Analyzing the Impact of Store Type and Neighborhood-Level Poverty and Racial Segregation on Crime and Food Access

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McIntire, Brandon George, Raegan Davis, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8919960/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 Despite persistent diet-related health inequities, crime has rarely been examined as a barrier to healthy food access. This study examined crime proximity to food stores and relationships among store type, racial segregation, concentrated poverty, and nearby crime. Observational data on food stores and reported crimes were obtained. The latitude and longitude of crimes within a 200m buffer occurring over six months around each food store address were summed. Variation in mean crime counts was observed across store types, racial segregation (≥ 80% residents reporting race Black or White), and concentrated poverty (≥ 50% households at/below poverty line). Violent crimes were reported within 200m of all store types, and counts were higher around grocery stores (x (mean) = 46.3, p < 0.001), relative to supermarkets (x = 26.17). Food stores in segregated Black census tracts had higher violent crime counts (x = 51.03, p < 0.001) compared to stores in non-segregated tracts (x = 41.46) and segregated White tracts (x = 16.23). Food stores in tracts without concentrated poverty had lower counts (x = 40.06, p < 0.001), relative to those in concentrated poverty tracts (x = 57.87, p < 0.001). These findings demonstrate that violent crimes occurred near food stores in Philadelphia and were particularly common in segregated-Black neighborhoods. Future research should assess the role of crime in limiting healthy food access. food access food insecurity community safety community violence reported crime neighborhood cohesion social cohesion Figures Figure 1 Introduction Access to healthy foods is a critical factor in improving diet quality and reducing the risk of obesity and diet-related diseases. 1 , 2 Black and low-income community members in the U.S. live with food environment inequities, in which they have limited geographical access to healthy foods relative to White and higher income community members. 3 – 5 For example, Zenk et al. (2014) found that as neighborhood median household income decreases, the availability of healthy food options in food stores declines, and that Black and Hispanic communities had lower availability of healthy alternatives in their local stores. 5 These environment characteristics stem from the racist and classist structures, systems, and institutions that have led to an inadequate, inequitable, and unjust food environment for Black Americans and other marginalized groups, including Americans of lower income. 6 – 8 The local food environment has a direct effect on the health of community members, as store type food offerings vary. Supermarkets improve the fruit and vegetable intake of residents, particularly Black Americans, when two or more supermarkets are available within a census tract. 9 However, supermarkets are 4x more common in predominantly White neighborhoods, in comparison to predominantly Black neighborhoods. 10 While smaller stores might offer fresh fruits and vegetables, they stock less options and have higher pricing than at supermarkets. 5 For example, a study in Philadelphia reported that conveniences stores, compared to supermarkets, offered less healthy options across food categories and when offered, the healthy version was often more expensive than the less healthy version of the item. 11 Understanding the differences in store type offerings is important for identifying food environment inequities. For example, low-income and Black and/or Hispanic neighborhoods have less access to supermarkets and more access to convenience stores and grocery stores and leading these communities to have decreased access to fruits and vegetables. 12 – 14 The same systemic racism and structural inequalities that limit food store access in predominately Black and/or low-income neighborhoods also contribute to neighborhood safety. In a study that looked at the National Neighborhood Crime Study data from 1999–2001 and 2010–2013 across 75 cities, researchers found that exposure to violent crimes was significantly more likely in durably segregated (racial residency is homogeneous over time) Black neighborhoods, in comparison to durably White. 15 Income of a neighborhood impacts violence rates, across racial groups. Beard et al. (2017) used neighborhood-level measures of income and found that lower rates of firearm assaults for both White and Black individuals living in higher-income areas. 16 Prior data and research reveal that exposure to crime and healthy food access correlates with neighborhood-level characteristics, like racial segregation and concentrated poverty. Many factors in urban food environments have been assessed for their role in healthy food access and diet quality, but the proximity of crimes to food stores has not. There is limited research examining how proximal violent crime occurs to food store locations within neighborhoods, and if incidents vary by neighborhood racial segregation and/or neighborhood poverty level. Examining crime near food stores may help to clarify an overlooked barrier to healthy food access that residents experience on the ground. Compiling more evidence on the proximity of violent crime to neighborhood food stores in urban areas is needed to develop effective food access interventions, particularly those that address systemic and structural inequities like racial segregation and persistent concentrated poverty within neighborhoods. In a 2021 NIH led workshop, session speakers made a call for action for researchers to better understand why “poor neighborhood conditions coexist in neighborhoods with higher proportions of racially/ethnically minority groups and those experiencing food insecurity.” 17 Reported crime is a component of the neighborhood conditions that co-occur with the more commonly highlighted drivers of healthy food access. Therefore, the goals of this study were twofold, first, to examine the proximity of reported violent crime to SNAP and/or WIC authorized food stores in Philadelphia. Second, to assess the correlation of store type, neighborhood racial segregation and neighborhood poverty rates on observed patterns of reported violent crime proximity to food stores. Methods Setting Philadelphia was the setting of this study, as its residents are diverse in income level, race, and in their experience of neighborhood crime. In 2019, violent crimes in Philadelphia increased by 7.2%, homicides totaled at 356 for the year, and Philadelphia had the highest poverty rate among the country’s 10 largest cities. 