Georeferencing and classifying love hotels in Lima: A comparison between field research and Google Maps

Georeferencing and classifying love hotels in Lima: A comparison between field research and Google Maps | 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 Georeferencing and classifying love hotels in Lima: A comparison between field research and Google Maps Ignacio Franco Vega, Melanie Channon, Eleonora Fichera This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7311058/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 Hostales (love hotels) are a common yet understudied institution in Perú. Their widespread use and capacity to concentrate large numbers of people about to have sex make them a potentially important setting for sexual health interventions. Given Perú’s high rates of unintended pregnancies, its potential role as a location for implementing new sexual health policies should be explored. The first step towards this is to document their prevalence, characteristics and distribution. We conducted a census over a 4 km 2 area of southern Lima, georeferencing the location of all hostales and photographing their visual characteristics. We classified them according to their size and appearance, and the findings were compared against their appearance in Google Maps to assess the platform’s reliability as a secondary data source. We found 114 hostales , one every 35 m 2 . Their spatial distribution varied across neighbourhoods, reflecting distinct patterns based on local characteristics. Most of them stated their sexual nature through their advertisements, names or offered amenities. Notably, only 45% of those we found were present in Google Maps. Hostales are a highly prevalent and heterogeneous business. They differ considerably in terms of offered services, sizes, and styles. They cluster around commercial areas but can also be found in residential and industrial areas. Google Maps proved unreliable as a data source. Given their ubiquity and potential for policy impact, hostales warrant more attention. Targeted interventions in them could reach a sizeable sample of at-risk populations, providing a unique opportunity to improve sexual health. Love hotels Hostales Peru Georeferencing Google Maps Reliability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction About 52% of pregnancies in Perú are unintended 1 . These pregnancies have a substantial impact on the well-being of those involved 2,3 . There is little information on the prevalence of sexually transmitted infections (STIs) at a national level, but the rates appear stagnant at best 4 . Neither unintended pregnancies nor STIs are distributed homogeneously in the population. Unintended pregnancies are considerably more frequent in disadvantaged households. At the same time, STIs are more common in men who have sex with men and sex workers 4 , groups frequently discriminated against, both by the general public and governmental bodies 6,7 . Under these conditions, inadequate access to sexual health services operates as a mechanism that reinforces the cycle of poverty. New strategies are needed to face these issues. Field experiments in hostales in Lima have proven that a condom distribution policy anchored in them can double the odds of condom use 8 . We need to learn more about these institutions to assess their role as potential settings for policy. Now, what exactly are hostales ? Aside from the work carried out by Ccopa in the mid-90s 9 , there is little systematised information about them. We know they are short-term lodgings where couples go to have sex. This is evident in the venues’ names, street signs, and services. Hostales advertise different amenities; some are common to other accommodations (e.g., hot water, cable TV, Wi-Fi). In contrast, others cater to more erotic interests (e.g., XXX videos, Tantra chairs) or secrecy (e.g., hidden entrances, receipts under fake names). In this regard, they are very similar to Japanese Love hotels 10 or Brazilian motéis 11 . They differ considerably in size, ranging from some with only a couple of very basic rooms to those with dozens of theme-decorated rooms. Hostales can be found all over the city in both affluent and resource-deprived areas. They can be located in commercial, residential, and even industrial areas. Room prices vary depending on the quality of the room, the services it includes, and the area where the hostal is located. It is important to note that while hostales may be used by sex workers and their clients, they are not brothels. These establishments provide a space for such activities but rarely facilitate them. Sex work is not illegal in the country, however, promoting or profiting from another person’s sex work (procuring) is a criminal offense 12 . As a result, hostal owners are careful to avoid any involvement in such activities. These businesses have the potential to contribute to the development of sexual health policies. As the settings where sexual encounters occur, they minimise the distance between the point of intervention and the act itself. Due to this, some of the information processing and planning biases that increase risky sexual behaviour can be avoided 13–15 . Additionally, as they are private spaces, the inhibiting effect of social emotions such as shame or guilt is lessened 16 . Finally, they are spaces frequented by various high-risk groups, especially sex workers. Understanding them better is necessary to properly design a policy around them. One of the first steps to understanding them is to establish how many of them there are. Anyone who pays attention to the capital’s landscape is going to notice their predominance. They appear to be everywhere, but despite their abundance, they remain thoroughly understudied and ignored by policymakers, appearing in none of Peru’s sexual and reproductive health policies. Ccopa is the only researcher that has attempted to quantify them before. Using administrative records from the Ministry of Tourism, he discovered that Lima had 1965 hospitality businesses in 1998; 100 of which were in the San Juan de Miraflores district 9 . As a point of comparison there are around 5000 motels in Brazil 11 (a country with a population six times larger to that of Perú). The primary objective of this study is to understand the spatial distribution and visual characteristics of hostales by conducting a census of their locations and the visual features and symbols that define them within a specific geographical area. To achieve this, the study addresses the following specific research questions: 1. What are the locations and visual characteristics of hostales? o Can hostales be classified into distinct visual styles based on their visual characteristics (e.g., signs, façades, symbols)? o What ideas and values are communicated through these visual styles and what do they say about their role in the city’s social, cultural, and economic life? 2. How does the prevalence of specific types or styles of hostales vary across different areas within the study region? 3. How reliable is Google Maps as a secondary data source for identifying the distribution and types of hostales? o Are certain types or styles of hostales more likely to be excluded from Google Maps and what factors might influence these omissions? If Google Maps proves to be a serviceable source of information, further studies could be conducted with their data. This is especially important as remote research is increasingly becoming the norm and assessing the criterion validity of its findings is proving to be an essential measure in ensuring their quality 17 , especially in non-WEIRD countries (Western, Educated, Industrialised, Rich and Democratic) 18,19 . Methods Site We surveyed the 4 km 2 quadrant limited by Avenidas Los Héroes, Víctor Castro Iglesias, Miguel Iglesias and Panamerica Sur. The entire section is in the San Juan de Miraflores district (SJM) in the southern cone of Lima. Most households in SJM are lower-middle income households. The surveyed section, however, was comprised of commercial and industrial areas and upper-middle income households 20 . The chosen area has a few noteworthy aspects. First, it includes residential, commercial, industrial and mixed-use areas. Second, it includes a series of important landmarks such as Mall del Sur (one of the largest malls in Lima’s southern cone), the Maria Auxiliadora hospital (one of the largest in the city), the Atocongo bus terminal (one of the most frequently used entrances to Lima), the offices of the municipal government, one large and two midsize markets, and two technical/pedagogical colleges. Third, it is very well-connected to the rest of the city. Data collection technique We surveyed every street in the quadrant. When encountering a hostal, hostel, alojamiento or hotel , we took a series of georeferenced pictures of its façade and street sign. We systematically surveyed the area until we were confident that we had identified every hostal. This took a total of eight days during the period between December 2021 and March 2022. Data processing and analysis The data was processed in several steps: 1. We created a dataset with the name of each hostal . 2. We extracted the GPS coordinates from the pictures’ metadata and added them to the dataset using ExifTool 21 . 3. We examined each picture and filled in additional fields in the dataset, such as advertised services and building size. 4. We conducted a thorough manual search for each hostal in Google Maps and determined if it was included in their database. In June 2022. 5. We established two measures of Google presence: a. A detailed version (Google Status): · Yes : The hostal can be found in the map in Google Maps in the correct location. · Wrong name : There is a hostal under the wrong name in the same spot on the map. · Wrong location : The hostal appears on the map; however, its location is substantially incorrect. · Not visible : The hostal is in Google Maps and in the correct location; however, it cannot be seen on the map; it is only found by writing the name in the search bar or by clicking on the building and looking for it. This is because when more than one business is located in the same building, the Google interface only shows one of them. · No : The hostal cannot be found on Google Maps. b. A simplified, binary version of this same Status (Google Presence). 1. Yes : Yes + Not visible 2. No : No + Wrong location + Wrong name 6. We imported the database created into R. 7. We constructed a map that would serve for the presentation of the geographical information. We personalised the Google Maps output using Google Maps Styling Wizard, removed undesired information to declutter the map that would become the canvas of our thematic maps (e.g., street, block and business names), and exported the code for the personalised map using JavaScript Object Notation (JSON). 