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Black sexually minoritized men (BSMM) are the most likely to acquire HIV in Chicago- a racially segregated city where their daily travel may confer different HIV-related risks. From survey and GPS data among participants of the Neighbourhoods and Networks Cohort Study, we examined spatial (proportion of total activity space away from home), temporal (proportion of total GPS points away from home), and motivation-specific (discordance between residential and frequented sex or socializing neighbourhoods) dimensions of mobility. To identify potential drivers of BSMM's risk, we then examined associations between mobility and sexual behaviours known to cause HIV transmission: condomless anal sex, condomless anal sex with a casual partner, transactional sex, group sex, and sex-drug use. Multivariable logistic regression models assessed associations. Of 269 cisgender BSMM, most were 20-29 years old, identified as gay, and lowincome. On average, 96.9% (Standard Deviation: 3.7%) of participants' activity space and 53.9% (Standard Deviation: 38.1%) of participants' GPS points occurred outside their 800m home network buffer. After covariate adjustment, those who reported sex away from home were twice as likely to report condomless sex (Odds Ratio: 2.02, [95% Confidence Interval (CI): 1.08, 3.78]). Those who reported socializing away from home were four times more likely to have condomless sex with a casual partner (Odds Ratio: 4.16 [CI: 0.99, 29.0]). BSMM are on the move in Chicago, but only motivation-specific mobility may increase HIV transmission risk. Multidimensional investigations of mobility can inform place-based strategies for HIV service delivery.

Neighborhood deprivation plays an important role in childhood health and development, but defining the appropriate neighborhood definition presents theoretical as well as practical challenges. Few studies have compared neighborhood definitions outside of highly urbanized settings. The purpose of the current study was to evaluate how various administrative and ego-centric neighborhood definitions may impact measured exposure to deprivation across the urban-rural continuum. We do so using the Family Life Project, a prospective longitudinal population-based sample of families living in North Carolina and Pennsylvania (USA), which also sets the stage for future investigations of neighborhood impacts on childhood health and development. To measure neighborhood deprivation, a standardized index of socioeconomic deprivation was calculated using data from the 2007-2011 American Community Survey. Families' residential addresses when children were 2 months of age (n=1036) were geocoded and overlaid onto a deprivation index layer created at the census block group level to construct multiple administrative and ego-centric neighborhood definitions. Friedman tests were used to compare distributions of neighborhood deprivation across these neighborhood definitions within urbanized areas, urban clusters, and rural areas. Results indicated differences in urbanized areas (Chisquare= 897.75, P<0.001) and urban clusters (Chi-square=687.83, P<0.001), but not in rural areas (Chi-square=13.52, P=0.332). Findings imply that in urban areas, choice of neighborhood definition impacts measured exposure to neighborhood deprivation. Although exposure to neighborhood deprivation appears to be less sensitive to neighborhood definition in rural areas, researchers should apply theoretical reasoning to choose appropriate definitions of children's neighborhood.

The aim of this study was to assess mobility patterns among a sample of transgender women (n=14) in New York City via survey and Global Positioning System (GPS) monitoring. We found varying levels of concordance between the residential neighbourhood and each of the non-residential contexts: 64.3% considered the neighbourhood that they socialised in most often to be different from their residential neighbourhood. While participants' residences represented 10 zone improvement plan code tabulation areas (ZCTAs), GPS data were recorded in 124 of 263 ZCTAs (47.1%). Overall, 58.2% (n=373,262) were recorded in ZCTAs in the highest quartile of human immunodeficiency virus (HIV) prevalence. The association between place, community HIV prevalence, mobility, and factors that increase the vulnerability of transgender women to HIV infection are worthy of future investigation in reducing the burden of the HIV epidemic in these communities.

Research has examined how the food environment affects the risk of cardiovascular disease (CVD). Many studies have focused on residential neighbourhoods, neglecting the activity spaces of individuals. The objective of this study was to investigate whether food environments in both residential and global positioning system (GPS)-defined activity space buffers are associated with body mass index (BMI) and blood pressure (BP) among low-income adults. Data came from the New York City Low Income Housing, Neighborhoods and Health Study, including BMI and BP data (n=102, age=39.3±14.1 years), and one week of GPS data. Five food environment variables around residential and GPS buffers included: fast-food restaurants, wait-service restaurants, corner stores, grocery stores, and supermarkets. We examined associations between food environments and BMI, systolic and diastolic BP, controlling for individual- and neighbourhood-level sociodemographics and population density. Within residential buffers, a higher grocery store density was associated with lower BMI (β=- 0.20 kg/m2, P<0.05), and systolic and diastolic BP (β =-1.16 mm Hg; and β=-1.02 mm Hg, P<0.01, respectively). In contrast, a higher supermarket density was associated with higher systolic and diastolic BP (β=1.74 mm Hg, P<0.05; and β=1.68, P<0.01, respectively) within residential buffers. In GPS neighbourhoods, no associations were documented. Examining how food environments are associated with CVD risk and how differences in relationships vary by buffer types have the potential to shed light on determinants of CVD risk. Further research is needed to investigate these relationships, including refined measures of spatial accessibility/exposure, considering individual's mobility.