18 The data obtained included geographical and statistical data relating to SNAP retailers, WIC retailers, and Philadelphia reported crime from August 2018-January 2019. Data Sources Food Store Location and Type The SNAP authorized store list was publicly accessed through the USDA website. 19 The report provided historical data on the addresses of authorized SNAP retailers during the study period of August 2018-January 2019. The SNAP USDA data that was obtained included stores with ongoing authorization in Philadelphia County and stores that were newly authorized during the study window, meaning that all the stores were active during the study period. The Philadelphia WIC authorized store list was requested through the Pennsylvania WIC program. The list reported the existing WIC retailers during the study period. The WIC retailer list was cross referenced with the retailers provided from the USDA list, and any WIC authorized stores that were not in the SNAP database were added. If a store had the same address but a different name, it was considered the same store. The final dataset contained 1930 stores across Philadelphia County. Stores were then categorized into four groups based on size and retail function; the SNAP retailer database provided Store type designations, and we collapsed them into 4 types. The categories included 1) grocery stores, 2) supermarkets (supermarkets and super stores), 3) convenience stores, and 4) “other” store types (big box store, drug store, discount retailer, specialty food store, and other). 20 When it was unclear what type of food retailer the location was, two reviewers used Google descriptions of locations and when that was not possible, referred to photos of the location to determine what type of food store it was. The USDA Store type definitions were relied on to assess store type, based on the name and size of the store. Neighborhood-Level Racial Segregation and Concentrated Poverty Census tract level data was used to indicate predominantly Black census tracts, sourced from the Decennial Census, and/or census tracts with concentrated poverty, sourced from the 2015 American Community Survey. 21 , 22 Census data follows the Office of Management and Budget federal guidelines related to maintaining data on race and ethnicity and therefore are self-reported/family reported race at the census tract level. The data were used to represent racial segregation in neighborhoods, in which tracts were considered segregated when 80% or more of the respondents reported their race as Black or White. When the tract was not predominately Black or White, it was considered a non-segregated tract. Because geographic concentration of households reporting incomes at or below the poverty line can relate to neighborhood distress and disinvestment in by public and private institutions, we assessed how concentrated poverty related to crimes near food stores. Concentrated poverty was defined as 50% or more of households at or below the poverty line. Crime Location and Type Reported crime datasets were downloaded from publicly available reported crime incidents compiled by the Philadelphia Police Department. 23 , 24 The 2018 and 2019 datasets were downloaded and sorted to include crimes reported to police with a dispatch date between August 2018-January 2019. Based on the City’s Open Data definition, crimes were determined “violent” when they were interpersonal in nature and included violent offenses such as homicide, aggravated assault (with and without a firearm), and robbery (with and without a firearm). 23 , 24 For the analysis, we included assessments of both all crimes and violent crimes, however we will discuss the findings around violent crime because it may be more likely to interrupt the use of a food store. In some cases, more than one crime occurred at the crime event, and each crime was accounted for in the data. Statistical and Geographical Methods The SNAP dataset provided the latitude and longitude of stores. The WIC dataset was geocoded using a geographical informational system (GIS), ArcGIS Pro Version 2.9. 25 The addresses of the crimes were used to identify the crime locations. A distance buffer of 200m was created around each store and used to quantify proximity of crimes to stores. The 200m buffer represents a reported crime within 1.5 blocks of the store. The number of all and violent crimes occurring within the distance buffer around each store was summed. R v4.3.2 Statistical Software was used to clean and merge the race and poverty data into the crime and store dataset. 26 SPSS Version 29 was used to perform descriptive and analytic statistics. 27 The mean and standard deviation (SD) were identified for all reported crimes and violent crimes at 200m from each store. Welch’s analysis of variance (ANOVA) was used (due to unequal variances across the group) to understand if there were significant differences in the means of the crimes around the four store type categories. A post-hoc test with Bonferroni correction distinguished which type of store experienced more or less crime, in comparison to the “other” store types. Next, Welch’s ANOVA with a post-hoc Bonferroni correction was used to observe if there were significant differences in the means of reported violent crimes across non-segregated, racially segregated Black, and racially segregated White tracts and to determine the variance in mean crime. Results Neighborhood Characteristics of Food Store Locations. Of the food stores (n = 1930) included in the study, 60% of stores were in non-segregated tracts (n = 1164), 30% of stores were in segregated Black tracts (n = 585), and 10% of stores were in segregated White tracts (n = 181). Regarding neighborhood poverty, 12% of stores were located in tracts with concentrated poverty (n = 230), while the remaining 88% stores were in tracts without concentrated poverty (n = 1688) (n = 14 stores could not be classified due to missing data). Figure 1 shows the distribution of stores in tracts with concentrated poverty by