotspots and coldspots of party support using LISA, thus uncovering spatial nuances that can inform both electoral theory and political strategy.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eBy integrating electoral geography with spatial statistics, this study makes two main contributions. First, it enriches scholarly understanding of Indonesia\u0026rsquo;s electoral dynamics(Busroh \u0026amp; Khairo, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) by moving beyond descriptive accounts toward quantitatively rigorous spatial analysis. Second, it offers policy-relevant insights: identifying spatial inequalities, while mapping party strongholds, can inform political actors\u0026rsquo; approaches to coalition building and campaigning. The findings thus hold implications not only for the academic literature on spatial politics but also for the practice of democratic governance in Indonesia and comparable emerging democracies(Testriono, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Electoral Geography and Spatial Dimensions of Voting\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eElectoral geography(Forest, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), a subfield of political geography(Shin, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), investigates the spatial patterns of voting behavior, political party support, and representation(Li \u0026amp; Fotheringham, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). It seeks to understand how geographic space influences, and is influenced by, electoral processes(Johnston, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Foundational works in this tradition emphasize the spatial clustering of political legacies in shaping electoral outcomes(Flint, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Electoral geography is not confined to mapping results(Wiedemann, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); it interrogates the structural relationships between territory, identity, and political mobilization, thus bridging geography with political science(O\u0026rsquo;Grady \u0026amp; Wiedemann, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAt the global level, electoral geography has been instrumental in explaining phenomena such as partisan polarization in the United States(Chen \u0026amp; Rodden, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), regional cleavages in Europe(Caramani, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and ethnic or religious voting in Africa and Asia(Aidoo \u0026amp; Botchway, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In each of these cases, spatial patterns are neither accidental nor random; rather, they reveal underlying socio-political structure and inequalities(Roberts, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A central concern is whether electoral outcomes exhibit territorial clustering(Wiedemann, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), suggesting the existence of strongholds and localized political cultures(Reilly, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), or whether they are fragmented(Huang et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), signaling fluidity in voter preferences(Chan et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn the Indonesian context, electoral geography is particularly salient given the country\u0026rsquo;s archipelagic geography and ethno-religious diversity(Warganegara \u0026amp; Waley, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, most studies have analyzed elections from the perspectives of patronage politics(Istania, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), clientelism, and money(Berenschot \u0026amp; Aspinall, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While these approaches shed light on the socio-political mechanisms of vote mobilization(Ayta\u0026ccedil; et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). They have paid less attention to the spatial distribution of votes and turnout(Boyle, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Consequently, the territorial logics of Indonesia\u0026rsquo;s elections remain underexplored.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Spatial Autocorrelation in Electoral Studies\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSpatial autocorrelation is the statistical measure of the degree to which a spatial phenomenon in one location is similar to that in neighboring locations(Yang et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Positive autocorrelation indicates that areas with dissimilar values(Pregi \u0026amp; Novotn\u0026yacute;, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) (high adjacent to low) are spatially proximate(Z. Zhang et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The absence of spatial autocorrelation helps identify whether political support or voter turnout is geographically concentrated or disperseds(Griffith, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGlobal Moran\u0026rsquo;s I is one of the most widely used indices for measuring overall spatial autocorrelation across a region(Jaber et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A positive and significant Moran\u0026rsquo;s I indicates clustering, while a negative and considerable value implies dispersion. However, global measures often conceal localized variations(Westerholt, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To address this, Anselin (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1995\u003c/span\u003e)(Yu et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) introduced Local Indicator of Spatial Association (LISA), which identifies spatial clusters (High-High, Low-Low) and outliers (High-Low, Low-High) at finer scales. These techniques have transformed electoral geography from descriptive cartography into a statistically robust field.\u003c/p\u003e\u003cp\u003eApplications of Moran\u0026rsquo;s I and LISA in political analysis are well documented(C. Zhang et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, Griffith and Jones (1980)(Griffith, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) used spatial autocorrelation to detect partisan clustering in U.S. counties, while (Johnston et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)applied similar methods to study voting patterns in the United Kingdom. More recent studies employ these tools to investigate gerrymandering(Trelles et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), regional polarization(Chen \u0026amp; Rodden, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and turnout inequalities(Tam Cho et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These works highlight the capacity of spatial statistics to uncover hidden structures in electoral data that would otherwise remain invisible in non-spatial analysis.