Structure of 311 service requests as a signature of urban location.
ABSTRACT: While urban systems demonstrate high spatial heterogeneity, many urban planning, economic and political decisions heavily rely on a deep understanding of local neighborhood contexts. We show that the structure of 311 Service Requests enables one possible way of building a unique signature of the local urban context, thus being able to serve as a low-cost decision support tool for urban stakeholders. Considering examples of New York City, Boston and Chicago, we demonstrate how 311 Service Requests recorded and categorized by type in each neighborhood can be utilized to generate a meaningful classification of locations across the city, based on distinctive socioeconomic profiles. Moreover, the 311-based classification of urban neighborhoods can present sufficient information to model various socioeconomic features. Finally, we show that these characteristics are capable of predicting future trends in comparative local real estate prices. We demonstrate 311 Service Requests data can be used to monitor and predict socioeconomic performance of urban neighborhoods, allowing urban stakeholders to quantify the impacts of their interventions.
Project description:Opioid use disorder and overdose deaths is a public health crisis in the United States, and there is increasing recognition that its etiology is rooted in part by social determinants such as poverty, isolation and social upheaval. Limiting research and policy interventions is the low temporal and spatial resolution of publicly available administrative data such as census data. We explore the use of municipal service requests (also known as "311" requests) as high resolution spatial and temporal indicators of neighborhood social distress and opioid misuse. We analyze the spatial associations between georeferenced opioid overdose event (OOE) data from emergency medical service responders and 311 service request data from the City of Columbus, OH, USA for the time period 2008-2017. We find 10 out of 21 types of 311 requests spatially associate with OOEs and also characterize neighborhoods with lower socio-economic status in the city, both consistently over time. We also demonstrate that the 311 indicators are capable of predicting OOE hotspots at the neighborhood-level: our results show code violation, public health, and street lighting were the top three accurate predictors with predictive accuracy as 0.92, 0.89 and 0.83, respectively. Since 311 requests are publicly available with high spatial and temporal resolution, they can be effective as opioid overdose surveillance indicators for basic research and applied policy.
Project description:Spatial differences in urban environmental conditions contribute to health inequalities within cities. The purpose of the paper is to map environmental inequalities relevant for health in the City of Dortmund, Germany, in order to identify needs for planning interventions. We develop suitable indicators for mapping socioeconomically-driven environmental inequalities at the neighborhood level based on published scientific evidence and inputs from local stakeholders. Relationships between socioeconomic and environmental indicators at the level of 170 neighborhoods were analyzed continuously with Spearman rank correlation coefficients and categorically applying chi-squared tests. Reclassified socioeconomic and environmental indicators were then mapped at the neighborhood level in order to determine multiple environmental burdens and hotspots of environmental inequalities related to health. Results show that the majority of environmental indicators correlate significantly, leading to multiple environmental burdens in specific neighborhoods. Some of these neighborhoods also have significantly larger proportions of inhabitants of a lower socioeconomic position indicating hotspots of environmental inequalities. Suitable planning interventions mainly comprise transport planning and green space management. In the conclusions, we discuss how the analysis can be used to improve state of the art planning instruments, such as clean air action planning or noise reduction planning towards the consideration of the vulnerability of the population.