the store’s racial segregation tract category. Among stores located in non-segregated tracts, 17% (n = 192) were also located in a tract with concentrated poverty. Among stores located within segregated Black census tracts, 7% (n = 38) of the stores were located in a tract with concentrated poverty. Among stores located within segregated White tracts, none of the stores were in a tract with concentrated poverty (Fig. 1 ). Store Type and Reported Crimes. Table I presents the mean reported crime counts within the 200m buffer for all food stores. There were 1930 stores in total, with 739 (38%) grocery stores, 101 (5%) supermarkets, 665 (35%) convenience stores, and 425 (22%) “other” store types. The results narrative will focus on reported violent crimes within 200m of stores, but Table I includes data on all reported crimes and violent crimes at 200m. Within 200m of stores, the overall mean (SD) number of reported violent crimes was 41.95 (33.11) over the six-month study period. The mean reported violent crime count within 200m was the highest for grocery stores (x = 46.30) and lowest for supermarkets (x = 26.17) (Table I). There were significant differences among the four store type categories in violent crime counts within 200m (p < .001) (Table I). The average number of reported violent crimes within 200 meters was significantly lower around supermarkets and higher around grocery stores than the comparison store types. However, reported violent crime within 200m did not significantly differ between convenience and “other” store types (Table II). Neighborhood Characteristics and Reported Crime. Table III presents the mean differences of reported violent crime within 200m of a store by racial segregation at the tract-level. Differences were observed for racial segregation (p < 0.01), demonstrating that food stores in racially segregated Black tracts had higher mean reported violent crimes within the 200m buffer (x = 51.03, SD = 24.59) relative to food stores in racially segregated White tracts (x = 16.23, SD = 11.54). Similarly, non-segregated tracts had higher mean reported violent crime (x = 41.46, SD = 36.7) than racially segregated White tracts. An independent-samples t-test was used to evaluate differences of reported crime within 200m of a store by concentrated poverty at the tract-level. The results found that tracts without concentrated poverty had lower mean reported violent crime (p < 0.001, x = 40.06, SD = 33.04) than food stores in tracts with concentrated poverty (x = 57.87, SD = 29.11). Since no stores in White tracts were located in concentrated poverty, we were unable to assess the interaction effect between store type, racial segregation, and poverty. Discussion This study reveals two key findings related to crime, food store type, and neighborhood level characteristics in Philadelphia. First, areas around supermarkets consistently had significantly lower counts of violent crime while areas surrounding grocery stores experienced significantly more violent crimes. Second, non-segregated and racially segregated Black neighborhoods experienced substantially higher mean counts of reported violent crime compared to segregated White tracts. Food stores within concentrated poverty tracts had significantly higher violent crime, but it is important to note that the majority of stores were located outside of these tracts. Store type was related to proximal reported violent crime counts. Supermarkets had less violent crime reported nearby, relative to the comparison store types. This finding is of value because supermarkets play an important role in nutritional health as they offer the largest selection of foods, particularly fresh foods. Research investigating crime around supermarkets is limited but has found supermarkets to be associated with lower violent crime. 28 , 29 However, Tung et al. (2018) found that poor neighborhood safety led to decreased access for large grocery stores. 30 There are some elements that may contribute to lower counts of crime around supermarkets, such as improved lighting outside the building and being located in commercial rather than residential districts (i.e. lower residential population surrounding the store). However, this study did not properly account for confounding, so we can only describe the rate at which reported crimes are happening near stores. Future research should consider whether the physical elements of stores affect the propensity of crime. Reported violent crime was higher around grocery stores than the comparison store types. This does not align well with existing research. For example, Singleton et al. (2022) found that crime was lower around grocery stores and supermarkets relative to convenience and smaller stores. 29 But they did not differentiate grocery stores from supermarkets in that study. Tung et al. (2018) found that residents reported poor neighborhood safety was associated with decreased access to grocery stores within a mile of their home and that reported past experience with crime led to bypassing nearby stores. 30 Grocery stores are typically embedded in urban residential blocks, where access to supermarkets may be low, and this might be associated with neighborhood characteristics. Bower et al. (2014) supports this, reporting that as census tract poverty increases, grocery stores increase and supermarkets decrease. 12 Our findings demonstrate that grocery stores, specifically in segregated Black neighborhoods, experience greater reported crime. Future research should assess whether reported crimes present a barrier to the use of stores with healthy foods. Additionally, our analysis highlighted structural inequalities in Philadelphia, as no predominantly White neighborhoods experienced concentrated poverty that we were unable to examine the interaction between race, income, and crime. Similar to our findings that stores in neighborhoods with concentrated poverty experienced greater counts of reported crimes, prior research found that neighborhood poverty levels were a significant predictor of violent crime rates in Philadelphia. Thus, engaging with residents in communities with concentrated poverty should be a priority. 