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Spatial Inequalities in Voter Participation\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eTurnout disparities represent another critical dimension of electoral geography(Barber \u0026amp; Holbein, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Research has shown that geographic variations in turnout are shaped by accessibility to polling stations, socioeconomic inequalities, and institutional arrangements(Gimpel \u0026amp; Reeves, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Spatial autocorrelation methods allow researchers to detect whether low-turnout areas are geographically clustered(Maškarinec, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), thereby signaling systematic exclusions, or whether turnout is randomly distributed.\u003c/p\u003e\u003cp\u003eIn emerging democracies(Rafique et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), turnout inequalities often reflect deeper structural constraints such as poor infrastructure(Mart\u0026iacute;n-Legendre \u0026amp; Rungo, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), limited voter education, or geographic isolation(Simon et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For instance, studies in Sub-Saharan Africa and South Asia reveal that rural and peripheral areas frequently register lower participation, Bratton (2013) (Datta, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Indonesia, anecdotal evidence points to similar patterns(Fawzia et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), especially in remote island regions where logistical barriers impede electoral access. Yet, systematic spatial analyses of turnout disparities remain scarce.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Electoral Geography in Indonesia\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eIndonesia\u0026rsquo;s elections have been widely studied from a political science and area studies perspective(Handoko et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Much of the literature emphasizes the role of local elites(Noak, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), patronage networks(Apriliyanti, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and money politics in shaping outcomes(Dettman, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Studies also highlight the fragmented nature of the party system and the challenges of consolidating democratic accountability (Berenschot \u0026amp; Aspinall, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, relatively few studies apply quantitative spatial approaches. Exceptions include(Holt, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), who examined urban-rural cleavages in Jakarta\u0026rsquo;s gubernatorial election(Suhardi, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who explored spatial clustering in ethnic voting in Papua(Sasmita, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These contributions remain limited compared to the extensive application of Moran\u0026rsquo;s I and LISA in Western contexts. Given Indonesia\u0026rsquo;s geographic vastness and regional diversity, neglecting spatial statistical methods risks overlooking the key territorial dynamics of its electoral processes(Solehudin, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Research Gap and Contribution\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eFrom the preceding discussion, two research gaps emerge. First, while electoral geography has been applied extensively in established democracies, its application in Indonesia remains embryonic, with little systematic use of spatial statistics. Second, existing Indonesian election studies largely privilege socio-political explanations over spatial ones, thereby underestimating the geographic structuring of voter behavior.\u003c/p\u003e\u003cp\u003eThis study addresses this gap by employing Moran\u0026rsquo;s I and LISA to analyze voter turnout and party support in Central Sulawesi during the 2024 legislative election. By combining descriptive electoral data with spatial statistical analysis, the research provides fresh insight into the geography of electoral participation and party clustering in Indonesia. Beyond contributing to the empirical understanding of one province, the study demonstrates the utility of spatial statistics for electoral analysis in emerging democracies more broadly.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Study Area\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eCentral Sulawesi Province, located in eastern Indonesia, was selected as the case study for this research. The province comprises 23 districts and cities, with a total registered electorate (Daftar Pemilih Tetap, DPT) of 2,236,603 in the 2024 legislative election. Its geography is highly diverse, encompassing mainland territories, coastal areas, and peripheral islands such as Banggai Kepulauan dan Banggai Laut. This geographic heterogeneity makes Central Sulawesi an ideal setting for investigating spatial disparities in electoral participation and party support. The province\u0026rsquo;s political context is similarly complex, with national parties competing for influence in both urban centers such as Palu and more remote, rural regions.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Data Sources\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe primary dataset was obtained from the General Election Commission (Komisi Pemilihan Umum, KPU) of Central Sulawesi which provides official records of registered voters. Voter turnout and valid votes for each party contesting the 2024 legislative election. The dataset covers all 13 administrative districts and cities in the province.\u003c/p\u003e\u003cp\u003eVariables included in the analysis were:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eVoter turnout: measured as the percentage of registered voters casting valid ballots in each district.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eParty vote shares: measured as the proportion of valid votes received by each major party (e.g., PDIP, Golkar, PKB, Gerindra, Nasdem, Demokrat) at the district level.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Analytical Framework\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe analytical strategy combines descriptive electoral statistics with spatial statistical techniques. The approach is designed to answer three research questions:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhat is the spatial distribution of voter turnout across districts in Central Sulawesi?