8. We imported this information into R using the RJSONIO and maggittr 22,23 and used the googleway R 24 and the Maps Static API from Google Cloud Platform to draw a map of the area. 9. We plotted the location of the georeferenced hostales in the constructed canvas using ggmap 25 and optimised its visualisations with ggsci, Rcolorbrewer and scales 26–28 . 10. We modelled Google Presence to identify if a particular hostal characteristic made it less likely to be included in Google Maps with R base and Stargazer 29 . We created two variables to further classify hostales . The first variable is Hostal Size. We built a variable based on the number of rooms visible from the outside and the presence or absence of a garage. The logic is simple; the more visible rooms a hostal has, the larger it is. Those hostales with a garage received a boost equivalent to two “rooms”. Ethical clearance was obtained from the University of Bath Social Sciences Research Ethics Committee (SSREC: S21-101). Results General findings A total of 114 hostales were identified in the surveyed area. Given that the survey was conducted on foot in a methodical manner, we believe this figure closely approximates the true number of hostales in the area. The hostales were categorised based on two criteria: size and style. Table 1 summarises the joint distribution of these variables. The first element of note is the density of hostales in the area. We found 114 in a 4000 m² sample of the city. Hostales are among the most common businesses in the area. Geographic distribution The main goal of this study was to plot the location of all hostales in the area. Figure 1 and 2 presents the position of the 114 identified hostales . Hostales can be found all over the map, but they tend to cluster in two areas. One is in the northwest, around Mall del Sur, and the other is in the true north, in front of the Ciudad de Dios Market. Their location on ample avenues or small streets varies depending on the specific sub-area; hostales in the southern part of the map tend to be located on large roads, while those in the northern part can be found everywhere. Hostal Size and Style We created four size categories based on the number of rooms visible from the outside. A Small hostal has between one to four visible rooms. A Medium one, between five and seven. A Large one between eight and thirteen. Finally, an Extra Large hostal has fourteen visible rooms or more. There are no standards on what a “large hostal ” is; however, this operationalisation has enough face validity to be serviceable as a first approach to the issue. Hostal Style, the second constructed variable, is more complex. Based on a mixture of architectural features and services provided we created a typology of hostales . We identified four types (See Figure 3): · Caleta : Caleta is a Peruvian slang term for hidden. These are made to be secret. They are identifiable as hostales because they have a discrete street sign, but not much else. This is done on purpose; they aim to attract clients looking for secrecy. · Business : These hostales are formal, elegant, and sober. They use dark or muted colours and do not list their services on the outside. Albeit they appear formal, they still offer services aimed at increasing the sexual pleasure of their clients’ visits; they just do not announce it openly. · Telo : Telo is slang for hostal . Telos are clearly a place for sex. They are very open about offering services of sexual nature, they show hearts and couples on their signs, and their names appeal to romance or lust. · Mixed : Business hostales and Telos could be seen as two opposite sides of a continuum. Mixed hostales sit somewhere between these two extremes. Regarding the hostal style, we can see that Caleta hostales are relatively uncommon. Specialised hostales , be it Telos and or Business hostales , are found in equal proportions (30%). Mixed hostales , compose most of the population (39%). Regarding the size of hostales , we found that large hostales , defined as those with eight to thirteen visible rooms, are the most common (35%). Medium and extra-large hostales each represent approximately 25% of the population. Small hostales are the least frequent category. Presence in Google Maps The third goal of this study was to determine the proportion of hostales that could be found in a secondary data source, in this case, Google Maps. More than half of the hostales are not found in the system. Table 2 shows the sample’s distribution of Google Status and Google Presence. It remains important to assess if any elements make a hostal more likely to appear on Google Maps than others. This will allow us to identify if there is a specific bias (for example, if Small hostales are more frequently absent). Table 3 summarises the distribution of the presence of hostales in Google by Style and Size. To ascertain if there is a relation between certain characteristics of an hostal and its presence in Google Maps, we estimate two logistic regression models. The outcome variable is the presence in Google Maps, and the covariates are a set of dummy variables for both Hostal Style and Hostal Size (See Table 4). We should still be mindful of two specific caveats. First, the samples arerelatively small for this type of analysis, which might make it prone to large proportional changes caused by small absolute frequency changes and to type II errors. For this reason, we chose to exclude the three cases of Caleta hostales in Hostal Style. Second, since our study includes the entire population of hostales in the area, the goal is to model the strength of the relationship (something akin to a measure of effect size) Two elements of note are present here. The first one is that Telos are more commonly found in Google Maps than the other Styles of hostales . This is particularly true in Business hostales , which show the least likelihood of being reported. The second element is that the odds of being present in Google Maps are higher for Large Hostales and Extra Large hostales . Next, we consider if any geographical characteristics are associated with being present in Google or not. For this, we have plotted the same map as before while adding information regarding Google Status and Presence (Figure 4 and 5). Upon examining the entire map, no marked trend is apparent. However, when analysing each cluster, there does appear to be a specific pattern. Those hostales in the True North cluster seem often absent from Google. At the same time, hostales in the Northwest cluster appear to be more present in Google, especially those in the Avenida Los Lirios (the main entrance to Mall del Sur). When delving deeper into the different Status, we see that not visible hostales can be found primarily in the Northwest cluster. Having the incorrect place in seems to be more common in those areas where hostales are not clustered together, such as the ones in the southern part of the map (See Figure 5). Discussion Quantities and distribution This study attempts to quantify the number of hostales in a Peruvian city, describe them, and assess their presence in Google Maps. Its results show the sizeable presence of these businesses in Lima. We found 114 in only 4 km 2 of the city. If they were evenly spaced, this would imply one hostal every 35 m 2 . Even though the surveyed area has certain features that made it attractive to explore (e.g., its relatively well-connected nature and the presence of two commercial centres), the fact is that it is far from unique. There are more than 37 shopping malls in the city and a much larger number of local markets 30 . These 4 km 2 are not special; a comparable concentration and distribution of hostales is likely to be found across most of the city. Our results also show that the number of hostales has increased considerably in the past 25 years. Previous research 9 found 100 hospitality businesses in all of SJM district; we have found more than that in less than one fifth of the district’s territory. Their ubiquitousness speaks of their popularity, and they would not be so popular unless they were used. The fact that they are so prominent in both number and capacity gives us an idea of how important they are in the sexual functioning of the city. These businesses are frequently full during the weekends, which would imply that, across the city, tens of thousands of people utilise hostales every Friday and Saturday to have sex. The fact that there are so many makes a sexual health policy centred around them potentially impactful. Hostales in the area tended to cluster around two retail centres, Mall del Sur and Ciudad de Dios market. Their location there is strategic. Being closer to these spaces increases their odds of getting clients after a night out, a date, or a shopping trip. Although they are more likely to be close to these commercial hubs, they can still be found all over the area, in both industrial and residential neighbourhoods. These businesses are often located on large avenues, where clients are more likely to walk by. Albeit a very small minority of hostales try to remain hidden, catering towards clients that require the utmost secrecy, the overwhelming majority are clearly out in the open, publicly offering their services in what appears to be a highly competitive sector. These quantities, distribution and location differ considerably from those of Brazilian motéis (the closest geographical and thematic point of comparison). Moteís tend to be large businesses located on the outskirts of cities, and are made to be accessible almost exclusively by car 11 . Hostales can be found all over the city, are much smaller, and cluster around areas of high pedestrian traffic. Google Maps as a source of data The evidence from this trial suggests that Google Maps data is not an accurate source of information regarding hostales in Lima. If Google Maps data had been used uncritically, it would have underestimated the number of hostales by half. Considering that the area we surveyed had commercial, industrial, and residential sections, the results are likely to be similar in other parts of the city. Still, more validation studies are required. Another issue present in the dataset is that not all sorts of hostales are as likely to be included. Large hostales and Telos were more likely to be included than other sizes and styles of hostales . If estimating the total number of hostales is proving to be problematic, estimating the distribution of hostales of specific characteristics will be even less accurate. Here is a good point to explain how a business is included in Google Maps. This can happen in two ways. The easiest one is to find the spot on the map, click on it, click on “Add a missing place”, fill in some information regarding the name and nature of the place, and it is done. Google will confirm the submission, and the business will be visible in the app. It is