31 The goal of this research is not to further link crime with low-resource neighborhoods of color, but to reinforce that vulnerable neighborhoods continue to experience disinvestment and structural barriers that impact health. These findings support the need for urban cities to ensure that environmental factors, such as violence, do not inhibit the use of vital food resources for residents. Programs and policies addressing food access should include solutions for mitigating the effects of violent crimes near stores, especially in non-segregated and segregated Black low-income census tracts. Limitations It is important to note that our study only includes reported crimes, which may not accurately represent actual crimes. In predominantly Black neighborhoods, police misconduct often leads to a decline in the trust of policing and therefore a decrease in the reporting of crimes. 32 Additionally, we did not control for population density in our study. This could impact the interpretation of crime counts. For example, it is likely that supermarkets are located in commercial areas with fewer residential households, and therefore, there is less opportunity for crime due to the lower density of people. However, it has been found that greater concentrations of crime may or may not relate to specific area’s population density. 33 Lastly, we could have considered annual sales or food offerings as a measure for ranking our food store types. Using food offerings, such as 5 or more fresh fruits offered onsite, would have helped to distinguish grocery stores from convenience stores. Conclusion Our findings suggest that reported violent crimes are occurring near food stores. In order to ensure that all people have access to healthy and affordable foods, we recommend more research to understand if and how crime near food stores impacts healthy food access. Additionally, residents and grass roots organizations should be directly involved in brainstorming and decision-making around solutions for addressing this issue. Declarations Funding Sources The authors received no specific funding for this work. Acknowledgements Thank you to John McAna for his contributions to this work. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8919960","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627129889,"identity":"8ae749c4-e234-4746-bf80-82f2493ea550","order_by":0,"name":"Nina Diamond","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABE0lEQVRIie3QMWuDQBTA8SeCLi92fWKpn6CgCNZQ23wVg+DknDENFOxi9votOknHhEBdDrsKXQqFzhe6pFNrLBUCGhwLvf9wyt37cSiASPQXk/eLQs07cYQTdfF7tBpA9HsEPW0n+0iT8vMwEAaQC3W05jDzzPO75frFP/UJ1eUb3z7CmVYFnWR8q4UEJdk5K8PLGCNCLFw9Y+DoPcTaoEVSQlJexa4R42Y+oUiRRwlMH/qJs6vJpCEefhGa7/JHTW6OEHd/y7QhgCtCUsCoSWAdIV5QUpgz5oxTDOtviRQ9S8jO2Gs3eWZOxWfzq7xI7eozva7/2JPMt4lvakX3LU3tkZS2e9Q/fthu6KBIJBL9p74BiVJXQ69WQ1oAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0008-5993-8040","institution":"Thomas Jefferson University - Center City Campus: Thomas Jefferson University","correspondingAuthor":true,"prefix":"","firstName":"Nina","middleName":"","lastName":"Diamond","suffix":""},{"id":627129890,"identity":"34d22136-83db-4a8d-aea8-05f360f4fef1","order_by":1,"name":"Russell K. McIntire","email":"","orcid":"","institution":"Lehigh University","correspondingAuthor":false,"prefix":"","firstName":"Russell","middleName":"K.","lastName":"McIntire","suffix":""},{"id":627129891,"identity":"f3b2ed66-9ba9-448c-853e-0a5963e634b1","order_by":2,"name":"Brandon George","email":"","orcid":"","institution":"Thomas Jefferson University College of Population Health","correspondingAuthor":false,"prefix":"","firstName":"Brandon","middleName":"","lastName":"George","suffix":""},{"id":627129892,"identity":"21fa9505-5757-4d58-8ef8-fe6deb9132f2","order_by":3,"name":"Raegan Davis","email":"","orcid":"","institution":"Thomas Jefferson University College of Population Health","correspondingAuthor":false,"prefix":"","firstName":"Raegan","middleName":"","lastName":"Davis","suffix":""},{"id":627129893,"identity":"7735c13d-a594-4950-a2b5-e4a40366b1a7","order_by":4,"name":"Katie DiSantis","email":"","orcid":"","institution":"Thomas Jefferson University College of Population Health","correspondingAuthor":false,"prefix":"","firstName":"Katie","middleName":"","lastName":"DiSantis","suffix":""}],"badges":[],"createdAt":"2026-02-19 17:59:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8919960/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8919960/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108144150,"identity":"d778d866-6610-4e50-b4b0-609a2b6e36ad","added_by":"auto","created_at":"2026-04-29 20:25:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148211,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe Distribution of Food Stores in Concentrated Poverty with Segregation Across Census Tracts.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8919960/v1/32df2805b19ef483b7753f9f.jpg"},{"id":109501474,"identity":"0edcff3a-ff9c-42e9-81e5-bfdd5dfdac45","added_by":"auto","created_at":"2026-05-18 23:02:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":307353,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8919960/v1/5874811c-2c32-4176-90bd-db70b4aec73e.pdf"},{"id":108144152,"identity":"af409505-b4b5-41b1-9bdd-983218a4f252","added_by":"auto","created_at":"2026-04-29 20:25:23","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":15758,"visible":true,"origin":"","legend":"","description":"","filename":"UrbanHealthTableI.docx","url":"https://assets-eu.researchsquare.com/files/rs-8919960/v1/77b61c04cd80a25860888ed0.docx"},{"id":108144153,"identity":"00a937a9-260f-4e19-a11b-ff67678267cc","added_by":"auto","created_at":"2026-04-29 20:25:23","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17098,"visible":true,"origin":"","legend":"","description":"","filename":"UrbanHealthTableII.docx","url":"https://assets-eu.researchsquare.com/files/rs-8919960/v1/dda0a8244e0be45ed350ad6e.docx"},{"id":108183145,"identity":"1544055c-7c99-45e0-85b1-ba407cf5a6a0","added_by":"auto","created_at":"2026-04-30 08:59:49","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15377,"visible":true,"origin":"","legend":"","description":"","filename":"UrbanHealthTableIII.docx","url":"https://assets-eu.researchsquare.com/files/rs-8919960/v1/f1addf5ca6b892df407f60a5.docx"}],"financialInterests":"","formattedTitle":"Analyzing the Impact of Store Type and Neighborhood-Level Poverty and Racial Segregation on Crime and Food Access","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccess to healthy foods is a critical factor in improving diet quality and reducing the risk of obesity and diet-related diseases.