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDo political parties exhibit significant spatial clustering in their electoral support?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eWhere are the localized hotspots and coldspots of party support?\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Global Moran\u0026rsquo;s I\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eMoran\u0026rsquo;s I is a global measure of how spatial units are correlated (Cliff \u0026amp; Ord, 1981). It is computed as:\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{I}=\\frac{\\varvec{n}}{{\\varvec{\\Sigma\\:}}_{\\varvec{i}}{\\varvec{\\Sigma\\:}}_{\\varvec{j}}{\\varvec{W}}_{\\varvec{j}}}\\:\\text{x}\\:\\frac{{\\varvec{\\Sigma\\:}}_{\\varvec{i}}{\\varvec{\\Sigma\\:}}_{\\varvec{j}}{\\varvec{W}}_{\\varvec{i}\\varvec{j}}\\left({\\varvec{X}}_{\\varvec{i}}-\\varvec{X}\\right)\\left({\\varvec{X}}_{\\varvec{j}}-\\varvec{X}\\right)}{{\\varvec{\\Sigma\\:}}_{\\varvec{i}}{\\left({\\varvec{X}}_{\\varvec{i}}-\\stackrel{-}{\\varvec{X}}\\right)}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003en\u003c/em\u003e is the number of spatial units,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{w}_{ij}\\)\u003c/span\u003e\u003c/span\u003e represents the spatial weight between locations \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the value of the variable at location \u003cem\u003ei\u003c/em\u003e,\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{x}\\)\u003c/span\u003e\u003c/span\u003e is the mean of the variable, and\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:W\\)\u003c/span\u003e\u003c/span\u003e is the sum of all spatial weights.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eValues of Moran\u0026rsquo;s I were used to evaluate whether party vote shares within the district were spatially clustered, dispersed, or random across Central Sulawesi.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Local indicators of Spatial Association (LISA)\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhile Moran\u0026rsquo;s I provides a global summary, it may obscure local heterogeneities. To address this limitation, Local Indicators of Spatial Association (LISA) were employed (Anselin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Local Indicators of Spatial Association (LISA) measure local spatial relationships to identify whether clustering occurs in aspecific location. In other words, LISA helps to determine if an area is part of a statistically significant cluster of similar values (High-High or Low-Low) or an outlier (High-Low or Low-High). It is computed as:\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{I}}_{\\varvec{i}}=\\:\\frac{\\left({\\varvec{X}}_{\\varvec{i}}-\\:\\stackrel{-}{\\varvec{X}}\\right)}{{\\varvec{S}}^{2}}\\sum\\:_{\\varvec{j}}{\\varvec{W}}_{\\varvec{i}\\varvec{j}}\\left({\\varvec{X}}_{\\varvec{j}}-\\:\\stackrel{-}{\\varvec{X}}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e = the value of the variable of interest in region i\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{X}\\)\u003c/span\u003e\u003c/span\u003e = the mean of the variable across all regions\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:S\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e = the variance of the variable\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:W\\)\u003c/span\u003e\u003c/span\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e \u003cem\u003e=\u003c/em\u003e the spatial weight between region \u003cem\u003ei\u003c/em\u003e and region \u003cem\u003ej\u003c/em\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eLISA identifies clusters and outliers by comparing each unit\u0026rsquo;s value with those neighbors. The results classify spatial patterns into four categories:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eHigh-High (HH): district with high values surrounded by high values neighbors (hotspots).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLow-Low (LL): districts with low values surrounded by low-value neighbors (coldspots).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eHigh-Low (HL): district with high values surrounded by low value neighbors (outliers).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eLow-High (LH): district with low values surrounded by high value neighbors (outliers).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eLISA thus provides fine-grained insights into localized patterns of party support and turnout.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Spatial Weight Matrix\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eBoth Moran\u0026rsquo;s I and LISA require the specification of a spatial weight matrix (\u003cem\u003eW\u003c/em\u003e) that defines the neighborhood structure. For this study, a contiguity-based Queen\u0026rsquo;s case spatial weight was adopted, where districts are considered neighbors if they share a common border or vertex. This approach is suitable given the polygonal administrative boundaries of Central Sulawesi. Row standardization of the weight matrix was applied to ensure comparability across districts with varying numbers of neighbors.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Software Implementation\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAll spatial statistical analyses were conducted using GeoDa 1.20, an open-source software developed for spatial econometrics and visualization. Descriptive statistics and data cleaning were carried out in Microsoft Excel, while ArcGIS/QGIS was used to generate cartographic representations of turnout distributions, Moran\u0026rsquo;s I scatterplots, and LISA cluster maps.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.8. Limitations\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eSeveral methodological limitations must be noted. First, the use of district-level aggregation may mask intra-district heterogeneities, as electoral dynamics often vary at finer scale (e.g.,sub-district or village). Second, the reliance on official electoral data assumes accuracy and completeness, though discrepancies in voter lists and reporting cannot be fully ruled out. Finally, while Moran\u0026rsquo;s I and LISA identify spatial clustering, they do not explain the underlying causal mechanisms, which may include demographic, socio-economic, or infrastructural factors beyond the scope of this analysis.