free, quick, and can be done by anyone with basic internet literacy. The other is slightly more complicated. This procedure is done by the owner or manager of the venue in question. They need to create a Google Business profile, register information on the business in question, and validate the profile. Again, this procedure is free and relatively simple; however, it requires a profile validation, which might take a few days. Based on the information available from the profiles in the area, it seems that most hostales mapped in Google Maps were registered by their clients, not by their owners. It would be expected that the more clients a business has, and the more technologically savvy they are, the higher the odds that a hostal will be found in the system. Telos, which probably have a faster flux of visitors, given their openly sexual nature, are understandably more easily found in the system. We should also consider that the percentage of hostales identified probably differs in other Peruvian cities where internet coverage is less reliable 31 . Using such a data source to study the distribution of hostales will likely produce even more unreliable results. However, there are still some positive points that we would like to mention. The first one is that having established a ratio of presence/absence of hostales in Google Maps, we could still use the existing data to estimate the actual number of hostales by assuming it is closer to twice what is registered. Such estimation should be considered a very rough approximation and only applicable to Lima. Nevertheless, it is vital, if one does this, to check before how many hostales were registered in Google Maps but were not found during the field survey. In other words, to identify, quantify, and account for the number of false cases of hostales in the system. Another positive aspect is that, even though half of the hostales are missing, their geographic distribution in Google Maps is similar enough to that in the field. This implies that the results of studies focused on the spatial distribution of hostales within the city might still provide usable, yet generic, results. A final point to consider is that Google Maps data seems to be growing in Lima. It might be that this is not an adequate source of information now, but it is likely to improve in years to come. Limitations and future research This study has a few elements that should be improved. Although 4 km 2 is not a small area, it seems trivial compared to Lima's full sprawling size (See Fig. 6 ). A longer and more extensive data collection using the same methodology could broaden the covered area and provide a more accurate measure hostales numbers and distribution. Both hostal classifiers (Size and Style) provide a serviceable first approach but still require further revision and calibration, especially the use of visible rooms as a proxy for size. Exploring other secondary data sources should also be considered. Since most hostales appear to have an operating business licence, administrative records could be a valuable data source. These licences must include the number of bookable rooms, which would provide a more reliable measure of hostal size and capacity. Still, the quality and extent of these records are unknown; they need to have their reliability studied. Conclusions Policy implications This study quantifies systematically, for the first time, the overwhelming presence of hostales in modern Lima. Their growing numbers speak volumes about their popularity and their importance in the sexual life of the city. The fact that Peruvians need these institutions at such a scale for such an essential need hints at several underlying phenomena. It speaks of the overcrowded living conditions around the city, of the necessity to keep sexual activity not only private but secret, and of what appears to be a growing commercial sex market, partially fuelled by the Venezuelan diaspora and the increasing numbers of people trafficking in the country 32 , 33 . These extremely common businesses concentrate large groups of people who are about to engage in sexual encounters. A condom distribution intervention anchored in hostales has shown very favourable results 8 , 34 , and these could reach a sizeable pool of beneficiaries. Secondary data sources Google Maps is a poor source of information for these businesses. The presence in Google Maps is not random; certain Sizes and Styles of hostales are more likely to appear than others. This makes it, at least now, an unsuitable source to identify their quantities in the city. We have established that the ubiquitousness of hostales could make them into a valuable vehicle for policy implementation. However, this insight would not have been possible purely through desk-based research. This study shows the importance of combining ground truthing, ethnographic and quantitative methods, as it uncovers nuances that other approaches might miss. Declarations Declaration of interest Funding details: IFV was received financial support from the University of Bath’s BRID fund (No grant number). Ethical approval: Ethical clearance was obtained from the University of Bath Research Ethics Committee (SSREC: S21-101) Consent to participate: There are no human participants on this study. Disclosure of interest: No potential competing interest to declare. Availability of data and material: At request. Currently stored at https://researchdata.bath.ac.uk/id/eprint/1581 Code availability: At request. References INEI. 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Egger M, Pauw J, Lopatatzidis A, Medrano D, Paccaud F, Smith GD. Promotion of condom use in a high-risk setting in Nicaragua: a randomised controlled trial. The Lancet . 2000;355(9221):2101-2105. doi:10.1016/S0140-6736(00)02376-X Google. Metropolitan Lima. Google Maps. (lon = -76.97194012615122, lat = -12.161490701886304) INEI. Caracteristicas demográficas. In: Lima Metropolitana: Perfil Socio-Demográfico . Instituto Nacional de Estadística e Informática; 1993. Tables Table 1. Distribution of hostales by Style and Size. The percentages are calculated based on the total sample. Style / Size Small Medium Large Extra large Total Caleta 1 1 1 - 3 1% 1% 1% - 3% Telo 4 8 12 7 31 4% 7% 11% 6% 27% Mixed 10 15 15 5 45 9% 13% 13% 4% 39% Business - 5 12 18 35 - 4% 11% 16% 31% Total 15 29 40 30 114 13% 25% 35% 26% 100% Table 2. Table summarising the Presence and Status of the surveyed hostales in Google Maps. Presence Status No 63 (55%) No 56 (49%) Wrong place 6 (5%) Wrong name 1 (1%) Yes 51 (45%) Not visible 8 (7%) Yes 43 (38%) Total 114 (100%) Table 3. Presence of hostales in Google Maps by Hostal Style and Size Presence Hostal Style Hostal Size Total Telo Mixed Business Caleta Small Medium Large Extra large No 13 26 22 2 10 20 17 16 63 42% 58% 63% 67% 67% 69% 43% 53% 55% Yes 18 19 13 1 5 9 23 14 51 58% 42% 37% 33% 33% 31% 58% 47% 45% Total 31 45 35 3 15 29 40 30 114 Table 4. Style and Size Models for Presence in Google Maps. Presence in Google Maps Models Style Model Size Model Style (Ref: Telo) Mixed -0.639 (0.473) Business -0.852 * (0.505) Size (Ref: Small) Medium -0.105 (0.679) Large 0.995 (0.634) Extra Large 0.560 (0.659) Constant 0.325 (0.364) -0.693 (0.548) Observations 111 114 Log Likelihood -74.817 -75.512 Akaike Inf. Crit. 155.635 159.023 Note: * p<0.1; ** p<0.05; *** p<0.01 The Caleta style was removed from the sample in the Style model. 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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-7311058","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498857415,"identity":"a485afa9-e385-472c-99c3-b429b79e0043","order_by":0,"name":"Ignacio Franco Vega","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABJUlEQVRIiWNgGAWjYBACNjBpwMDA3oAQZHwAJHj4CGnhOYAQZDYAaWEjZB2yFjYJhGmYgI9/7cEPPwpsGHikDz978KOCIbGff/Gzyq85djJsDMwPH93A4jCJd8mSPQZpQKenmRv2nGFInDnjmdlt2W3JQIexGRvnYNNyxkCaweAwgz0Pg5k0YxuDscGNA2a3JbcxA7XwsElj12L8G6SFh4f9G1iL/Y3j34olt9Xj1sLfYyYN0cIDtkXOACjC+HHbYTy28JhZAv0C1MFTJtlzRkJO4gZPsTTjtuM8bMzY/SLff8b4xo8/NnJAh22T+FFhw8Pff3zjx5/bqu352ZsfPsaihUEiAUzxwLhgEWYwlxmLchDgP4BFhPEHDtWjYBSMglEwIgEAlGhSrOV8PgIAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-3023-8485","institution":"University of Bath Department of Social and Policy Sciences","correspondingAuthor":true,"prefix":"","firstName":"Ignacio","middleName":"Franco","lastName":"Vega","suffix":""},{"id":498857416,"identity":"c8c1fb0e-bc95-4e47-b266-cd1337e17e3d","order_by":1,"name":"Melanie Channon","email":"","orcid":"","institution":"University of Bath Department of Social and Policy Sciences","correspondingAuthor":false,"prefix":"","firstName":"Melanie","middleName":"","lastName":"Channon","suffix":""},{"id":498857417,"identity":"2d316332-308c-49b3-9c8a-55c5ed464664","order_by":2,"name":"Eleonora Fichera","email":"","orcid":"","institution":"University of Bath Department of Economics","correspondingAuthor":false,"prefix":"","firstName":"Eleonora","middleName":"","lastName":"Fichera","suffix":""}],"badges":[],"createdAt":"2025-08-06 15:00:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7311058/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7311058/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89389160,"identity":"0db96b87-56d0-46aa-a636-ac73bb3356ec","added_by":"auto","created_at":"2025-08-19 12:47:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4656460,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLocation of the 114 surveyed hostales.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7311058/v1/29250aa50b0ad1bb0290579f.png"},{"id":89391065,"identity":"b7038ef9-646f-494e-9742-0ddcc5042166","added_by":"auto","created_at":"2025-08-19 13:03:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4568508,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeat map of hostal concentration in the surveyed area. \u003c/strong\u003eThe area could be divided into three sections. The northwest cluster, the true north cluster and the southern half of the map, where hostales less markedly grouped.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7311058/v1/6f028a10d770442e4e8ac44a.png"},{"id":89389167,"identity":"65406554-9fc4-4870-9433-c1b171c98654","added_by":"auto","created_at":"2025-08-19 12:47:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1876408,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExamples of hostales.\u003c/strong\u003e \u003cstrong\u003eLeft\u003c/strong\u003e: Caleta hostal. No sign is visible except for the very small one next to the door. Nothing but an “H” is visible on it. \u003cstrong\u003eCentre-Left\u003c/strong\u003e: Business hostal. Very sober colours (for Perú). No ads with services or prices. \u003cstrong\u003eCentre\u003c/strong\u003e-\u003cstrong\u003eRight\u003c/strong\u003e: Telo. The hostal named “Privacy” shows a sign with a loving couple and proudly announces the presence of porn videos on the premises. \u003cstrong\u003eRight\u003c/strong\u003e: Mixed hostal. Primavera hostal shows its prices and some of its services; none of them is openly sexual, but the presence of a jacuzzi hints at the possibility of a licentious night.