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Black and low-income community members in the U.S. live with food environment inequities, in which they have limited geographical access to healthy foods relative to White and higher income community members.\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e For example, Zenk et al. (2014) found that as neighborhood median household income decreases, the availability of healthy food options in food stores declines, and that Black and Hispanic communities had lower availability of healthy alternatives in their local stores.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e These environment characteristics stem from the racist and classist structures, systems, and institutions that have led to an inadequate, inequitable, and unjust food environment for Black Americans and other marginalized groups, including Americans of lower income.\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe local food environment has a direct effect on the health of community members, as store type food offerings vary. Supermarkets improve the fruit and vegetable intake of residents, particularly Black Americans, when two or more supermarkets are available within a census tract.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e However, supermarkets are 4x more common in predominantly White neighborhoods, in comparison to predominantly Black neighborhoods.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e While smaller stores might offer fresh fruits and vegetables, they stock less options and have higher pricing than at supermarkets.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e For example, a study in Philadelphia reported that conveniences stores, compared to supermarkets, offered less healthy options across food categories and when offered, the healthy version was often more expensive than the less healthy version of the item.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e Understanding the differences in store type offerings is important for identifying food environment inequities. For example, low-income and Black and/or Hispanic neighborhoods have less access to supermarkets and more access to convenience stores and grocery stores and leading these communities to have decreased access to fruits and vegetables.\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe same systemic racism and structural inequalities that limit food store access in predominately Black and/or low-income neighborhoods also contribute to neighborhood safety. In a study that looked at the National Neighborhood Crime Study data from 1999\u0026ndash;2001 and 2010\u0026ndash;2013 across 75 cities, researchers found that exposure to violent crimes was significantly more likely in durably segregated (racial residency is homogeneous over time) Black neighborhoods, in comparison to durably White.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Income of a neighborhood impacts violence rates, across racial groups. Beard et al. (2017) used neighborhood-level measures of income and found that lower rates of firearm assaults for both White and Black individuals living in higher-income areas.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e Prior data and research reveal that exposure to crime and healthy food access correlates with neighborhood-level characteristics, like racial segregation and concentrated poverty. Many factors in urban food environments have been assessed for their role in healthy food access and diet quality, but the proximity of crimes to food stores has not.\u003c/p\u003e \u003cp\u003eThere is limited research examining how proximal violent crime occurs to food store locations within neighborhoods, and if incidents vary by neighborhood racial segregation and/or neighborhood poverty level. Examining crime near food stores may help to clarify an overlooked barrier to healthy food access that residents experience on the ground. Compiling more evidence on the proximity of violent crime to neighborhood food stores in urban areas is needed to develop effective food access interventions, particularly those that address systemic and structural inequities like racial segregation and persistent concentrated poverty within neighborhoods. In a 2021 NIH led workshop, session speakers made a call for action for researchers to better understand why \u0026ldquo;poor neighborhood conditions coexist in neighborhoods with higher proportions of racially/ethnically minority groups and those experiencing food insecurity.\u0026rdquo;\u003csup\u003e17\u003c/sup\u003e Reported crime is a component of the neighborhood conditions that co-occur with the more commonly highlighted drivers of healthy food access. Therefore, the goals of this study were twofold, first, to examine the proximity of reported violent crime to SNAP and/or WIC authorized food stores in Philadelphia. Second, to assess the correlation of store type, neighborhood racial segregation and neighborhood poverty rates on observed patterns of reported violent crime proximity to food stores.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSetting\u003c/h2\u003e \u003cp\u003ePhiladelphia was the setting of this study, as its residents are diverse in income level, race, and in their experience of neighborhood crime. In 2019, violent crimes in Philadelphia increased by 7.2%, homicides totaled at 356 for the year, and Philadelphia had the highest poverty rate among the country\u0026rsquo;s 10 largest cities.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e The data obtained included geographical and statistical data relating to SNAP retailers, WIC retailers, and Philadelphia reported crime from August 2018-January 2019.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Sources\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFood Store Location and Type\u003c/h2\u003e \u003cp\u003eThe SNAP authorized store list was publicly accessed through the USDA website.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e The report provided historical data on the addresses of authorized SNAP retailers during the study period of August 2018-January 2019. The SNAP USDA data that was obtained included stores with ongoing authorization in Philadelphia County and stores that were newly authorized during the study window, meaning that all the stores were active during the study period.