Previous research has highlighted the salience of spatial stigma on the lives of low-income residents, but has been theoretical in nature and/or has predominantly utilised qualitative methods with limited generalisability and ability to draw associations between spatial stigma and measured cardiovascular health outcomes. The primary objective of this study was to evaluate relationships between perceived spatial stigma, body mass index (BMI), and blood pressure among a sample of low-income housing residents in New York City (NYC). Data come from the community-based NYC Low-income Housing, Neighborhoods and Health Study. We completed a crosssectional analysis with survey data, which included the four items on spatial stigma, as well objectively measured BMI and blood pressure data (analytic n=116; 96.7% of the total sample). Global positioning systems (GPS) tracking of the sample was conducted for a week. In multivariable models (controlling for individual-level age, gender, race/ethnicity, education level, employment status, total household income, neighborhood percent non-Hispanic Black and neighborhood median household income) we found that participants who reported living in an area with a bad neighborhood reputation had higher BMI (B=4.2, 95%CI: -0.01, 8.3, P=0.051), as well as higher systolic blood pressure (B=13.2, 95%CI: 3.2, 23.1, P=0.01) and diastolic blood pressure (B=8.5, 95%CI: 2.8, 14.3, P=0.004). In addition, participants who reported living in an area with a bad neighborhood reputation had increased risk of obesity/overweight [relative risk (RR)=1.32, 95%CI: 1.1, 1.4, P=0.02) and hypertension/pre-hypertension (RR=1.66, 95%CI: 1.2, 2.4, P=0.007). However, we found no differences in spatial mobility (based GPS data) among participants who reported living in neighborhoods with and without spatial stigma (P>0.05). Further research is needed to investigate how placebased stigma may be associated with impaired cardiovascular health among individuals in stigmatised neighborhoods to inform effective cardiovascular risk reduction interventions.

The purpose of this study was to evaluate the feasibility of using global positioning system (GPS) methods to understand the spatial context of obesity and hypertension risk among a sample of low-income housing residents in New York City (n = 120). GPS feasibility among participants was measured with a pre- and post-survey as well as adherence to a protocol which included returning the GPS device as well as objective data analysed from the GPS devices. We also conducted qualitative interviews with 21 of the participants. Most of the sample was overweight (26.7%) or obese (40.0%). Almost one-third (30.8%) was pre-hypertensive and 39.2% was hypertensive. Participants reported high ratings of GPS acceptability, ease of use and low levels of wear-related concerns in addition to few concerns related to safety, loss or appearance, which were maintained after the baseline GPS feasibility data collection. Results show that GPS feasibility increased over time. The overall GPS return rate was 95.6%. Out of the total of 114 participants with GPS, 112 (98.2%) delivered at least one hour of GPS data for one day and 84 (73.7%) delivered at least one hour on 7 or more days. The qualitative interviews indicated that overall, participants enjoyed wearing the GPS devices, that they were easy to use and charge and that they generally forgot about the GPS device when wearing it daily. Findings demonstrate that GPS devices may be used in spatial epidemiology research in low-income and potentially other key vulnerable populations to understand geospatial determinants of obesity, hypertension and other diseases that these populations disproportionately experience.

Geocoding, the process of matching addresses to geographic coordinates, is a necessary first step when using geographical information systems (GIS) technology. However, different geocoding methodologies can result in different geographic coordinates. The objective of this study was to compare the positional (i.e. longitude/latitude) difference between two common geocoding methods, i.e. ArcGIS (Environmental System Research Institute, Redlands, CA, USA) and Batchgeo (freely available online at http://www.batchgeo.com). Address data came from the YMCA-Harvard After School Food and Fitness Project, an obesity prevention intervention involving children aged 5-11 years and their families participating in YMCA-administered, after-school programmes located in four geographically diverse metropolitan areas in the USA. Our analyses include baseline addresses (n = 748) collected from the parents of the children in the after school sites. Addresses were first geocoded to the street level and assigned longitude and latitude coordinates with ArcGIS, version 9.3, then the same addresses were geocoded with Batchgeo. For this analysis, the ArcGIS minimum match score was 80. The resulting geocodes were projected into state plane coordinates, and the difference in longitude and latitude coordinates were calculated in meters between the two methods for all data points in each of the four metropolitan areas. We also quantified the descriptions of the geocoding accuracy provided by Batchgeo with the match scores from ArcGIS. We found a 94% match rate (n = 705), 2% (n = 18) were tied and 3% (n = 25) were unmatched using ArcGIS. Forty-eight addresses (6.4%) were not matched in ArcGIS with a match score ≥80 (therefore only 700 addresses were included in our positional difference analysis). Six hundred thirteen (87.6%) of these addresses had a match score of 100. Batchgeo yielded a 100% match rate for the addresses that ArcGIS geocoded. The median for longitude and latitude coordinates for all the data was just over 25 m. Overall, the range for longitude was 0.04-12,911.8 m, and the range for latitude was 0.02-37,766.6 m. Comparisons show minimal differences in the median and minimum values, while there were slightly larger differences in the maximum values. The majority (>75%) of the geographic differences were within 50 m of each other; mostly <25 m from each other (about 49%). Only about 4% overall were ≥400 m apart. We also found geographic differences in the proportion of addresses that fell within certain meter ranges. The match-score range associated with the Batchgeo accuracy level "approximate" (least accurate) was 84-100 (mean = 92), while the "rooftop" Batchgeo accuracy level (most accurate) delivered a mean of 98.9 but the range was the same. Although future research should compare the positional difference of Batchgeo to criterion measures of longitude/latitude (e.g. with global positioning system measurement), this study suggests that Batchgeo is a good, free-of-charge option to geocode addresses.