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7311058/v1/6a2a11c59cbc6ab7e53ce44f.png"},{"id":89391064,"identity":"64ef8662-f753-4ff0-a397-58dde3206545","added_by":"auto","created_at":"2025-08-19 13:03:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5055202,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHostales by their Presence in Google Maps\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7311058/v1/87443026d98bbfaae70ab00f.png"},{"id":89389171,"identity":"75d48d6a-7ad7-4bcd-928c-34f3a38ff666","added_by":"auto","created_at":"2025-08-19 12:47:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5039894,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHostales by their Status on Google Maps.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7311058/v1/4275d7036e15cc938c950843.png"},{"id":89389161,"identity":"66af955c-9e81-4d61-9eb5-9cc7fdef8ad0","added_by":"auto","created_at":"2025-08-19 12:47:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":912167,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of the Metropolitan Lima\u003c/strong\u003e \u003csup\u003e35\u003c/sup\u003e. Lima is a large city; the conurbation of Lima city and the Callao constitutional province occupied 2819 km\u003csup\u003e2\u003c/sup\u003e thirty years ago\u003csup\u003e 36. \u003c/sup\u003eNo more recent data regarding the city size is available, but it has grown considerably since then. The small section shadowed in purple is the surveyed quadrant.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7311058/v1/709343cb0f5e900f2d23da0c.png"},{"id":89704080,"identity":"db0f8f83-e765-4ef1-b16e-3421d8a4c048","added_by":"auto","created_at":"2025-08-22 22:18:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":27675680,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7311058/v1/f87e72ee-40ba-4364-9482-f265e8def068.pdf"}],"financialInterests":"","formattedTitle":"Georeferencing and classifying love hotels in Lima:\n\nA comparison between field research and Google Maps","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAbout 52% of pregnancies in Perú are unintended\u003csup\u003e1\u003c/sup\u003e. These pregnancies have a substantial impact on the well-being of those involved\u003csup\u003e2,3\u003c/sup\u003e. There is little information on the prevalence of sexually transmitted infections (STIs) at a national level, but the rates appear stagnant at best\u003csup\u003e4\u003c/sup\u003e. Neither unintended pregnancies nor STIs are distributed homogeneously in the population. Unintended pregnancies are considerably more frequent in disadvantaged households. At the same time, STIs are more common in men who have sex with men and sex workers\u003csup\u003e4\u003c/sup\u003e, groups frequently discriminated against, both by the general public and governmental bodies\u003csup\u003e6,7\u003c/sup\u003e. Under these conditions, inadequate access to sexual health services operates as a mechanism that reinforces the cycle of poverty. New strategies are needed to face these issues. Field experiments in \u003cem\u003ehostales\u003c/em\u003e in Lima have proven that a condom distribution policy anchored in them can double the odds of condom use\u003csup\u003e8\u003c/sup\u003e. We need to learn more about these institutions to assess their role as potential settings for policy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNow, what exactly are \u003cem\u003ehostales\u003c/em\u003e? Aside from the work carried out by Ccopa in the mid-90s\u003csup\u003e9\u003c/sup\u003e, there is little systematised information about them. We know they are short-term lodgings where couples go to have sex. This is evident in the venues’ names, street signs, and services. \u003cem\u003eHostales\u003c/em\u003e advertise different amenities; some are common to other accommodations (e.g., hot water, cable TV, Wi-Fi). In contrast, others cater to more erotic interests (e.g., XXX videos, Tantra chairs) or secrecy (e.g., hidden entrances, receipts under fake names). In this regard, they are very similar to Japanese Love hotels\u003csup\u003e10\u003c/sup\u003e or Brazilian \u003cem\u003emotéis\u003c/em\u003e\u003csup\u003e11\u003c/sup\u003e.\u0026nbsp;They differ considerably in size, ranging from some with only a couple of very basic rooms to those with dozens of theme-decorated rooms. \u003cem\u003eHostales\u003c/em\u003e can be found all over the city in both affluent and resource-deprived areas. They can be located in commercial, residential, and even industrial areas. Room prices vary depending on the quality of the room, the services it includes, and the area where the \u003cem\u003ehostal\u003c/em\u003e is located.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt is important to note that while \u003cem\u003ehostales\u003c/em\u003e may be used by sex workers and their clients, they are not brothels. These establishments provide a space for such activities but rarely facilitate them. Sex work is not illegal in the country, however, promoting or profiting from another person’s sex work (procuring) is a criminal offense\u003csup\u003e12\u003c/sup\u003e. As a result, \u003cem\u003ehostal\u003c/em\u003e owners are careful to avoid any involvement in such activities.\u003c/p\u003e\n\u003cp\u003eThese businesses have the potential to contribute to the development of sexual health policies. As the settings where sexual encounters occur, they minimise the distance between the point of intervention and the act itself. Due to this, some of the information processing and planning biases that increase risky sexual behaviour can be avoided\u003csup\u003e13–15\u003c/sup\u003e. Additionally, as they are private spaces, the inhibiting effect of social emotions such as shame or guilt is lessened\u003csup\u003e16\u003c/sup\u003e. Finally, they are spaces frequented by various high-risk groups, especially sex workers. Understanding them better is necessary to properly design a policy around them.\u003c/p\u003e\n\u003cp\u003eOne of the first steps to understanding them is to establish how many of them there are. Anyone who pays attention to the capital’s landscape is going to notice their predominance. They appear to be everywhere, but despite their abundance, they remain thoroughly understudied and ignored by policymakers, appearing in none of Peru’s sexual and reproductive health policies.\u0026nbsp;Ccopa is the only researcher that has attempted to quantify them before. Using administrative records from the Ministry of Tourism, he discovered that Lima had 1965 hospitality businesses in 1998; 100 of which were in the San Juan de Miraflores district\u003csup\u003e9\u003c/sup\u003e. As a point of comparison there are around 5000 motels in Brazil\u003csup\u003e11\u003c/sup\u003e (a country with a population six times larger to that of Perú).\u003c/p\u003e\n\u003cp\u003eThe primary objective of this study is to understand the spatial distribution and visual characteristics of \u003cem\u003ehostales\u003c/em\u003e by conducting a census of their locations and the visual features and symbols that define them within a specific geographical area. To achieve this, the study addresses the following specific research questions:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;\u0026nbsp;What are the locations and visual characteristics of hostales?\u003c/p\u003e\n\u003cp\u003eo Can hostales be classified into distinct visual styles based on their visual characteristics (e.g., signs, façades, symbols)?\u003c/p\u003e\n\u003cp\u003eo What ideas and values are communicated through these visual styles and what do they say about their role in the city’s social, cultural, and economic life?\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;\u0026nbsp;How does the prevalence of specific types or styles of hostales vary across different areas within the study region?\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;\u0026nbsp;How reliable is Google Maps as a secondary data source for identifying the distribution and types of hostales?\u003c/p\u003e\n\u003cp\u003eo Are certain types or styles of hostales more likely to be excluded from Google Maps and what factors might influence these omissions?\u003c/p\u003e\n\u003cp\u003eIf Google Maps proves to be a serviceable source of information, further studies could be conducted with their data. This is especially important as remote research is increasingly becoming the norm and assessing the criterion validity of its findings is proving to be an essential measure in ensuring their quality\u003csup\u003e17\u003c/sup\u003e, especially in non-WEIRD countries (Western, Educated, Industrialised, Rich and Democratic)\u003csup\u003e18,19\u003c/sup\u003e .\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003eSite\u003c/h2\u003e\n\u003cp\u003eWe surveyed the 4 km\u003csup\u003e2\u003c/sup\u003e quadrant limited by Avenidas Los Héroes, Víctor Castro Iglesias, Miguel Iglesias and Panamerica Sur. The entire section is in the San Juan de Miraflores district (SJM) in the southern cone of Lima. Most households in SJM are lower-middle income households. The surveyed section, however, was comprised of commercial and industrial areas and upper-middle income households\u0026nbsp;\u003csup\u003e20\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe chosen area has a few noteworthy aspects. First, it includes residential, commercial, industrial and mixed-use areas. Second, it includes a series of important landmarks such as Mall del Sur (one of the largest malls in Lima’s southern cone), the Maria Auxiliadora hospital (one of the largest in the city), the Atocongo bus terminal (one of the most frequently used entrances to Lima), the offices of the municipal government, one large and two midsize markets, and two technical/pedagogical colleges. Third, it is very well-connected to the rest of the city.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eData collection technique\u003c/h2\u003e\n\u003cp\u003eWe surveyed every street in the quadrant. When encountering a \u003cem\u003ehostal, hostel, alojamiento\u003c/em\u003e or \u003cem\u003ehotel\u003c/em\u003e, we took a series of georeferenced pictures of its façade and street sign. We systematically surveyed the area until we were confident that we had identified every \u003cem\u003ehostal.\u003c/em\u003e This took a total of eight days during the period between December 2021 and March 2022.