\u003c/p\u003e \u003cp\u003eThe Philadelphia WIC authorized store list was requested through the Pennsylvania WIC program. The list reported the existing WIC retailers during the study period. The WIC retailer list was cross referenced with the retailers provided from the USDA list, and any WIC authorized stores that were not in the SNAP database were added. If a store had the same address but a different name, it was considered the same store. The final dataset contained 1930 stores across Philadelphia County. Stores were then categorized into four groups based on size and retail function; the SNAP retailer database provided Store type designations, and we collapsed them into 4 types. The categories included 1) grocery stores, 2) supermarkets (supermarkets and super stores), 3) convenience stores, and 4) \u0026ldquo;other\u0026rdquo; store types (big box store, drug store, discount retailer, specialty food store, and other).\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e When it was unclear what type of food retailer the location was, two reviewers used Google descriptions of locations and when that was not possible, referred to photos of the location to determine what type of food store it was. The USDA Store type definitions were relied on to assess store type, based on the name and size of the store.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eNeighborhood-Level Racial Segregation and Concentrated Poverty\u003c/h3\u003e\n\u003cp\u003eCensus tract level data was used to indicate predominantly Black census tracts, sourced from the Decennial Census, and/or census tracts with concentrated poverty, sourced from the 2015 American Community Survey.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Census data follows the Office of Management and Budget federal guidelines related to maintaining data on race and ethnicity and therefore are self-reported/family reported race at the census tract level. The data were used to represent racial segregation in neighborhoods, in which tracts were considered segregated when 80% or more of the respondents reported their race as Black or White. When the tract was not predominately Black or White, it was considered a non-segregated tract. Because geographic concentration of households reporting incomes at or below the poverty line can relate to neighborhood distress and disinvestment in by public and private institutions, we assessed how concentrated poverty related to crimes near food stores. Concentrated poverty was defined as 50% or more of households at or below the poverty line.\u003c/p\u003e\n\u003ch3\u003eCrime Location and Type\u003c/h3\u003e\n\u003cp\u003eReported crime datasets were downloaded from publicly available reported crime incidents compiled by the Philadelphia Police Department.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e The 2018 and 2019 datasets were downloaded and sorted to include crimes reported to police with a dispatch date between August 2018-January 2019. Based on the City\u0026rsquo;s Open Data definition, crimes were determined \u0026ldquo;violent\u0026rdquo; when they were interpersonal in nature and included violent offenses such as homicide, aggravated assault (with and without a firearm), and robbery (with and without a firearm).\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e For the analysis, we included assessments of both all crimes and violent crimes, however we will discuss the findings around violent crime because it may be more likely to interrupt the use of a food store. In some cases, more than one crime occurred at the crime event, and each crime was accounted for in the data.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical and Geographical Methods\u003c/h2\u003e \u003cp\u003eThe SNAP dataset provided the latitude and longitude of stores. The WIC dataset was geocoded using a geographical informational system (GIS), ArcGIS Pro Version 2.9.\u003csup\u003e25\u003c/sup\u003e The addresses of the crimes were used to identify the crime locations. A distance buffer of 200m was created around each store and used to quantify proximity of crimes to stores. The 200m buffer represents a reported crime within 1.5 blocks of the store. The number of all and violent crimes occurring within the distance buffer around each store was summed. R v4.3.2 Statistical Software was used to clean and merge the race and poverty data into the crime and store dataset.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSPSS Version 29 was used to perform descriptive and analytic statistics.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e The mean and standard deviation (SD) were identified for all reported crimes and violent crimes at 200m from each store. Welch\u0026rsquo;s analysis of variance (ANOVA) was used (due to unequal variances across the group) to understand if there were significant differences in the means of the crimes around the four store type categories. A post-hoc test with Bonferroni correction distinguished which type of store experienced more or less crime, in comparison to the \u0026ldquo;other\u0026rdquo; store types. Next, Welch\u0026rsquo;s ANOVA with a post-hoc Bonferroni correction was used to observe if there were significant differences in the means of reported violent crimes across non-segregated, racially segregated Black, and racially segregated White tracts and to determine the variance in mean crime.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eNeighborhood Characteristics of Food Store Locations.\u003c/b\u003e Of the food stores (n\u0026thinsp;=\u0026thinsp;1930) included in the study, 60% of stores were in non-segregated tracts (n\u0026thinsp;=\u0026thinsp;1164), 30% of stores were in segregated Black tracts (n\u0026thinsp;=\u0026thinsp;585), and 10% of stores were in segregated White tracts (n\u0026thinsp;=\u0026thinsp;181). Regarding neighborhood poverty, 12% of stores were located in tracts with concentrated poverty (n\u0026thinsp;=\u0026thinsp;230), while the remaining 88% stores were in tracts without concentrated poverty (n\u0026thinsp;=\u0026thinsp;1688) (n\u0026thinsp;=\u0026thinsp;14 stores could not be classified due to missing data). