Mortgage Foreclosures,Race, and Postrecession Lending

This article examines the effect of high foreclosure rates on postrecession lending. Our hypothesis is that high neighborhood foreclosure rates will have a significant and positive effect on the likelihood of mortgage loan denial. In a case study on Toledo, Ohio, we explore the role of foreclosure activity, race, and racial disparities in lending practices and how they differ across neighborhoods. Our results suggest that applicants in high-foreclosure neighborhoods have a greater likelihood of loan denial (ceteris paribus). We also find that minority applicants face a higher probability of loan denial in high-foreclosure minority neighborhoods. Overall, the results depict highly variable lending practices where race seems to make a difference albeit in a small subset of neighborhoods deeply affected by the foreclosure crisis. There is also some indication of a chilling effect on minority loan applicants in Toledo during the postrecession period. Key Words: foreclosure, logistic regression, mortgage lending, neighborhood contingency, race discrimination.     

Developing a GIS-Based Online Survey Instrument to Elicit Perceived Neighborhood Geographies to Address the Uncertain Geographic Context Problem

Although neighborhood factors have been consistently associated with health, technological difficulties in eliciting self-defined neighborhoods from large cohorts have compromised the interpretability of this research. Here, we offer a mixed-methods approach to elicit and validate self-defined neighborhoods. Participants used a customized Google.Maps interface to “draw” their neighborhood and answered questions about perceived map accuracy, neighborhood definition, and neighborhood activities. We compared geographic concordance of drawn and narrative neighborhood definitions, quantified differential accuracy by demographic characteristics, and examined factors influencing neighborhood definitions. We found similar geographic concordance between narrative and mapped boundaries in two cities, with no differences by neighborhood size. Self-reported neighborhoods had greater concordance with larger administrative areas (e.g., police precincts) than for smaller units (e.g., census tracts). To delineate their neighborhood boundaries, participants reported using administrative definitions, walking distance, their familiarity with people and structures, where they spend time, and physical landmarks. In New York City, participants also reported considering sociodemographic characteristics and transportation. Our method demonstrates the feasibility of collecting perceived (egocentric) neighborhoods through online mapping surveys, adaptable to many study settings.     

广州社区绿化环境对居民自评健康的邻里影响

良好的城市生态环境有益于居民的健康福祉。至今鲜有研究阐明绿化环境对中国城市居民健康的心理—社会—行为机制。本文运用广州调查问卷数据、遥感影像数据和百度街景数据,提取多种社区绿化指标,并运用多层线性回归模型和中介效应分析技术,阐明社区绿化环境影响居民自评健康的路径和机制,定量测度社区绿化环境的健康效应在不同社会群体间的差异。研究发现：① 社区绿化水平与居民的自评健康水平存在显著关联;② 社区绿化通过缓解心理压力提升居民的自评健康水平;③ 社区绿化的健康效应在不同收入群体和不同性别群体之间存在明显差异,表现为社区绿化水平与健身时长和心理压力的关系,中低收入群体强于高收入群体,女性群体强于男性群体。本文以期丰富健康地理学视角下的绿化环境与公共健康的实证研究,并为健康社区建设和人居环境提升提供科学依据。     

Twitter and Academic Geography through the Lens of #AAG2018

Twitter has emerged as a global social network of active users who share conversations with one another in an online setting. Academics are one community that has increasingly taken to Twitter as a means of connecting with other scholars, sharing research, and obtaining meaningful feedback. Tweeting has become especially popular during academic conferences where conference attendees use Twitter hashtags to filter conference conversations into a separate dialogue. For geographers, the Annual Meeting of the American Association of Geographers (AAG) represents one such occasion to use Twitter to discuss contemporary developments in geographic research. In this article, we provide an overview of Twitter as well as the ways in which the academic community uses the platform. Following this, we discuss the tweets sent using the hashtag for the 2018 AAG Annual Meeting, #AAG2018. To analyze these tweets, we collected all tweets with this hashtag for a period of four weeks and examined the content using word clouds and sentiment analysis to explore general feelings and trends associated with geography and the AAG Annual Meeting. We conclude with suggestions for future research avenues that could use Twitter data to gauge the pulse of the geographic discipline. Key Words: academic conferences, American Association of Geographers, geography, sentiment analysis, Twitter.     

Illustrative Materials for Geography

Abstract

Studies dealing with characteristics of housing and settlement have been based almost entirely on field observation or aerial photographs. However, this report uses information from the Department of Census and Statistics of Ceylon to plot the distribution of dwellings with specific construction material characteristics for Ceylon, thus indicating an aspect of rural housing characteristics and settlement. Use of dwelling construction materials such as mud, clay, stone, brick, cadjan, and thatch can be related to physical characteristics of the area involved, availability of building materials, and level of living and culture of the inhabitants. Cost of construction material must also be considered. If similar housing data, but more complete and of greater scope, were available from the census of every nation, much could be achieved rapidly in the geography of settlement.     