\u003c/p\u003e\n\u003ch2 id=\"_Toc154046750\"\u003eData processing and analysis\u003c/h2\u003e\n\u003cp\u003eThe data was processed in several steps:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;\u0026nbsp;We created a dataset with the name of each \u003cem\u003ehostal\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;\u0026nbsp;We extracted the GPS coordinates from the pictures’ metadata and added them to the dataset using ExifTool\u003csup\u003e21\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;\u0026nbsp;We examined each picture and filled in additional fields in the dataset, such as advertised services and building size.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp;\u0026nbsp;We conducted a thorough manual search for each \u003cem\u003ehostal\u003c/em\u003e in Google Maps and determined if it was included in their database. In June 2022.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5.\u0026nbsp; \u0026nbsp;\u0026nbsp;We established two measures of Google presence:\u003c/p\u003e\n\u003cp\u003ea.\u0026nbsp; \u0026nbsp;\u0026nbsp;A detailed version (Google Status):\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003eYes\u003c/strong\u003e: The \u003cem\u003ehostal\u003c/em\u003e can be found in the map in Google Maps in the correct location.\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003eWrong name\u003c/strong\u003e: There is a \u003cem\u003ehostal\u003c/em\u003e under the wrong name in the same spot on the map.\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003eWrong location\u003c/strong\u003e: The \u003cem\u003ehostal\u003c/em\u003e appears on the map; however, its location is substantially incorrect.\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003eNot visible\u003c/strong\u003e: The \u003cem\u003ehostal\u003c/em\u003e is in Google Maps and in the correct location; however, it cannot be seen on the map; it is only found by writing the name in the search bar or by clicking on the building and looking for it. This is because when more than one business is located in the same building, the Google interface only shows one of them.\u003c/p\u003e\n\u003cp\u003e· \u003cstrong\u003eNo\u003c/strong\u003e: The \u003cem\u003ehostal\u003c/em\u003e cannot be found on Google Maps.\u003c/p\u003e\n\u003cp\u003eb.\u0026nbsp; \u0026nbsp;\u0026nbsp;A simplified, binary version of this same Status (Google Presence).\u003c/p\u003e\n\u003cp\u003e1. \u003cstrong\u003eYes\u003c/strong\u003e: Yes + Not visible\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eNo\u003c/strong\u003e: No + Wrong location + Wrong name\u003c/p\u003e\n\u003cp\u003e6.\u0026nbsp; \u0026nbsp;\u0026nbsp;We imported the database created into R.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e7.\u0026nbsp; \u0026nbsp;\u0026nbsp;We constructed a map that would serve for the presentation of the geographical information. We personalised the Google Maps output using Google Maps Styling Wizard, removed undesired information to declutter the map that would become the canvas of our thematic maps (e.g., street, block and business names), and exported the code for the personalised map using JavaScript Object Notation (JSON).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e8.\u0026nbsp; \u0026nbsp;\u0026nbsp;We imported this information into R using the RJSONIO and maggittr\u003csup\u003e22,23\u003c/sup\u003e and used the googleway R\u003csup\u003e24\u003c/sup\u003e and the Maps Static API from Google Cloud Platform to draw a map of the area.\u003c/p\u003e\n\u003cp\u003e9.\u0026nbsp; \u0026nbsp;\u0026nbsp;We plotted the location of the georeferenced \u003cem\u003ehostales\u003c/em\u003e in the constructed canvas using ggmap\u0026nbsp;\u003csup\u003e25\u003c/sup\u003e and optimised its visualisations with ggsci, Rcolorbrewer and scales\u003csup\u003e26–28\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e10.\u0026nbsp;We modelled Google Presence to identify if a particular \u003cem\u003ehostal\u003c/em\u003e characteristic made it less likely to be included in Google Maps with R base and Stargazer\u003csup\u003e29\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe created two variables to further classify \u003cem\u003ehostales\u003c/em\u003e. The first variable is \u003cem\u003eHostal\u003c/em\u003e Size. We built a variable based on the number of rooms visible from the outside and the presence or absence of a garage. The logic is simple; the more visible rooms a \u003cem\u003ehostal\u003c/em\u003e has, the larger it is. Those \u003cem\u003ehostales\u003c/em\u003e with a garage received a boost equivalent to two “rooms”.\u003c/p\u003e\n\u003cp\u003eEthical clearance was obtained from the University of Bath Social Sciences Research Ethics Committee (SSREC: S21-101).\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eGeneral findings\u003c/h2\u003e\n\u003cp\u003eA total of 114 \u003cem\u003ehostales\u003c/em\u003e were identified in the surveyed area. Given that the survey was conducted on foot in a methodical manner, we believe this figure closely approximates the true number of \u003cem\u003ehostales\u003c/em\u003e in the area. The \u003cem\u003ehostales\u003c/em\u003e were categorised based on two criteria: size and style. Table 1 summarises the joint distribution of these variables.\u003c/p\u003e\n\u003cp\u003eThe first element of note is the density of \u003cem\u003ehostales\u003c/em\u003e in the area. We found 114 in a 4000 m\u0026sup2; sample of the city. \u003cem\u003eHostales\u003c/em\u003e are among the most common businesses in the area.\u003c/p\u003e\n\u003cp id=\"_Toc154046768\"\u003e\u003cstrong\u003eGeographic distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe main goal of this study was to plot the location of all \u003cem\u003ehostales\u003c/em\u003e in the area. Figure 1 and 2 presents the position of the 114 identified \u003cem\u003ehostales\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHostales\u003c/em\u003e can be found all over the map, but they tend to cluster in two areas. One is in the northwest, around Mall del Sur, and the other is in the true north, in front of the Ciudad de Dios Market. Their location on ample avenues or small streets varies depending on the specific sub-area; \u003cem\u003ehostales\u003c/em\u003e in the southern part of the map tend to be located on large roads, while those in the northern part can be found everywhere.\u003c/p\u003e\n\u003ch2\u003eHostal Size and Style\u003c/h2\u003e\n\u003cp\u003eWe created four size categories based on the number of rooms visible from the outside. A Small \u003cem\u003ehostal\u003c/em\u003e has between one to four visible rooms. A Medium one, between five and seven. A Large one between eight and thirteen. Finally, an Extra Large \u003cem\u003ehostal\u003c/em\u003e has fourteen visible rooms or more. There are no standards on what a \u0026ldquo;large \u003cem\u003ehostal\u003c/em\u003e\u0026rdquo; is; however, this operationalisation has enough face validity to be serviceable as a first approach to the issue.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHostal\u003c/em\u003e Style, the second constructed variable, is more complex. Based on a mixture of architectural features and services provided we created a typology of \u003cem\u003ehostales\u003c/em\u003e. We identified four types (See Figure 3):\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026middot; Caleta\u003c/strong\u003e: \u003cem\u003eCaleta\u0026nbsp;\u003c/em\u003eis a Peruvian slang term for hidden. These are made to be secret. They are identifiable as \u003cem\u003ehostales\u003c/em\u003e because they have a discrete street sign, but not much else. This is done on purpose; they aim to attract clients looking for secrecy.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eBusiness\u003c/strong\u003e: These \u003cem\u003ehostales\u003c/em\u003e are formal, elegant, and sober. They use dark or muted colours and do not list their services on the outside. Albeit they appear formal, they still offer services aimed at increasing the sexual pleasure of their clients\u0026rsquo; visits; they just do not announce it openly.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eTelo\u003c/strong\u003e: \u003cem\u003eTelo\u0026nbsp;\u003c/em\u003eis slang for \u003cem\u003ehostal\u003c/em\u003e. Telos are clearly a place for sex. They are very open about offering services of sexual nature, they show hearts and couples on their signs, and their names appeal to romance or lust.\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eMixed\u003c/strong\u003e: Business \u003cem\u003ehostales\u003c/em\u003e and Telos could be seen as two opposite sides of a continuum. Mixed \u003cem\u003ehostales\u003c/em\u003e sit somewhere between these two extremes.\u003c/p\u003e\n\u003cp\u003eRegarding the \u003cem\u003ehostal\u003c/em\u003e style, we can see that Caleta \u003cem\u003ehostales\u003c/em\u003e are relatively uncommon. Specialised \u003cem\u003ehostales\u003c/em\u003e, be it Telos and or Business\u003cem\u003e\u0026nbsp;hostales\u003c/em\u003e, are found in equal proportions (30%). Mixed \u003cem\u003ehostales\u003c/em\u003e, compose most of the population (39%).\u003c/p\u003e\n\u003cp\u003eRegarding the size of \u003cem\u003ehostales\u003c/em\u003e, we found that large \u003cem\u003ehostales\u003c/em\u003e, defined as those with eight to thirteen visible rooms, are the most common (35%). Medium and extra-large \u003cem\u003ehostales\u003c/em\u003e each represent approximately 25% of the population. Small \u003cem\u003ehostales\u003c/em\u003e are the least frequent category.\u003c/p\u003e\n\u003cp id=\"_Toc154046769\"\u003e\u003cstrong\u003ePresence in Google Maps\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe third goal of this study was to determine the proportion of \u003cem\u003ehostales\u003c/em\u003e that could be found in a secondary data source, in this case, Google Maps. More than half of the \u003cem\u003ehostales\u003c/em\u003e are not found in the system. Table 2 shows the sample\u0026rsquo;s distribution of Google Status and Google Presence.\u003c/p\u003e\n\u003cp\u003eIt remains important to assess if any elements make a \u003cem\u003ehostal\u003c/em\u003e more likely to appear on Google Maps than others. This will allow us to identify if there is a specific bias (for example, if Small \u003cem\u003ehostales\u003c/em\u003e are more frequently absent). Table 3 summarises the distribution of the presence of \u003cem\u003ehostales\u003c/em\u003e in Google by Style and Size.