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the distribution of stores in tracts with concentrated poverty by the store\u0026rsquo;s racial segregation tract category. Among stores located in non-segregated tracts, 17% (n\u0026thinsp;=\u0026thinsp;192) were also located in a tract with concentrated poverty. Among stores located within segregated Black census tracts, 7% (n\u0026thinsp;=\u0026thinsp;38) of the stores were located in a tract with concentrated poverty. Among stores located within segregated White tracts, none of the stores were in a tract with concentrated poverty (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStore Type and Reported Crimes.\u003c/b\u003e Table I presents the mean reported crime counts within the 200m buffer for all food stores. There were 1930 stores in total, with 739 (38%) grocery stores, 101 (5%) supermarkets, 665 (35%) convenience stores, and 425 (22%) \u0026ldquo;other\u0026rdquo; store types. The results narrative will focus on reported violent crimes within 200m of stores, but Table I includes data on all reported crimes and violent crimes at 200m. Within 200m of stores, the overall mean (SD) number of reported violent crimes was 41.95 (33.11) over the six-month study period.\u003c/p\u003e \u003cp\u003eThe mean reported violent crime count within 200m was the highest for grocery stores (x\u0026thinsp;=\u0026thinsp;46.30) and lowest for supermarkets (x\u0026thinsp;=\u0026thinsp;26.17) (Table I). There were significant differences among the four store type categories in violent crime counts within 200m (p \u0026lt; .001) (Table I). The average number of reported violent crimes within 200 meters was significantly lower around supermarkets and higher around grocery stores than the comparison store types. However, reported violent crime within 200m did not significantly differ between convenience and \u0026ldquo;other\u0026rdquo; store types (Table II).\u003c/p\u003e \u003cp\u003e \u003cb\u003eNeighborhood Characteristics and Reported Crime.\u003c/b\u003e Table III presents the mean differences of reported violent crime within 200m of a store by racial segregation at the tract-level. Differences were observed for racial segregation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), demonstrating that food stores in racially segregated Black tracts had higher mean reported violent crimes within the 200m buffer (x\u0026thinsp;=\u0026thinsp;51.03, SD\u0026thinsp;=\u0026thinsp;24.59) relative to food stores in racially segregated White tracts (x\u0026thinsp;=\u0026thinsp;16.23, SD\u0026thinsp;=\u0026thinsp;11.54). Similarly, non-segregated tracts had higher mean reported violent crime (x\u0026thinsp;=\u0026thinsp;41.46, SD\u0026thinsp;=\u0026thinsp;36.7) than racially segregated White tracts. An independent-samples t-test was used to evaluate differences of reported crime within 200m of a store by concentrated poverty at the tract-level. The results found that tracts without concentrated poverty had lower mean reported violent crime (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, x\u0026thinsp;=\u0026thinsp;40.06, SD\u0026thinsp;=\u0026thinsp;33.04) than food stores in tracts with concentrated poverty (x\u0026thinsp;=\u0026thinsp;57.87, SD\u0026thinsp;=\u0026thinsp;29.11). Since no stores in White tracts were located in concentrated poverty, we were unable to assess the interaction effect between store type, racial segregation, and poverty.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study reveals two key findings related to crime, food store type, and neighborhood level characteristics in Philadelphia. First, areas around supermarkets consistently had significantly lower counts of violent crime while areas surrounding grocery stores experienced significantly more violent crimes. Second, non-segregated and racially segregated Black neighborhoods experienced substantially higher mean counts of reported violent crime compared to segregated White tracts. Food stores within concentrated poverty tracts had significantly higher violent crime, but it is important to note that the majority of stores were located outside of these tracts.\u003c/p\u003e \u003cp\u003eStore type was related to proximal reported violent crime counts. Supermarkets had less violent crime reported nearby, relative to the comparison store types. This finding is of value because supermarkets play an important role in nutritional health as they offer the largest selection of foods, particularly fresh foods. Research investigating crime around supermarkets is limited but has found supermarkets to be associated with lower violent crime.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e However, Tung et al. (2018) found that poor neighborhood safety led to decreased access for large grocery stores.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e There are some elements that may contribute to lower counts of crime around supermarkets, such as improved lighting outside the building and being located in commercial rather than residential districts (i.e. lower residential population surrounding the store). However, this study did not properly account for confounding, so we can only describe the rate at which reported crimes are happening near stores. Future research should consider whether the physical elements of stores affect the propensity of crime.\u003c/p\u003e \u003cp\u003eReported violent crime was higher around grocery stores than the comparison store types. This does not align well with existing research. For example, Singleton et al. (2022) found that crime was lower around grocery stores and supermarkets relative to convenience and smaller stores.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e But they did not differentiate grocery stores from supermarkets in that study. Tung et al. (2018) found that residents reported poor neighborhood safety was associated with decreased access to grocery stores within a mile of their home and that reported past experience with crime led to bypassing nearby stores.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Grocery stores are typically embedded in urban residential blocks, where access to supermarkets may be low, and this might be associated with neighborhood characteristics. Bower et al. (2014) supports this, reporting that as census tract poverty increases, grocery stores increase and supermarkets decrease.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Our findings demonstrate that grocery stores, specifically in segregated Black neighborhoods, experience greater reported crime. Future research should assess whether reported crimes present a barrier to the use of stores with healthy foods.