Investigation of The Sufficiency of Antalya Parks

Urban green spaces are areas established to meet the recreational needs of urban people. Although green spaces vary from country to country and region in terms of plan and design features, they were basically created to allow people to meet with nature. Parks are the basic components of urban landscapes that provide environmental and social functional value. Urban parks, in particular, provide spaces for outdoor physical activities. In order to take advantage of the opportunities of activities in the parks, users must have convenient access to these resources. One of the most important aspects for researching the use and potential benefits of urban green spaces is the assessment of their geographic accessibility. The widespread use of smart city systems and the gradual expansion of their usage areas increase the importance of spatial analysis. Spatial analyses are used in today’s urban management in the processes of determining social needs, identifying current problems, and putting forward solutions. When spatial analyses are used together with GIS, the field of application develops even more, and it supports local governments in responding to the changing demands of the society for a better life. In the study, the adequacy and accessibility of 160 city parks in Konyaaltı District of Antalya Province were examined. In terms of the adequacy of the parks, the area value of 10 m2 per person determined with the Construction Plan numbered 3194 was taken as basis. In terms of accessibility, distance values of 200, 400, 800, 1,200 meters were examined. Neighborhood boundaries and population information were obtained from the relevant units, and Arc-GIS software was used in the analysis. It was determined that the parks in Konyaaltı district were insufficient in terms of adequacy and accessibility. Finally, suggestions were made in terms of increasing the adequacy of the parks and ensuring accessibility.

My Neighborhood Is Fuzzy, Not Hard and Fast: A Mixed-Methods Study of Neighborhood Size Among Aging Americans

In this exploratory sequential mixed-methods study, interviews with 125 adults aged 55-92 living in the Minneapolis (Minnesota) metropolitan area suggest that neighborhood boundaries are “fuzzy”. Qualitative analysis of neighborhood perceptions identified race, mobility, driving status, social connections, housing insecurity, land use, urbanicity, and crime as key themes. Over 8,000 REGARDS participants (mean age 72) indicated how many blocks composed their neighborhoods (mean=9.9, SD=35.4). Linear regression models showed that being over the age of 85, white, less educated, lower income, less physically and cognitively healthy, and living outside of a metropolitan area significantly predicted smaller self-reported neighborhood sizes. Further, there was significant variation among participants residing in the same areas as other respondents. The mixed-methods results indicate that neighborhoods are fluid and dependent on a mix of personal and geographic factors. Findings inform the scale of environmental audits, place-based interventions, and community outreach programs targeting older adults.

Abstract 16462: Using Geospatial Mapping to Target Risk and Facilitate Placement of Community Health Workers, Educational Tools, and AHA Field Teams

Introduction: Diabetes is a chronic condition that affects 34.2 million Americans. Long-lasting or undiagnosed diabetes increases the risk of developing cardiovascular disease (CVD). Geographic Information Systems (GIS) inform healthcare delivery interventions to target patients, clinicians, health systems and communities at highest risk. A gap remains in how data and GIS are applied and leveraged in the context of value-based care delivery systems. This spatial analysis demonstrates the application of a generalizable methodology for allocating resources such as AHA Field Teams, community health workers (CHWs), and educational tools to areas most burdened by diabetes and CVD. Methods: The automated geocoding infrastructure from the Duke Medicine electronic data warehouse was developed to standardize and scale geocoding addresses for a population of people with type 2 diabetes from the Southeastern Diabetes Initiative (SEDI) in 2017. While maintaining accuracy and assuring that patients’ protected health information was secure, we used geocoded data and public data (public health data, census tract data, and information on the built environment) to assess risk and identify accessible resources. Results and Discussion: Using this method to understand racial distribution, level of education, and income,we identified high risk communities in Durham County. We correlated geospatial patterns of health risk, illness burden, and determined accessible resources for diabetes and CVD.We calculated a resource ratio of one CHW to 100 patient-population and identified a central hub for fixed resource access. Using neighborhood boundaries, we identified a community where the burden of diabetes reached 17%, 60% of inhabitants were African American, median income was $55,945, and just 17.7% had a bachelor’s degree. Understanding risk using these social determinants (Figure 1) supports the intentional allocation of resources and hiring of CHWs.

Riding Up the Oil Boom, Sliding Down the Oil Bust, Mid-1970s–1980s

In response to the oil bust of the early 1980s and especially the much-delayed desegregation of Houston schools, neighborhood boundaries and demographics in the 1980s underwent reorganization. The overlapping processes of suburbanization from the mid-1970s and economic fallout in Houston in the 1980s contributed to sweeping change across the metropolis. These transformations nonetheless reinforced an entrenched culture of racial residential segregation.

How to Draw a Neighborhood? The Potential of Big Data, Regionalization, and Community Detection for Understanding the Heterogeneous Nature of Urban Neighborhoods.