\u003c/p\u003e\n\u003cp\u003eTo ascertain if there is a relation between certain characteristics of an \u003cem\u003ehostal\u003c/em\u003e and its presence in Google Maps, we estimate two logistic regression models. The outcome variable is the presence in Google Maps, and the covariates are a set of dummy variables for both \u003cem\u003eHostal\u003c/em\u003e Style and \u003cem\u003eHostal\u003c/em\u003e Size (See Table 4). We should still be mindful of two specific caveats. First, the samples arerelatively small for this type of analysis, which might make it prone to large proportional changes caused by small absolute frequency changes and to type II errors. For this reason, we chose to exclude the three cases of Caleta \u003cem\u003ehostales\u003c/em\u003e in \u003cem\u003eHostal\u003c/em\u003e Style. Second, since our study includes the entire population of \u003cem\u003ehostales\u003c/em\u003e in the area, the goal is to model the strength of the relationship (something akin to a measure of effect size)\u003c/p\u003e\n\u003cp\u003eTwo elements of note are present here. The first one is that Telos are more commonly found in Google Maps than the other Styles of \u003cem\u003ehostales\u003c/em\u003e. This is particularly true in Business \u003cem\u003ehostales\u003c/em\u003e, which show the least likelihood of being reported. The second element is that the odds of being present in Google Maps are higher for Large \u003cem\u003eHostales\u003c/em\u003e and Extra Large \u003cem\u003ehostales\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eNext, we consider if any geographical characteristics are associated with being present in Google or not. For this, we have plotted the same map as before while adding information regarding Google Status and Presence (Figure 4 and 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUpon examining the entire map, no marked trend is apparent. However, when analysing each cluster, there does appear to be a specific pattern. Those \u003cem\u003ehostales\u003c/em\u003e in the True North cluster seem often absent from Google. At the same time, \u003cem\u003ehostales\u003c/em\u003e in the Northwest cluster appear to be more present in Google, especially those in the Avenida Los Lirios (the main entrance to Mall del Sur).\u003c/p\u003e\n\u003cp\u003eWhen delving deeper into the different Status, we see that not visible \u003cem\u003ehostales\u003c/em\u003e can be found primarily in the Northwest cluster. Having the incorrect place in seems to be more common in those areas where \u003cem\u003ehostales\u003c/em\u003e are not clustered together, such as the ones in the southern part of the map (See Figure 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eQuantities and distribution\u003c/h2\u003e\u003cp\u003eThis study attempts to quantify the number of \u003cem\u003ehostales\u003c/em\u003e in a Peruvian city, describe them, and assess their presence in Google Maps. Its results show the sizeable presence of these businesses in Lima. We found 114 in only 4 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e of the city. If they were evenly spaced, this would imply one \u003cem\u003ehostal\u003c/em\u003e every 35 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Even though the surveyed area has certain features that made it attractive to explore (e.g., its relatively well-connected nature and the presence of two commercial centres), the fact is that it is far from unique. There are more than 37 shopping malls in the city and a much larger number of local markets\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. These 4 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e are not special; a comparable concentration and distribution of \u003cem\u003ehostales\u003c/em\u003e is likely to be found across most of the city. Our results also show that the number of \u003cem\u003ehostales\u003c/em\u003e has increased considerably in the past 25 years. Previous research\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e found 100 hospitality businesses in all of SJM district; we have found more than that in less than one fifth of the district\u0026rsquo;s territory.\u003c/p\u003e\u003cp\u003eTheir ubiquitousness speaks of their popularity, and they would not be so popular unless they were used. The fact that they are so prominent in both number and capacity gives us an idea of how important they are in the sexual functioning of the city. These businesses are frequently full during the weekends, which would imply that, across the city, tens of thousands of people utilise \u003cem\u003ehostales\u003c/em\u003e every Friday and Saturday to have sex. The fact that there are so many makes a sexual health policy centred around them potentially impactful.\u003c/p\u003e\u003cp\u003e\u003cem\u003eHostales\u003c/em\u003e in the area tended to cluster around two retail centres, Mall del Sur and Ciudad de Dios market. Their location there is strategic. Being closer to these spaces increases their odds of getting clients after a night out, a date, or a shopping trip. Although they are more likely to be close to these commercial hubs, they can still be found all over the area, in both industrial and residential neighbourhoods. These businesses are often located on large avenues, where clients are more likely to walk by. Albeit a very small minority of \u003cem\u003ehostales\u003c/em\u003e try to remain hidden, catering towards clients that require the utmost secrecy, the overwhelming majority are clearly out in the open, publicly offering their services in what appears to be a highly competitive sector.\u003c/p\u003e\u003cp\u003eThese quantities, distribution and location differ considerably from those of Brazilian \u003cem\u003emot\u0026eacute;is\u003c/em\u003e (the closest geographical and thematic point of comparison). \u003cem\u003eMote\u0026iacute;s\u003c/em\u003e tend to be large businesses located on the outskirts of cities, and are made to be accessible almost exclusively by car\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eHostales\u003c/em\u003e can be found all over the city, are much smaller, and cluster around areas of high pedestrian traffic.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eGoogle Maps as a source of data\u003c/h2\u003e\u003cp\u003eThe evidence from this trial suggests that Google Maps data is not an accurate source of information regarding \u003cem\u003ehostales\u003c/em\u003e in Lima. If Google Maps data had been used uncritically, it would have underestimated the number of \u003cem\u003ehostales\u003c/em\u003e by half. Considering that the area we surveyed had commercial, industrial, and residential sections, the results are likely to be similar in other parts of the city. Still, more validation studies are required.\u003c/p\u003e\u003cp\u003eAnother issue present in the dataset is that not all sorts of \u003cem\u003ehostales\u003c/em\u003e are as likely to be included. Large \u003cem\u003ehostales\u003c/em\u003e and Telos were more likely to be included than other sizes and styles of \u003cem\u003ehostales\u003c/em\u003e. If estimating the total number of \u003cem\u003ehostales\u003c/em\u003e is proving to be problematic, estimating the distribution of \u003cem\u003ehostales\u003c/em\u003e of specific characteristics will be even less accurate.\u003c/p\u003e\u003cp\u003eHere is a good point to explain how a business is included in Google Maps. This can happen in two ways. The easiest one is to find the spot on the map, click on it, click on \u0026ldquo;Add a missing place\u0026rdquo;, fill in some information regarding the name and nature of the place, and it is done. Google will confirm the submission, and the business will be visible in the app. It is free, quick, and can be done by anyone with basic internet literacy. The other is slightly more complicated. This procedure is done by the owner or manager of the venue in question. They need to create a Google Business profile, register information on the business in question, and validate the profile. Again, this procedure is free and relatively simple; however, it requires a profile validation, which might take a few days.\u003c/p\u003e\u003cp\u003eBased on the information available from the profiles in the area, it seems that most \u003cem\u003ehostales\u003c/em\u003e mapped in Google Maps were registered by their clients, not by their owners. It would be expected that the more clients a business has, and the more technologically savvy they are, the higher the odds that a \u003cem\u003ehostal\u003c/em\u003e will be found in the system. Telos, which probably have a faster flux of visitors, given their openly sexual nature, are understandably more easily found in the system.\u003c/p\u003e\u003cp\u003eWe should also consider that the percentage of \u003cem\u003ehostales\u003c/em\u003e identified probably differs in other Peruvian cities where internet coverage is less reliable\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Using such a data source to study the distribution of \u003cem\u003ehostales\u003c/em\u003e will likely produce even more unreliable results.\u003c/p\u003e\u003cp\u003eHowever, there are still some positive points that we would like to mention. The first one is that having established a ratio of presence/absence of \u003cem\u003ehostales\u003c/em\u003e in Google Maps, we could still use the existing data to estimate the actual number of \u003cem\u003ehostales\u003c/em\u003e by assuming it is closer to twice what is registered. Such estimation should be considered a very rough approximation and only applicable to Lima. Nevertheless, it is vital, if one does this, to check before how many \u003cem\u003ehostales\u003c/em\u003e were registered in Google Maps but were not found during the field survey. In other words, to identify, quantify, and account for the number of false cases of \u003cem\u003ehostales\u003c/em\u003e in the system.\u003c/p\u003e\u003cp\u003eAnother positive aspect is that, even though half of the \u003cem\u003ehostales\u003c/em\u003e are missing, their geographic distribution in Google Maps is similar enough to that in the field. This implies that the results of studies focused on the spatial distribution of \u003cem\u003ehostales\u003c/em\u003e within the city might still provide usable, yet generic, results.\u003c/p\u003e\u003cp\u003eA final point to consider is that Google Maps data seems to be growing in Lima. It might be that this is not an adequate source of information now, but it is likely to improve in years to come.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and future research\u003c/h2\u003e\u003cp\u003eThis study has a few elements that should be improved. Although 4 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e is not a small area, it seems trivial compared to Lima's full sprawling size (See Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). A longer and more extensive data collection using the same methodology could broaden the covered area and provide a more accurate measure \u003cem\u003ehostales\u003c/em\u003e numbers and distribution. Both \u003cem\u003ehostal\u003c/em\u003e classifiers (Size and Style) provide a serviceable first approach but still require further revision and calibration, especially the use of visible rooms as a proxy for size.