Population Distribution in Mexico—1950

Abstract

To participate in a modern economy, every region needs at least one bigjob (Basic Income-Generating JOB), an economic activity that brings money into a region from outside. Accurate identification of bigjobs is needed for effective infrastructure-building, vocational education, and public subsidies. This article describes a mapping activity that helps students identify bigjobs in state economies. The activity shows that the farm output and industrial production of California, although very large in absolute terms, are below the national average per capita. Further analysis of census data leads to the conclusion that the profits (capital gains) from sales of existing houses are arguably one of the largest bigjobs in the state. The decline in profits between 1988 (the peak year) and 1992 (during a slump) was much greater than the cutbacks in high-tech military contracts (often blamed for the high unemployment in the early 1990s).     

Spatial autocorrelation and data uncertainty in the American Community Survey: a critique

ABSTRACT

We argue that the use of American Community Survey (ACS) data in spatial autocorrelation statistics without considering error margins is critically problematic. Public health and geographical research has been slow to recognize high data uncertainty of ACS estimates, even though ACS data are widely accepted data sources in neighborhood health studies and health policies. Detecting spatial autocorrelation patterns of health indicators on ACS data can be distorted to the point that scholars may have difficulty in perceiving the true pattern. We examine the statistical properties of spatial autocorrelation statistics of areal incidence rates based on ACS data. In a case study of teen birth rates in Mecklenburg County, North Carolina, in 2010, Global and Local Moran’s I statistics estimated on 5-year ACS estimates (2006–2010) are compared to ground truth rate estimates on actual counts of births certificate records and decennial-census data (2010). Detected spatial autocorrelation patterns are found to be significantly different between the two data sources so that actual spatial structures are misrepresented. We warn of the possibility of misjudgment of the reality and of policy failure and argue for new spatially explicit methods that mitigate the biasedness of statistical estimations imposed by the uncertainty of ACS data.     

蓝绿空间暴露对老年人健康的邻里影响

利用2018年广州社区问卷数据、遥感影像、城市街景、环境污染等多元数据,提取多种蓝绿空间指标,并使用多层模型、中介效应模型和倾向值匹配法,探讨中国城市背景下蓝绿空间暴露影响老年人健康的“生物?心理?社会”邻里影响路径和机制,以及对不同社会阶层间的差异。研究发现,社区层面的蓝绿空间暴露与老年人的身心健康均存在显著相关;周围蓝绿空间程度通过促进体育锻炼、缓解压力提升老年人的身体健康,可达性通过促进社会交往提升老年人的心理健康;蓝绿空间暴露对老年人健康的影响在不同阶层间存在差异。为健康老龄化、健康城市建设提供科学参考。     

Spatial crime distribution and prediction for sporting events using social media

ABSTRACT

Sporting events attract high volumes of people, which in turn leads to increased use of social media. In addition, research shows that sporting events may trigger violent behavior that can lead to crime. This study analyses the spatial relationships between crime occurrences, demographic, socio-economic and environmental variables, together with geo-located Twitter messages and their ‘violent’ subsets. The analysis compares basketball and hockey game days and non-game days. Moreover, this research aims to analyze crime prediction models using historical crime data as a basis and then introducing tweets and additional variables in their role as covariates of crime. First, this study investigates the spatial distribution of and correlation between crime and tweets during the same temporal periods. Feature selection models are applied in order to identify the best explanatory variables. Then, we apply localized kernel density estimation model for crime prediction during basketball and hockey games, and on non-game days. Findings from this study show that Twitter data, and a subset of violent tweets, are useful in building prediction models for the seven investigated crime types for home and away sporting events, and non-game days, with different levels of improvement.     

Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks

ABSTRACT

The investigation of human activity patterns from location-based social networks like Twitter is an established approach of how to infer relationships and latent information that characterize urban structures. Researchers from various disciplines have performed geospatial analysis on social media data despite the data’s high dimensionality, complexity and heterogeneity. However, user-generated datasets are of multi-scale nature, which results in limited applicability of commonly known geospatial analysis methods. Therefore in this paper, we propose a geographic, hierarchical self-organizing map (Geo-H-SOM) to analyze geospatial, temporal and semantic characteristics of georeferenced tweets. The results of our method, which we validate in a case study, demonstrate the ability to explore, abstract and cluster high-dimensional geospatial and semantic information from crowdsourced data.     