\u003c/p\u003e\u003cp\u003eDespite these limitations, the chosen methods provide robust tools for uncovering spatial structures in electoral participation and party support. Their application in the Indonesian context represents a methodological innovation and a valuable contribution to the study of electoral geography in emerging democracies.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Voter Turnout in Central Sulawesi\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe 2024 legislative election in Central Sulawesi recorded a provincial turnout of 81.62%, an increase from the 79.6% reported in the 2019 election. While this reflects generally high levels of electoral participation, turnout was not evenly distributed across districts. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents voter turnout by district. Participation was highest in Buol (85%), Tojo Una-Una (85%), and Banggai Laut (85%), whereas relatively lower rates were observed in Palu (76.%), Poso (77%), and Tolitoli (79%). This suggests a distinct spatial divide between mainland and island regions, with peripheral areas occasionally exhibiting either very high or very low participation.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eVoter Turnout by District in Central Sulawesi (2024)\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistrict/City\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRegistered Voters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVoter Turnout (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBanggai\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e271.439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e82,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBanggai Kepulauan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90.851\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBanggai Laut\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBuol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e109.198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDonggala\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e224.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMorowali\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e125.843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e84,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMorowali Utara\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e106.964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParigi Moutong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e326.675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoso\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e178.999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSigi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e191.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e83,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTojo Una-Una\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e119.804\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eToli Toli\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e167.526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKota Palu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e271.124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76,00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.236.603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81,62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe geographic distribution of turnout suggests that accessibility and urban-rural dynamics are influential. In particular, Palu, as the provincial capital, recorded the lowest turnout, reflecting patterns observed in many democracies where urban electorates display greater apathy compared to rural voters. Conversely, island districts such as Banggai Laut recorded unexpectedly high participation, which may reflect the presence of strong local political mobilization despite logistical barriers.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of Voters of the Political Parties\u0026rsquo; Elections In Central Sulawesi (2024)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistricts\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePKB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGERINDRA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePDIP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGOLKAR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNASDEM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDEMOKRAT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eƩ Voters of Political Party\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBanggai\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15860\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e63798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23229\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e176426\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePoso\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18386\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e28830\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e104271\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDonggala\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e27126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e110214\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eToli Toli\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8266\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12711\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12502\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8837\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e61398\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBuol\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e58785\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMorowali\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5928\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e75151\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParigi Moutong\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e31518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33835\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e159788\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBanggai Kepulauan\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8447\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e48106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSigi\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9488\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18536\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23828\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e96207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTojo Una-Una\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5231\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e57018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBanggai Laut\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4757\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4834\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e7076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e26165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMorowali Utara\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5370\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e57745\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKota Palu\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10348\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e13280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e89655\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eƩ Valid Voters\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e130967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e207198\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e156189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e262720\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e208116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e155739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1120929\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e%\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15,3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12,1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e65,1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Global Moran\u0026rsquo;s I Results for Party Support\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eGlobal Moran\u0026rsquo;s I statistics were computed for the vote shares of major political parties.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eResults are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMoran\u0026rsquo;s I for Party Vote Shares (Central Sulawesi, 2024)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParty\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMoran\u0026rsquo;s I\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ez-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePKB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.745\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePositive \u0026amp; significant clustering\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGerindra\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.233\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNot significant (random)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePDIP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.643\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStrong clustering\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGolkar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePositive clsustering\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNasDem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRandom distribution\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemokrat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSignificant clustering\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThese results indicate distinct spatial dynamics across parties.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003ePDIP shows the strongest clustering (Moran\u0026rsquo;s I\u0026thinsp;=\u0026thinsp;0.61), suggesting well-defined territorial strongholds.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDemokrat (0.35), PKB (0.30), and Golkar (0.28) also exhibit significant clustering, though weaker than PDIP.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eGerindra and Nasdem reveal no significant autocorrelation, implying more dispersed or random support across districts.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis suggests that some parties rely on entrenched geographic bases, while others have yet to consolidate territorial strongholds in Central Sulawesi.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Spatial Visualization\u003c/h2\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e4.4. LISA Cluster Analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWhile Moran\u0026rsquo;s I provides a global overview, LISA analysis reveals local-level clustering pattern. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;6 (see maps) illustrates the distribution of High-High (HH) clusters, Low-Low (LL) coldspots, and outliers for major parties.\u003c/p\u003e\u003cp\u003eThe LISA cluster maps reveal distinct spatial variations in party support. PKB and Golkar show mainly Low-Low clusters, reflecting spatially concentrated weak support. Gerindra displays a localized High-High cluster along the eastern coast (Banggai), while PDIP exhibits no significant clustering, indicating a diffuse distribution. Nasdem reveals a Low-High outlier in the central region (Sigi), suggesting uneven support. By contrast, Demokrat demonstrates the clearest pattern, with extensive High-High clusters in the west (Sigi) and north (Donggala) and Low-Low clusters in eastern districts. Overall, only Demokrat and Gerindra maintain consolidated strongholds, while the other parties display dispersed or spatially inconsistent support.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpatial Autocorrelation Interpretation Based on LISA Cluster and LISA Significance\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePolitical Party\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLISA Cluster Pattern\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLISA Significance (p-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSpatial Interpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePKB\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow-High clusters in Central\u0026ndash;West and North (Sigi and Buol)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSignificant at 0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePKB votes display localized negative spatial autocorrelation (low surrounded by high), suggesting weak support in areas dominated by neighboring strongholds.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGerindra\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh-Low cluster in the Northeast (Banggai)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSignificant at 0.05 (local)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGerindra votes show a localized high-value surrounded by low neighbors, indicating isolated strongholds in an otherwise weaker support region.