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eExploring other secondary data sources should also be considered. Since most \u003cem\u003ehostales\u003c/em\u003e appear to have an operating business licence, administrative records could be a valuable data source. These licences must include the number of bookable rooms, which would provide a more reliable measure of \u003cem\u003ehostal\u003c/em\u003e size and capacity. Still, the quality and extent of these records are unknown; they need to have their reliability studied.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003ePolicy implications\u003c/h2\u003e\u003cp\u003eThis study quantifies systematically, for the first time, the overwhelming presence of \u003cem\u003ehostales\u003c/em\u003e in modern Lima. Their growing numbers speak volumes about their popularity and their importance in the sexual life of the city. The fact that Peruvians need these institutions at such a scale for such an essential need hints at several underlying phenomena. It speaks of the overcrowded living conditions around the city, of the necessity to keep sexual activity not only private but secret, and of what appears to be a growing commercial sex market, partially fuelled by the Venezuelan diaspora and the increasing numbers of people trafficking in the country\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThese extremely common businesses concentrate large groups of people who are about to engage in sexual encounters. A condom distribution intervention anchored in \u003cem\u003ehostales\u003c/em\u003e has shown very favourable results\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and these could reach a sizeable pool of beneficiaries.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eSecondary data sources\u003c/h2\u003e\u003cp\u003eGoogle Maps is a poor source of information for these businesses. The presence in Google Maps is not random; certain Sizes and Styles of \u003cem\u003ehostales\u003c/em\u003e are more likely to appear than others. This makes it, at least now, an unsuitable source to identify their quantities in the city.\u003c/p\u003e\u003cp\u003eWe have established that the ubiquitousness of \u003cem\u003ehostales\u003c/em\u003e could make them into a valuable vehicle for policy implementation. However, this insight would not have been possible purely through desk-based research. This study shows the importance of combining ground truthing, ethnographic and quantitative methods, as it uncovers nuances that other approaches might miss.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eDeclaration of interest\u003c/p\u003e\n\u003cp\u003eFunding details: IFV was received financial support from the University of Bath\u0026rsquo;s BRID fund (No grant number).\u003c/p\u003e\n\u003cp\u003eEthical approval: Ethical clearance was obtained from the University of Bath Research Ethics Committee (SSREC: S21-101)\u003c/p\u003e\n\u003cp\u003eConsent to participate: There are no human participants on this study.\u003c/p\u003e\n\u003cp\u003eDisclosure of interest: No potential competing interest to declare.\u003c/p\u003e\n\u003cp\u003eAvailability of data and material: At request. Currently stored at https://researchdata.bath.ac.uk/id/eprint/1581\u003c/p\u003e\n\u003cp\u003eCode availability: At request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eINEI. \u003cem\u003ePer\u0026uacute;: Encuesta Demogr\u0026aacute;fica y de Salud Familiar 2023 Nacional y Departamental\u003c/em\u003e. Instituto Nacional de Estad\u0026iacute;stica e Inform\u0026aacute;tica; 2024.\u003c/li\u003e\n \u003cli\u003eAlc\u0026aacute;zar L. Asistencia y Deserci\u0026oacute;n en Escuelas Secundarias Rurales del Per\u0026uacute;. \u003cem\u003eREICE Rev Iberoam Sobre Calid Efic Cambio En Educ\u003c/em\u003e. 2016;7(4). doi:10.15366/reice2009.7.4.007\u003c/li\u003e\n \u003cli\u003eBatyra E. Increasing Educational Disparities in the Timing of Motherhood in the Andean Region: A Cohort Perspective. \u003cem\u003ePopul Res Policy Rev\u003c/em\u003e. 2020;39(2):283-309. doi:10.1007/s11113-019-09535-0\u003c/li\u003e\n \u003cli\u003eMinisterio de Salud del Per\u0026uacute;. Situaci\u0026oacute;n epidemiol\u0026oacute;gica del VIH - Sida en el Per\u0026uacute; - Bolet\u0026iacute;n VIH - III Trimestre - 2023. Published online 2023.\u003c/li\u003e\n \u003cli\u003eINEI. Encuesta Demogr\u0026aacute;fica y de Salud Familiar - ENDES 2017. Published online 2018.\u003c/li\u003e\n \u003cli\u003ePROMSEX. \u003cem\u003eLa Oposici\u0026oacute;n a La Provisi\u0026oacute;n: Los Mecanismos de Presi\u0026oacute;n de Los Grupos Antiderechos Para El Desabastecimiento de Insumos Para La Salud Sexual y Reproductiva En Am\u0026eacute;rica Latina\u003c/em\u003e. PROMSEX; 2011.\u003c/li\u003e\n \u003cli\u003ePROMSEX. \u003cem\u003eInforme Alternativo de Organizaciones de La Sociedad Civil al Comit\u0026eacute; de Derechos Econ\u0026oacute;micos, Sociales y Culturales Sobre La Situaci\u0026oacute;n de La Salud Sexual y Reproductiva En El Per\u0026uacute;\u003c/em\u003e. PROMSEX; 2011.\u003c/li\u003e\n \u003cli\u003eFranco Vega I. Effects of Condom Availability on Their Use: A Field Experiment in a Peruvian \u003cem\u003eHostal\u003c/em\u003e. \u003cem\u003eBasic Appl Soc Psychol\u003c/em\u003e. Published online 2025:1-12. doi:10.1080/01973533.2024.2403977\u003c/li\u003e\n \u003cli\u003eCcopa PP. \u003cem\u003eEros Liberado: Hostales y Sexualidad En La Lima de Los Noventa\u003c/em\u003e.; 1999.\u003c/li\u003e\n \u003cli\u003eCaballero JA, Tsukamoto Y. Tokyo Public Space Networks at the Intersection of the Commercial and the Domestic Realms Study on Dividual Space. \u003cem\u003eJ Asian Archit Build Eng\u003c/em\u003e. 2006;5(2):301-308. doi:10.3130/jaabe.5.301\u003c/li\u003e\n \u003cli\u003eSouty J. \u003cem\u003eMotel Brasil\u003c/em\u003e. Telhs; Terceiro Nome; 2019.\u003c/li\u003e\n \u003cli\u003eMinisterio de Justicia y derechos humanos del Per\u0026uacute;. \u003cem\u003eC\u0026oacute;digo penal\u003c/em\u003e. 12th ed. Ministerio de Justicia y derechos humanos del Per\u0026uacute;; 2016.\u003c/li\u003e\n \u003cli\u003eDariotis JK, Johnson MW. Sexual discounting among high-risk youth ages 18\u0026ndash;24: Implications for sexual and substance use risk behaviors. \u003cem\u003eExp Clin Psychopharmacol\u003c/em\u003e. 2015;23(1):49-58. doi:10.1016/j.physbeh.2017.03.040\u003c/li\u003e\n \u003cli\u003eSkakoon-Sparling S, Cramer KM, Shuper PA. The Impact of Sexual Arousal on Sexual Risk-Taking and Decision-Making in Men and Women. \u003cem\u003eArch Sex Behav\u003c/em\u003e. 2016;45(1):33-42. doi:10.1007/s10508-015-0589-y\u003c/li\u003e\n \u003cli\u003eSund B, Svensson M, Andersson H. Demographic determinants of incident experience and risk perception: do high-risk groups accurately perceive themselves as high-risk? \u003cem\u003eJ Risk Res\u003c/em\u003e. 2017;20(1):99-117. doi:10.1080/13669877.2015.1042499\u003c/li\u003e\n \u003cli\u003eMoore SG, Dahl DW, Gorn GJ, Weinberg CB, Park J, Jiang Y. Condom embarrassment: Coping and consequences for condom use in three countries. \u003cem\u003eAIDS Care - Psychol Socio-Med Asp AIDSHIV\u003c/em\u003e. 2008;20(5):553-559. doi:10.1080/09540120701867214\u003c/li\u003e\n \u003cli\u003eFraser T, Cherdchaiyapong N, Tekle W, et al. Trust but verify: Validating new measures for mapping social infrastructure in cities. \u003cem\u003eUrban Clim\u003c/em\u003e. 2022;46:101287. doi:10.1016/j.uclim.2022.101287\u003c/li\u003e\n \u003cli\u003eKanazawa S. What do we do with the WEIRD problem? \u003cem\u003eEvol Behav Sci\u003c/em\u003e. 2020;14(4):342-346. doi:10.1037/ebs0000222\u003c/li\u003e\n \u003cli\u003eVidal L, Alcaire F, Brunet G, et al. Validation of secondary data sources of the retail food environment in the capital of Uruguay, an emerging Latin American country. \u003cem\u003eHealth Place\u003c/em\u003e. 2024;90:103356. doi:10.1016/j.healthplace.2024.103356\u003c/li\u003e\n \u003cli\u003eINEI. Planos estratificados de Lima Metropolitana a nivel de manzanas - 2020. Published online 2020.\u003c/li\u003e\n \u003cli\u003eHarvey P. ExifTool. 2022. https://exiftool.org/exiftool_pod.html\u003c/li\u003e\n \u003cli\u003eBache SM, Wickham H. magrittr: A Forward-Pipe Operator for R. Published online 2022.\u003c/li\u003e\n \u003cli\u003eTemple Lang D, Wallace J. RJSONIO: Serialize R Objects to JSON, JavaScript Object Notation. Published online 2021.\u003c/li\u003e\n \u003cli\u003eCooley D. googleway: Accesses Google Maps APIs to Retrieve Data and Plot Maps. Published online 2022.\u003c/li\u003e\n \u003cli\u003eKahle D, Wickman H. ggmap: Spatial Visualization with ggplot2. \u003cem\u003eR J\u003c/em\u003e. 2013;5(1):144-161.\u003c/li\u003e\n \u003cli\u003eNeuwirth E. RColorBrewer: ColorBrewer Palettes. Published online 2022.\u003c/li\u003e\n \u003cli\u003eXiao N. ggsci: Scientific Journal and Sci-Fi Themed Color Palettes for \u0026ldquo;ggplot2.\u0026rdquo; Published online 2018.\u003c/li\u003e\n \u003cli\u003eWickham H, Seidel D. scales: Scale Functions for Visualization. Published online 2022.\u003c/li\u003e\n \u003cli\u003eHlavak M. \u003cem\u003eStargazer: Well-Formatted Regression and Summary Statistics Tables\u003c/em\u003e.; 2022.\u003c/li\u003e\n \u003cli\u003eGarc\u0026iacute;a M. Malls en Lima: \u0026iquest;cu\u0026aacute;les son los destinos m\u0026aacute;s buscados para nuevos proyectos? \u003cem\u003eGesti\u0026oacute;n\u003c/em\u003e. May 21, 2024.\u003c/li\u003e\n \u003cli\u003eOrganismo Supervisor de Inversi\u0026oacute;n Privada en Telecomunicaciones. Checa tu se\u0026ntilde;al. 2023. https://serviciosweb.osiptel.gob.pe/CoberturaMovil/#\u003c/li\u003e\n \u003cli\u003eCHS Alternativo, Municipalidad Metropolitana de Lima. \u003cem\u003eDiagn\u0026oacute;stico Para El An\u0026aacute;lisis, Interpretaci\u0026oacute;n y Medici\u0026oacute;n de La Situaci\u0026oacute;n Actual de La Trata de Personas En Lima Metropolitana\u003c/em\u003e. Capital Humano y Social Alternativo; 2022.\u003c/li\u003e\n \u003cli\u003eCHS Alternativo. \u003cem\u003eBalance de La Sociedad Civil Sobre La Situaci\u0026oacute;n de La Trata de Personas En El Per\u0026uacute; 2020\u0026ndash;2021\u003c/em\u003e. Capital Humano y Social Alternativo; 2022.\u003c/li\u003e\n \u003cli\u003eEgger M, Pauw J, Lopatatzidis A, Medrano D, Paccaud F, Smith GD. Promotion of condom use in a high-risk setting in Nicaragua: a randomised controlled trial. \u003cem\u003eThe Lancet\u003c/em\u003e. 2000;355(9221):2101-2105. doi:10.1016/S0140-6736(00)02376-X\u003c/li\u003e\n \u003cli\u003eGoogle. Metropolitan Lima. Google Maps. (lon = -76.97194012615122, lat = -12.161490701886304)\u003c/li\u003e\n \u003cli\u003eINEI. Caracteristicas demogr\u0026aacute;ficas. In: \u003cem\u003eLima Metropolitana: Perfil Socio-Demogr\u0026aacute;fico\u003c/em\u003e. Instituto Nacional de Estad\u0026iacute;stica e Inform\u0026aacute;tica; 1993.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Distribution of hostales by Style and Size. The percentages are calculated based on the total sample.