Project description:BACKGROUND:Dengue is a mosquito-borne virus that causes extensive morbidity and economic loss in many tropical and subtropical regions of the world. Often present in cities, dengue virus is rapidly spreading due to urbanization, climate change and increased human movements. Dengue cases are often heterogeneously distributed throughout cities, suggesting that small-scale determinants influence dengue urban transmission. A better understanding of these determinants is crucial to efficiently target prevention measures such as vector control and education. The aim of this study was to determine which socioeconomic and environmental determinants were associated with dengue incidence in an urban setting in the Pacific. METHODOLOGY:An ecological study was performed using data summarized by neighborhood (i.e. the neighborhood is the unit of analysis) from two dengue epidemics (2008-2009 and 2012-2013) in the city of Nouméa, the capital of New Caledonia. Spatial patterns and hotspots of dengue transmission were assessed using global and local Moran's I statistics. Multivariable negative binomial regression models were used to investigate the association between dengue incidence and various socioeconomic and environmental factors throughout the city. PRINCIPAL FINDINGS:The 2008-2009 epidemic was spatially structured, with clusters of high and low incidence neighborhoods. In 2012-2013, dengue incidence rates were more homogeneous throughout the city. In all models tested, higher dengue incidence rates were consistently associated with lower socioeconomic status (higher unemployment, lower revenue or higher percentage of population born in the Pacific, which are interrelated). A higher percentage of apartments was associated with lower dengue incidence rates during both epidemics in all models but one. A link between vegetation coverage and dengue incidence rates was also detected, but the link varied depending on the model used. CONCLUSIONS:This study demonstrates a robust spatial association between dengue incidence rates and socioeconomic status across the different neighborhoods of the city of Nouméa. Our findings provide useful information to guide policy and help target dengue prevention efforts where they are needed most.
Project description:Many scholars, policy analysts, and practitioners agree that neighborhoods are important contexts for urban youth. Yet, despite decades of research, our knowledge of why and how neighborhoods influence the day-to-day lives of youth is still emerging. Theories about neighborhood effects largely assume that neighborhoods operate to influence youth through exposure-based mechanisms. Extant theoretical approaches, however, have neglected the processes by which neighborhood socioeconomic contexts influence the routine spatial exposures-or activity spaces-of urban residents. In this article, we argue that exposure to organizations, institutions, and other settings that characterize individual activity spaces is a key mechanism through which neighborhoods influence youth outcomes. Moreover, we hypothesize that aggregate patterns of shared local exposure-captured by the concept of ecological networks-are influenced by neighborhood socioeconomic characteristics and are independently consequential for neighborhood youth. Neighborhoods in which residents intersect in space more extensively as a result of routine conventional activities will exhibit higher levels of social capital relevant to youth well-being, including (1) familiarity, (2) beneficial (weak) social ties, (3) trust, (4) shared expectations for pro-social youth behavior (collective efficacy), and (5) the capacity for consistent monitoring of public space. We then consider the implications of ecological networks for understanding the complexities of contextual exposure. We specifically discuss the role of embeddedness in ecological communities-that is, clusters of actors and locations that intersect at higher rates-for understanding contextual influences that are inadequately captured by geographically defined neighborhoods. We conclude with an overview of new approaches to data collection that incorporate insights from an activity-space and ecological-network perspective on neighborhood and contextual influences on youth. Our approach offers (1) a new theoretical approach to understanding the links between neighborhood socioeconomic characteristics and youth-relevant dimensions of neighborhood social capital; (2) a basis for conceptualizing contextual influences that vary within, or extend beyond, traditionally understood geographic neighborhoods; and (3) a suite of methodological tools and resources to address the mechanisms of contextual influence more precisely. Research into the causes and consequences of urban neighborhood routine activity structures will illuminate the social processes accounting for compromised youth outcomes in disadvantaged neighborhoods and enhance the capacity for effective youth-oriented interventions.
Project description:Accessing high-resolution, timely socioeconomic data such as data on population, employment, and enterprise activity at the neighborhood level is critical for social scientists and policy makers to design and implement location-based policies. However, in many developing countries or cities, reliable local-scale socioeconomic data remain scarce. Here, we show an easily accessible and timely updated location attribute-restaurant-can be used to accurately predict a range of socioeconomic attributes of urban neighborhoods. We merge restaurant data from an online platform with 3 microdatasets for 9 Chinese cities. Using features extracted from restaurants, we train machine-learning models to estimate daytime and nighttime population, number of firms, and consumption level at various spatial resolutions. The trained model can explain 90 to 95% of the variation of those attributes across neighborhoods in the test dataset. We analyze the tradeoff between accuracy, spatial resolution, and number of training samples, as well as the heterogeneity of the predicted results across different spatial locations, demographics, and firm industries. Finally, we demonstrate the cross-city generality of this method by training the model in one city and then applying it directly to other cities. The transferability of this restaurant model can help bridge data gaps between cities, allowing all cities to enjoy big data and algorithm dividends.