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePDI-P\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo significant cluster\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot significant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePDI-P votes appear randomly distributed with no strong spatial dependence, suggesting a more even spread across regions.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGolkar\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow-High cluster in Central\u0026ndash;East (Tojo Una-Una)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSignificant at 0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGolkar support shows negative spatial clustering, where weaker areas are located adjacent to stronger support regions.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNasDem\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow-High cluster in Central\u0026ndash;West (Sigi)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSignificant at 0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNasDem has localized weak spots embedded within stronger neighboring regions, indicating partial clustering but overall dispersed support.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDemokrat\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh-High clusters in the West \u0026amp; North (Sigi \u0026amp; Parigi Moutong); Low-Low clusters in Central \u0026amp; East (Banggai \u0026amp; Tojo Una-Una).\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSignificant at 0.05 and 0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDemokrat demonstrates the most complex clustering: stronghold concentrations (high-high) in some areas and weak pockets (low-low) in others, reflecting spatial polarization of voter support.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cdiv id=\"Sec23\"\u003e\n \u003ch2\u003e5.1. Spatial Inequalities in Voter Turnout\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe findings of this study reveal clear spatial disparities in voter participation across Central Sulawesi. Although the province achieved a relatively high overall turnout (81.62%), turnout varied substantially by district. Rural and peripheral regions such as Buol and Tojo Una-Una registered among the highest participation rates, while the provincial capital Palu recorded the lowest. This pattern contrast with common expectations in many advanced democracies(Els\u0026auml;sser \u0026amp; Sch\u0026auml;fer, 2023), where rural areas often experience lower turnout due to geographic isolation(Gimpel \u0026amp; Reeves, 2022).\u003c/p\u003e\n \u003cp\u003eIn the Central Sulawesi context, the high turnout in rural and peripheral districts may reflect strenghts of local patronage networks and community mobilization, which remain influential in Indonesian politics. Political elites and party brokers in rural districts often rely on direct, personal networks to mobilize voters, thereby generating strong local participation. Conversely, the relatively lower turnout in urban Palu suggests electoral disengagement, fragmentation of party competition, or voter fatigue in contexts where multiple parties compete intensely without clear ideological distinctions.\u003c/p\u003e\n \u003cp\u003eThe spatial clustering of turnout further indicates that participation is not randomly distributed but instead shaped by geographic and socio-political structure. High turnout clusters are concentrated in the eastern coastal and northern mainland regions, while low turnout clusters are found in the provincial capital and parts of the central interior. These findings underscore the importance of geography in shaping electoral inclusion, with implications for electoral management bodies tasked with reducing participation inequalities.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec24\"\u003e\n \u003ch2\u003e5.2. Party Clustering and Territorial Strongholds\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe party autocorrelation analysis using Moran\u0026rsquo;s I demonstrates that several parties most, notably PDIP, Demokrat, PKB, and Golkar exhibit significant clustering in Central Sulawesi. PDIP\u0026rsquo;s strong clustering (Moran\u0026rsquo;s I\u0026thinsp;=\u0026thinsp;0.61) highlights the existence of entranced territorial strongholds in Banggai Regency, particularly in subregions such as Toili and Luwuk. Similarly, Demokrat shows meaningful clustering around Banggai and Palu, while PKB is clustered in Donggala, Sigi, and Tojo Una-Una, reflecting its rural and peripheral support base. Golkar demonstrates localized clustering in Buol, indicating continued relevance of historical political networks in the northern districts.\u003c/p\u003e\n \u003cp\u003eBy contrast, Nasdem and Gerindra display random spatial distributions, with no significant clustering detected. This suggests either dispersed support across districts or an inability to consolidate geographic strongholds in Central Sulawesi. For Gerindra. This may reflect its role as a national opposition party with limited local infrastructure, while Nasdem\u0026rsquo;s fragmaented pattern could be attributed to its reliance on individual candidates rather than territorial bases.\u003c/p\u003e\n \u003cp\u003eThe LISA cluster map enriches this analysis by revealing local-level hotspots and coldspots. For example, PDIP\u0026rsquo;s High-High cluster in eastern Banggai is contrasted with Low-Low coldspots in Poso and Donggala. Demokrat\u0026rsquo;s dual clustering in Banggai and Palu illustrates both rural and urban support bases, while PKB\u0026rsquo;s rural clustering reflects its traditional grassroots networks. Importantly, Palu emerges as a consistent outlier, where no party holds a dominant territorial advantage, resulting in competitive fragmentation.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec25\"\u003e\n \u003ch2\u003e5.3. Urban Rural Electoral Dynamics\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe divergence between urban Palu and the surrounding rural districts underscores broader electoral dynamics in Indonesia. Urban voters tend to be more heterogeneous, issue-oriented and less tied to party loyalty, leading to fragmented support. In Palu, multiple parties compete vigorously, but none achieve overwhelming dominance, resulting in Low-High and High Low outlier patterns in the LISA maps. This suggests a highly contested urban political arena.