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"464\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStyle / Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedium\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLarge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eExtra large\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCaleta\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTelo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e31\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e27%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMixed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e45\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e39%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBusiness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e35\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e31%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e114\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e13%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e100%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Table summarising the Presence and Status of the surveyed hostales in Google Maps.\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 95px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003cp\u003e(55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e56\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(49%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWrong place\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(5%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWrong name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(1%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003cp\u003e(45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot visible\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(7%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e43\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(38%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 284px;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003cp\u003e(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Presence of hostales in Google Maps by Hostal Style and Size\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePresence\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 244px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eHostal Style\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eHostal Size\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTelo\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMixed\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBusiness\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCaleta\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSmall\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMedium\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLarge\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eExtra large\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e13\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e26\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e22\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e20\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e17\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e16\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e63\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e42%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e58%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e63%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e67%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e67%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e69%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e43%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e53%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e55%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e18\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e19\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e13\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e9\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e23\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e14\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e51\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e58%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e42%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e37%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003e33%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e33%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e31%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e58%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e47%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cem\u003e45%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eTotal\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e31\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e45\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e35\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e3\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e15\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e29\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e40\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e30\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e114\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Style and Size Models for Presence in Google Maps.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePresence in Google Maps Models\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eStyle Model\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSize Model\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eStyle\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(Ref: Telo)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMixed\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e-0.639\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e(0.473)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eBusiness\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e-0.852\u003csup\u003e*\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e(0.505)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e(Ref: Small)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eMedium\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e-0.105\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e(0.679)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eLarge\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e0.995\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e(0.634)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eExtra Large\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e0.560\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e(0.659)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eConstant\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e0.325\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e(0.364)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e-0.693\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e(0.548)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eObservations\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e111\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e114\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eLog Likelihood\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e-74.817\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e-75.512\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eAkaike Inf. Crit.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e155.635\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003e159.023\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eNote:\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/em\u003e\u003cem\u003ep\u0026lt;0.1; \u003csup\u003e**\u003c/sup\u003ep\u0026lt;0.05; \u003csup\u003e***\u003c/sup\u003ep\u0026lt;0.01\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003eThe Caleta style was removed from the sample in the Style model.\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\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":"Love hotels, Hostales, Peru, Georeferencing, Google Maps, Reliability","lastPublishedDoi":"10.21203/rs.3.rs-7311058/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7311058/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eHostales\u003c/em\u003e (love hotels) are a common yet understudied institution in Per\u0026uacute;. Their widespread use and capacity to concentrate large numbers of people about to have sex make them a potentially important setting for sexual health interventions. Given Per\u0026uacute;\u0026rsquo;s high rates of unintended pregnancies, its potential role as a location for implementing new sexual health policies should be explored. The first step towards this is to document their prevalence, characteristics and distribution.\u003c/p\u003e\u003cp\u003eWe conducted a census over a 4 km\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e area of southern Lima, georeferencing the location of all \u003cem\u003ehostales\u003c/em\u003e and photographing their visual characteristics. We classified them according to their size and appearance, and the findings were compared against their appearance in Google Maps to assess the platform\u0026rsquo;s reliability as a secondary data source.\u003c/p\u003e\u003cp\u003eWe found 114 \u003cem\u003ehostales\u003c/em\u003e, one every 35 m\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Their spatial distribution varied across neighbourhoods, reflecting distinct patterns based on local characteristics. Most of them stated their sexual nature through their advertisements, names or offered amenities. Notably, only 45% of those we found were present in Google Maps.\u003c/p\u003e\u003cp\u003e\u003cem\u003eHostales\u003c/em\u003e are a highly prevalent and heterogeneous business. They differ considerably in terms of offered services, sizes, and styles. They cluster around commercial areas but can also be found in residential and industrial areas. Google Maps proved unreliable as a data source.\u003c/p\u003e\u003cp\u003eGiven their ubiquity and potential for policy impact, \u003cem\u003ehostales\u003c/em\u003e warrant more attention. Targeted interventions in them could reach a sizeable sample of at-risk populations, providing a unique opportunity to improve sexual health.\u003c/p\u003e","manuscriptTitle":"Georeferencing and classifying love hotels in Lima:\nA comparison between field research and Google Maps","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 12:47:00","doi":"10.21203/rs.3.rs-7311058/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":"25da7f83-ef2f-4af2-950c-e123a93620d4","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-22T22:10:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 12:47:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7311058","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7311058","identity":"rs-7311058","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}