Project description:<h4>Background</h4>Most studies of the association between neighborhood socioeconomic deprivation and individual lifestyles leading to cardiovascular disease focused on a single cardiovascular risk factor. The concomitant assessment of more than one risk factor may provide clues to specific mechanisms linking neighborhood disadvantage to individual lifestyles. We investigated the association of neighborhood deprivation with fruits and vegetables consumption and leisure-time physical activity in adults living in an urban center in Portugal.<h4>Methods</h4>In 1999-2003, we assembled a random sample of 2081 adult residents in the city of Porto. Data on sociodemographic characteristics were collected by trained interviewers using structured questionnaires. Fruits and vegetables consumption was estimated using a validated 82-item semiquantitative food frequency questionnaire covering the previous year and expressed in portions per day. Physical activity was evaluated using a questionnaire exploring leisure-time activities over the previous year and expressed in metabolic equivalents (MET).minute/day. Self-reported address was used to place individuals in neighborhoods. Neighborhoods' socioeconomic characterization was based on aggregated data at the census block level provided by the 2001 National Census. Latent class analysis models were used to identify three discrete socioeconomic classes of neighborhoods. Random effects models with random intercepts at the neighborhood level were used to explore clustering and contextual effects of neighborhood deprivation on each of the outcomes.<h4>Results</h4>We found evidence of neighborhood clustering of fruits and vegetables consumption and leisure-time physical activity that persisted after adjustment for neighborhood deprivation only among women. Women living in the most deprived neighborhoods presented a consumption increase of 0.43 (95% CI: -0.033 to 0.89) portions of fruits and vegetables per day and a decrease in leisure-time physical activity of 47.8 (95% CI: -91.8 to 1.41) MET.minute/day, when compared to those living in the most affluent neighborhoods. Among men, no contextual neighborhood deprivation effects were observed.<h4>Conclusion</h4>Overall, neighborhood deprivation had a small effect on the consumption of fruits and vegetables and leisure-time physical activity. Neighborhood factors other than socioeconomic deprivation may still impact on the studied outcomes among women. This study provides relevant information for the design of interventions directed to neighborhood characteristics in the prevention of cardiovascular diseases.
Project description:We hypothesized that neighborhoods with drug markets, as compared to those without, have a greater concentration of infected sex partners, i.e. core transmitters, and that in these areas, there is an increased risk environment for STIs. This study determined if neighborhood drug markets were associated with a high-risk sex partnership and, separately, with a current bacterial STI (chlamydia and/or gonorrhea) after controlling for individual demographic and sexual risk factors among a household sample of young people in Baltimore City, MD. Analyses also tested whether links were independent of neighborhood socioeconomic status. Data for this study were collected from a household study, systematic social observations and police arrest, public health STI surveillance and U.S. census data. Nonlinear multilevel models showed that living in neighborhoods with household survey-reported drug markets increased the likelihood of having a high-risk sex partnership after controlling for individual-level demographic factors and illicit drug use and neighborhood socioeconomic status. Further, living in neighborhoods with survey-reported drug markets increased the likelihood of having a current bacterial STI after controlling for individual-level demographic and sexual risk factors and neighborhood socioeconomic status. The results suggest that local conditions in neighborhoods with drug markets may play an important role in setting-up risk environments for high-risk sex partnerships and bacterial STIs. Patterns observed appeared dependent on the type of drug market indicator used. Future studies should explore how conditions in areas with local drug markets may alter sexual networks structures and whether specific types of drug markets are particularly important in determining STI risk.