\u003c/p\u003e \u003cp\u003eAdditionally, our analysis highlighted structural inequalities in Philadelphia, as no predominantly White neighborhoods experienced concentrated poverty that we were unable to examine the interaction between race, income, and crime. Similar to our findings that stores in neighborhoods with concentrated poverty experienced greater counts of reported crimes, prior research found that neighborhood poverty levels were a significant predictor of violent crime rates in Philadelphia. Thus, engaging with residents in communities with concentrated poverty should be a priority.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe goal of this research is not to further link crime with low-resource neighborhoods of color, but to reinforce that vulnerable neighborhoods continue to experience disinvestment and structural barriers that impact health. These findings support the need for urban cities to ensure that environmental factors, such as violence, do not inhibit the use of vital food resources for residents. Programs and policies addressing food access should include solutions for mitigating the effects of violent crimes near stores, especially in non-segregated and segregated Black low-income census tracts.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eIt is important to note that our study only includes reported crimes, which may not accurately represent actual crimes. In predominantly Black neighborhoods, police misconduct often leads to a decline in the trust of policing and therefore a decrease in the reporting of crimes.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Additionally, we did not control for population density in our study. This could impact the interpretation of crime counts. For example, it is likely that supermarkets are located in commercial areas with fewer residential households, and therefore, there is less opportunity for crime due to the lower density of people. However, it has been found that greater concentrations of crime may or may not relate to specific area\u0026rsquo;s population density.\u003csup\u003e33\u003c/sup\u003e Lastly, we could have considered annual sales or food offerings as a measure for ranking our food store types. Using food offerings, such as 5 or more fresh fruits offered onsite, would have helped to distinguish grocery stores from convenience stores.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings suggest that reported violent crimes are occurring near food stores. In order to ensure that all people have access to healthy and affordable foods, we recommend more research to understand if and how crime near food stores impacts healthy food access. Additionally, residents and grass roots organizations should be directly involved in brainstorming and decision-making around solutions for addressing this issue.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding Sources\u003c/h2\u003e \u003cp\u003eThe authors received no specific funding for this work.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThank you to John McAna for his contributions to this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGregory CA, Coleman-Jensen A. Food insecurity, chronic disease, and health among working-age adults. Economic Research Report No. 235. 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J Quant Criminol. 2022;38(1):295\u0026ndash;321. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10940-021-09495-9\u003c/span\u003e\u003cspan address=\"10.1007/s10940-021-09495-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"food access, food insecurity, community safety, community violence, reported crime, neighborhood cohesion, social cohesion","lastPublishedDoi":"10.21203/rs.3.rs-8919960/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8919960/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite persistent diet-related health inequities, crime has rarely been examined as a barrier to healthy food access. This study examined crime proximity to food stores and relationships among store type, racial segregation, concentrated poverty, and nearby crime. Observational data on food stores and reported crimes were obtained. The latitude and longitude of crimes within a 200m buffer occurring over six months around each food store address were summed. Variation in mean crime counts was observed across store types, racial segregation (\u0026ge;\u0026thinsp;80% residents reporting race Black or White), and concentrated poverty (\u0026ge;\u0026thinsp;50% households at/below poverty line). Violent crimes were reported within 200m of all store types, and counts were higher around grocery stores (x (mean)\u0026thinsp;=\u0026thinsp;46.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), relative to supermarkets (x\u0026thinsp;=\u0026thinsp;26.17). Food stores in segregated Black census tracts had higher violent crime counts (x\u0026thinsp;=\u0026thinsp;51.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to stores in non-segregated tracts (x\u0026thinsp;=\u0026thinsp;41.46) and segregated White tracts (x\u0026thinsp;=\u0026thinsp;16.23). Food stores in tracts without concentrated poverty had lower counts (x\u0026thinsp;=\u0026thinsp;40.06, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), relative to those in concentrated poverty tracts (x\u0026thinsp;=\u0026thinsp;57.87, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These findings demonstrate that violent crimes occurred near food stores in Philadelphia and were particularly common in segregated-Black neighborhoods. Future research should assess the role of crime in limiting healthy food access.\u003c/p\u003e","manuscriptTitle":"Analyzing the Impact of Store Type and Neighborhood-Level Poverty and Racial Segregation on Crime and Food Access","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 20:25:18","doi":"10.21203/rs.3.rs-8919960/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":"18ae9455-5dd4-4131-ba3f-b5151cdb25c4","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Reject (without peer review)","date":"2026-05-18T17:35:44+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T23:02:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 20:25:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8919960","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8919960","identity":"rs-8919960","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}