How to draw neighborhood boundaries, or spatial regions in general, has been a long‐standing focus in Geography. This article examines this question from a methodological perspective, often referred to as regionalization, with an empirical study of neighborhoods in New York City. I argue that methodological advances, combined with the affordances of big data, enable a different, more nuanced approach to regionalization than has been possible in the past. Conventional data sets often dictate constraints in terms of data availability and spatio‐temporal granularity. However, big data is now available at much finer spatio‐temporal scales and covers a wider array of aspects of social life. The emergence of these data sets supports the notion that neighborhoods can be fuzzy and highly dependent on spatio‐temporal scales and socio‐economic variables. As such, these new data sets can help to bring quantitative analysis in line with social theory that has long emphasized the heterogeneous nature of neighborhoods. This article uses a data set of geotagged tweets to demonstrate how different “sets” of neighborhoods may exist at different spatio‐temporal scales and for different algorithms. Such varying neighborhood boundaries are not a technical problem in need of a solution but rather a reflection of the complexity of the underlying urban fabric.

Partner’s age, not social environment, predicts extrapair paternity in wild great tits (Parus major)

An individual’s fitness is not only influenced by its own phenotype, but by the phenotypes of interacting conspecifics. This is likely to be particularly true when considering fitness gains and losses caused by extrapair matings, as they depend directly on the social environment. While previous work has explored effects of dyadic interactions, limited understanding exists regarding how group-level characteristics of the social environment affect extrapair paternity (EPP) and cuckoldry. We use a wild population of great tits (Parus major) to examine how, in addition to the phenotypes of focal parents, two neighborhood-level traits—age and personality composition—predict EPP and cuckoldry. We used the well-studied trait “exploration behavior” as a measure of the reactive-proactive personality axis. Because breeding pairs inhabit a continuous “social landscape,” we first established an ecologically relevant definition of a breeding “neighborhood” through genotyping parents and nestlings in a 51-ha patch of woodland and assessing the spatial predictors of EPP events. Using the observed decline in likelihood of EPP with increasing spatial separation between nests, we determined the relevant neighborhood boundaries, and thus the group phenotypic composition of an individual’s neighborhood, by calculating the point at which the likelihood of EPP became negligible. We found no evidence that “social environment” effects (i.e., neighborhood age or personality composition) influenced EPP or cuckoldry. We did, however, find that a female’s own age influenced the EPP of her social mate, with males paired to older females gaining more EPP, even when controlling for the social environment. These findings suggest that partner characteristics, rather than group phenotypic composition, influence mating activity patterns at the individual level.

Comparing Different Residential Neighborhood Definitions and the Association Between Density of Restaurants and Home Cooking Among Dutch Adults

The definition of neighborhoods as areas of exposure to the food environment is a challenge in food environment research. We aimed to test the association of density of restaurants with home cooking using four different definitions of residential neighborhoods. We also tested effect modification by age, length of residency, education, and income. This innovative cross-sectional study was conducted in the Netherlands (N = 1245 adults). We calculated geographic information system-based measures of restaurant density using residential administrative neighborhood boundaries, 800 m and 1600 m buffers around the home and respondents’ self-defined boundaries (drawn by the respondents on a map of their residential area). We used adjusted Poisson regression to test associations of restaurant density (tertiles) and the outcome ”weekly consumption of home-cooked meals” (six to seven as compared to five days per week (day/week) or fewer). Most respondents reported eating home-cooked meals for at least 6 day/week (74.2%). Regardless of the neighborhood definition used, no association between food environment and home cooking was observed. No effect modification was found. Although exposure in terms of density of restaurants was different according to the four different neighborhood definitions, we found no evidence that the area under study influences the association between density of restaurants and home cooking among Dutch adults.

Strangers in the Neighborhood: Violence and Neighborhood Boundaries

New Orleans experienced elevated rates of violent crime throughout the thirty years between 1985 and 2015. Violence was disproportionately represented in socioeconomically disadvantaged communities. This study explores the lived experiences of residents from one such neighborhood, using individual interviews, focus groups, and participant observation. The data indicate that neighborhood boundaries vacillated between rigidly defensive and porous, which impacted residents’ ability to enact collective efficacy and thus to create a milieu that either positively or negatively influenced the likelihood of violence. With a long history of institutional and social neglect, the community initially viewed outsiders as invaders which resulted in rigidly defended boundaries. As the community emerged from social marginalization and was able to enact collective efficacy, its boundaries became more porous and resources flowed into the community. As a result, violence decreased, further enhancing collective efficacy and boundary porosity in a virtuous cycle. This suggests that crime prevention and response models that engage residents and decrease marginalization may decrease cynicism, open boundaries, and improve collective efficacy, thereby reducing neighborhood violence.