Project description:Neighborhood psychosocial stressors like crime and physical disorder may influence obesity-related outcomes through chronic stress or through adverse effects on health behaviors. Google Street View imagery provides a low-cost, reliable method for auditing neighborhood physical disorder, but few studies have examined associations of Street View-derived physical disorder scores with health outcomes. We used Google Street View to audit measures of physical disorder for residential census blocks from 225 women aged 18?44 enrolled from 4 Chicago neighborhoods. Latent neighborhood physical disorder scores were estimated using an item response theory model and aggregated to the block group level. Block-group level physical disorder scores and rates of police-recorded crime and 311 calls for service requests were linked to participants based on home addresses. Associations were estimated for 6 obesity-related outcomes: body mass index, obesity, total moderate-to-vigorous physical activity, and weekly consumption of sugar-sweetened beverages, fast food, and snacks. Hierarchical regression models estimated cross-sectional associations adjusting for individual sociodemographics and neighborhood poverty. Higher neighborhood physical disorder was associated with greater odds of obesity (OR: 1.43, 95% CI: 1.01, 2.02). Living in a neighborhood with a higher crime rate was associated with an increase in weekly snack consumption of 3.06 (95% CI: 1.59, 4.54).
Project description:BACKGROUND:We sought to identify if socioeconomic and demographic factors play a role in resident knowledge, attitude, and practice regarding Dengue, Chikungunya, and Zika in order to inform effective management procedures for disease prevention in Panama, a middle-income tropical country in Central America. All three are arthropod-borne viruses transmitted by Aedes mosquito vectors present in the focal region of Panama City, the largest city in Central America and an urban region of extreme socioeconomic polarization. METHODS:Between November 2017 and February 2018, we administered standardized, anonymous knowledge, attitude, and practice surveys to 263 residents split between two neighborhoods of high socioeconomic status (SES) and two neighborhoods of low SES. We then summed the knowledge, attitude, and practice scores respectively, and used linear and logistic regressions to quantify relationships with socioeconomic and demographic factors. RESULTS:Low-SES neighborhoods with high proportions of low income residents, residents over 70 years old had lower mean knowledge scores compared to other groups. Furthermore, residents in neighborhoods of low SES reported more mosquito biting relative to residents in neighborhoods of high SES, yet comparably lower level of concerns for disease transmission. Additionally, knowledge was lower for the more novel emergent threats of Chikungunya and Zika, compared to the endemic Dengue. CONCLUSION:Findings suggest that low-SES neighborhoods with high proportions of low income, low education, and elderly residents should be targeted for outreach programs designed to prevent DENV, CHIKV, or ZIKV in Panama City. These outcomes support our initial hypotheses as lower relative knowledge and fewer practices related to the prevention of Dengue, Chikungunya, and Zika were found in low-SES neighborhoods. There is also a widespread lack of adequate knowledge regarding these diseases as well as low levels of concern in areas of highly reported mosquito biting. We provide suggestions for taking neighborhood socioeconomic status and specific aspects resident health literacy and attitude into account for creating more effective outreach campaigns as both endemic and novel arthropod-borne disease rates continue to increase throughout Latin America.
Project description:This data article describes data from an Our Voice citizen science data collection aiming at identifying elements that facilitate or hinder physical activity among adolescents in a medium sized city in Sweden. Twenty-four adolescents from two neighborhoods with low socioeconomic status in Sweden used the Stanford Healthy Neighborhood Discovery Tool app on their phones to take photographs and record audio narratives of aspects of their neighborhood that they perceived as facilitating or hindering their physically activity. In total, 186 photos of the neighborhood elements were taken by the adolescents and thereafter the research group categorized the photos into a final set of 16 elements of which 12 described the built environment and 4 the social environment. The data collection included the combination of the following data collected using the app: photographs, geocoded data of where the photographs were taken, recorded narratives describing the photographs, positive and negative neighborhood attributes (portrayed as a happy or sad "smiley face"), and an 8-item survey. In addition, we used official statistics from the City of Västerås describing the two neighborhoods as well as the whole city. This data article is associated with the article titled "Using citizen science to understand the prerequisites for physical activity among adolescents in low socioeconomic status neighborhoods - the NESLA study" .