\u003c/p\u003e\n \u003cp\u003eIn contrast, rural districts exhibit stronger clustering for specific parties, as seen with PKB in Donggala and PDIP in Banggai. Such clustering aligns with theories of territorial politics, which argue that rural areas often sustain stronger partisan identities shaped by community networks, cultural ties, and local patronage. In Central Sulawesi, this manifests in cohesive hotspots where party support is geographically concentrated and self-reinforcing.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec26\"\u003e\n \u003ch2\u003e5.4. Comparison with International Studies\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe patterns observed in Central Sulawesi resonate with findings from electoral geography in other contexts but also highlight distinctive features of Indonesia\u0026rsquo;s emerging democracy.\u003c/p\u003e\n \u003c/div\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eIn the United States, spatial clustering of partisan support (the \u0026ldquo;red rural vs blue urban\u0026rdquo; divide) has been extensively documented(Amlani \u0026amp; Algara, 2021). However, in Central Sulawesi, the opposite occurs: rural areas often demonstrate stronger and more cohesive participation than urban areas.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIn Europe, regional strongholds of parties have been linked to historical cleavages and socioeconomic division(Durrer De La Sota, 2022). Similarly, PDIP\u0026rsquo;s clustering in Banggai reflects the persistence of localized political identities.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIn Sub-Saharan Africa, turnout inequalities are frequently shaped by geographic accessibility(Zhou \u0026amp; Tandi, 2023). While Central Sulawesi\u0026rsquo;s island districts might be expected to suffer similar disadvantages, high turnout in Banggai Laut suggests that strong local mobilization can overcome logistical barriers.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003cdiv\u003e\n \u003cp\u003eThese comparisons underscore that while spatial clustering and turnout disparities are global phenomena, their specific configurations are shaped by unique historical, cultural, and institutional contexts.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec27\"\u003e\n \u003ch2\u003e5.5. Policy Implications\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eThe spatial disparities uncovered in this study have clear implications for both electoral management and party strategy.\u003c/p\u003e\n \u003c/div\u003e\n \u003col\u003e\n \u003cli\u003e\n \u003cp\u003eFor the General Election Commission (KPU):\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eAddressing turnout inequalities should be prioritized, particularly in low turnout urban centers such as Palu.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eElectoral education campaigns could be targeted to urban voters, who may be more disengaged or skeptical about party politics.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eInfrastructure and logistical support must continue in perpheral districts to maintain high levels of participation.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003col start=\"2\"\u003e\n \u003cli\u003e\n \u003cp\u003eFor Political Parties:\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eThe existence of spatial clustering implies that parties should tailor strategies to their geographic bases. PDIP may consolidate its dominance in Banggai, while PKB can build on its rural strongholds.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eParties with dispersed or random patterns (Gerindra, Nasdem) may need to invest in organizational infrastructure to establish durable territorial roots.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eUrban centers such as Palu require differentiated strategies, as competition is highly fragmented and issue-oriented.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n \u003col start=\"3\"\u003e\n \u003cli\u003e\n \u003cp\u003eFor Democratic Consolidation:\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n \u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eRecognizing spatial inequalities ensures that all citizens, regardless of geography, can exercise equal political rights.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eBridging urban apathy and rural clientelism remains a central challenge for the future of Indonesian democracy.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ul\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec28\"\u003e\n \u003ch2\u003e5.6. Theoretical Contributions\u003c/h2\u003e\n \u003cdiv\u003e\n \u003cp\u003eBeyond its empirical findings, this study makes three theoretical contributions:\u003c/p\u003e\n \u003c/div\u003e\n \u003col\u003e\n \u003cli\u003e\n \u003cp\u003eIt demonstrates the utility of spatial autocorrelation methods (Moran\u0026rsquo;s I, LISA) for analyzing elections in emerging democracies, moving beyond descriptive accounts toward quantitative rigor.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIt highlights the importance of territorial bases of party competition, showing that clustering and randomness coexist within the same provincial system.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIt contributes to comparative electoral geography by illustrating how Indonesia\u0026rsquo;s archipelagic geography and clientelistic politics produce distinctive spatial configurations of turnout and party support.\u003c/p\u003e\n \u003c/li\u003e\n \u003c/ol\u003e\n\u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study has examined the spatial dynamics of voter participation and party support in the 2024 legislative election in Central Sulawesi Province, Indonesia, using spatial autocorrelation methods, Global Moran\u0026rsquo;s I and Local Indicators of Spatial Association (LISA). Drawing on official electoral data for 13 districts and cities, the analysis highlights significant spatial inequalities in turnout and distinctive clustering patterns among political parties.\u003c/p\u003e\u003cp\u003eFirst, while to province achieved a high overall turnout (81.62%), participation varied substantially across districts. Rural and peripheral regions such as Buol and Tojo Una-Una recorded the highest turnout rates, while the provincial capital, Palu registered the lowest. This urban-rural divide underscores the influence of geography on electoral participation, with rural areas benefiting from strong community mobilization networks and urban centers reflecting greater voter disengagement.\u003c/p\u003e\u003cp\u003eSecond, the results reveal that several parties exhibit significant territorial clustering. PDIP displayed the strongest clustering in eastern Banggai, Demokrat combined rural and urban hotspots in Banggai and Palu, PKB concentrated in Donggala and Sigi, and Golkar in Buol. In contrast, Nasdem and Gerindra demonstrated more random distributions of support, indicating weaker territorial bases. LISA cluster maps further illuminated localized hotspots, confirming the coexistence of clustered and fragmented competition. Palu consistently emerged as a contested urban area, with no single party establishing dominance.\u003c/p\u003e\u003cp\u003eThird, these findings contribute both theoretically and practically. Theoretically, they demonstrate the value of applying spatial statistics in electoral geography, particularly in emerging democracies where territorial cleavages are underexplored. This study shows how spatial autocorrelation methods can uncover patterns invisible to conventional analysis. Practically, the results hold implications for electoral management and party strategy. For the KPU, addressing turnout disparities, especially urban disengagement, remains a priority. For political parties, consolidating strongholds while adapting strategies to fragmented urban environments is critical for long-term competitiveness.\u003c/p\u003e\u003cp\u003eIn conclusion, electoral outcomes in Central Sulawesi are neither uniformly distributed nor entirely random; rather, they are shaped by a combination of geographic, socio-political, and institutional factors that generate both clustered strongholds and fragmented competition. Future research could build on this study by extending analysis to sub-district levels, incorporating demographics and socio-economic covariates, and comparing spatial electoral dynamics across multiple Indonesian provinces. Such work would further deepen our understanding of how geography shapes democracy in the world\u0026rsquo;s largest archipelagic state.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research was supported by the Regular Fundamental Research 2024 from the Indonesian Ministry of Research and Technology (Kemdiktisaintek). The funding agency had no role in the design of the study, the collection, analysis, and interpretation of data, or in the writing of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e(Andi Hartati)-Conceptualization, Methodology, Data Curation, Formal Analysis, Writing \u0026ndash; Original Draft.(Rahmad)- Data Collection, Validation, Writing \u0026ndash; Review \u0026amp; Editing.(Siska)-Visualization, Interpretation of Results, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eAcknowledmentThe author gratefully acknowledge the support of the Central Sulawesi Provincial Election Commission (Komisi Pemilihan Umum, KPU) for providing access to official electoral data from the 2024 legislative election. We also thank the research assistants and local enumerators who contributed to the compilation and validation of district-level datasets. Constructive feedback from colleagues in the Department of Political Science and Social Science, (Universitas Tompotika Luwuk Banggai), greatly improved earlier versions of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgnew, J. (1996). Mapping politics: How context counts in electoral geography. \u003cem\u003ePolitical Geography\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(2), 129\u0026ndash;146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0962-6298(95)00076-3\u003c/span\u003e\u003cspan address=\"10.1016/0962-6298(95)00076-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAidoo, G. A., \u0026amp; Botchway, T. P. (2021). 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In \u003cem\u003eSub-Saharan Political Cultures of Deceit in Language, Literature, and the Media, II: Across National Contexts\u003c/em\u003e. \u003cem\u003eSpringer Nature\u003c/em\u003e, 383\u0026ndash;404. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-031-42883-8_20\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-42883-8_20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Spatial autocorrelation, Moran’s I, LISA, Electoral geography, Voter participation","lastPublishedDoi":"10.21203/rs.3.rs-7730694/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7730694/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis Study investigates the spatial dynamics of voter participation and party support in the 2024 legislative election in Central Sulawesi Province, Indonesia. Drawing on official electoral data from the General Election Commission (KPU), we analyze 2,236,603 registered voters across 13 districts and cities, with an overall turnout rate of 81.62%. Employing spatial statistical methods, Global Moran\u0026rsquo;s I and Local Indicator of Spatial Association (LISA), implemented through Geoda, the study explores whether electoral outcomes reveal clustered, dispersed, or random spatial patterns. The results demonstrate that voter turnout exhibits significant geographical disparities, with higher participation in mainland districts such as Parigi Moutong (80%) and Buol (85%), and lower participation in peripheral island areas such as Banggai Laut (85% but numerically small) and Palu (76%). Spatial autocorrelation analysis reveals strong clustering for several parties, notably PDIP (Moran\u0026rsquo;s I\u0026thinsp;=\u0026thinsp;0.61, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Demokrat (0.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and PKB (0.30, p 0.01) in specific regions, while others, such as Nasdem (0.08, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), display random distributions. LISA results further identify localized hotspots of party support (e.g., PDIP in Toili-Banggai, Demokrat in Luwuk, and Nasdem in Tolitoli) as well as coldspots in weaker areas, highlighting fragmented competition in urban centers such as Palu. These findings underscore the importance of spatial approaches to electoral analysis in emerging democracies. They also carry practical implications for electoral management bodies to address turnout disparities and for political parties to refine geographically targeted campaign strategies.\u003c/p\u003e","manuscriptTitle":"Local Indicator of Spatial Association (LISA) and Moran’s I in Spatial Political Analysis of the 2024 Election in Central Sulawesi, Indonesia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 07:39:01","doi":"10.21203/rs.3.rs-7730694/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e8a0e366-7db0-4eb2-9025-11c213444e3b","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-06T07:39:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 07:39:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7730694","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7730694","identity":"rs-7730694","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}