Neighborhood-Based social capital and life satisfaction: the case of Rotterdam,The Netherlands

ABSTRACT

In this study, we examine the relationship between neighborhood-based social capital and residents’ life satisfaction by considering resident heterogeneity. Using a database of the city of Rotterdam, The Netherlands, we find a small but significant positive association between neighborhood-based social capital and individual life satisfaction. However, we also find considerable differences among residents because neighborhood-based social capital is important mainly for people who are more likely to spend considerable time in the neighborhood or who are more neighborhood dependent (i.e. less-educated people, people who live on welfare, people with poor health, retired people, and those who are divorced or widowed). Our results confirm the importance of neighborhood-based social capital for residents’ life satisfaction in terms of both actual social contacts with neighbors and the perceived social cohesion within a neighborhood. At the same time, the importance of neighborhood-based social capital varies among different groups of residents. These findings have important implications for policy-makers.     

邻里环境感知与个体健康对通勤模式选择的影响研究——以南京市为例

通勤是大部分城市就业者日常工作所必须面对的问题。相比宏观层面的城市建成环境,微观层面的邻里环境感知及个体健康与通勤模式选择的联系更为密切。以南京市主城区8个社区为案例地,利用结构方程模型探究邻里环境感知与个体健康对城市就业者通勤模式选择的影响。首先借助问卷调查数据,将通勤模式分为积极通勤、公共交通通勤、电动车/摩托车通勤以及小汽车通勤四类;其次使用因子分析法确定邻里环境感知的潜变量,并将个体健康分为身体健康、心理健康与健康行为3个潜变量;最后构建结构方程模型的理论框架并进行实证分析。研究发现：① 邻里环境感知变量中服务设施感知、环境品质感知对通勤模式选择存在显著的直接效应,交通安全感知对通勤模式选择存在显著的间接效应。② 个体健康变量中,心理健康与健康行为对通勤模式选择存在显著的直接效应。③ 通勤距离与社会经济属性对通勤模式选择存在显著的直接效应,同时社会经济属性对通勤模式选择亦存在显著的间接效应。基于研究结果,提出有效提升就业者对于社区环境的主观感受,进而提高就业者参与健康行为的意愿与就业者的健康水平,从而引导其选择更加健康和绿色的通勤模式。     

The Spatial Dialectics of Modernity and Retail Affect at Abasto Shopping,Buenos Aires,Argentina

Abstract

The paper is about the political life of a building: the Abasto. Located in what was called the “most porteño” neighborhood in the first part of the 20th century (“porteño” is someone from central Buenos Aires) when it functioned as the city's main food market, the Abasto became a massive shopping mall in 1998 amid rapid neoliberal restructuring. This paper charts the political life of this building in two steps. First, by drawing on theories of socio-spatial dialectics, this paper charts the history of the Abasto as an urban object in a wider political landscape of porteño modernity. Second, by incorporating recent theories of affect and presenting findings from field work carried out at the mall in 2010 and 2011, this paper develops a framework for understanding the politics of consumption in a “post-neoliberal” urban landscape.     

邻里目的地建成环境对老年日常活动机会的影响——基于感知和参与视角

老年人的日常活动水平与身心健康密切相关,且受到多种邻里目的地空间的复杂干预。但目前研究较少关注到老年人的活动机会特征和以邻里目的地为中心的建成环境范围。论文结合“城市人”理论,从老年人的生存、接触和社交等需求出发,以大连市主城区为例,基于老年人对日常活动感知和参与数量的问卷调查数据,并选取商业服务设施、医疗卫生设施、教育设施和休闲娱乐设施作为老年人潜在接触的邻里目的地,借助聚类分析、线性回归和Logistic回归模型探究活动机会类型及其建成环境影响因素。研究结果表明：老年日常活动机会可分为旁观型、目标型和活跃型3类;商业服务设施对活动感知、休闲娱乐设施对活动参与有显著影响;医院的环境适宜性对旁观型老年人、商场的空间区位条件对活跃型老年人分别存在较大的作用效应。研究结果可为精细化地建设老龄友好城市提供新思路。     

Finding spatial outliers in collective mobility patterns coupled with social ties

ABSTRACT

Currently the increase in the variety and volume of data sources is demanding new data analytical workflows for exploring them concurrently, especially if the goal is to detect spatial outliers. In this paper, we propose a data analytical workflow for exploring Call Detail Records in conjunction with geotagged tweets. The aim was to investigate how massive data point observations can be analyzed to detect spatial outliers in collective mobility patterns that are coupled with social ties. This workflow consists of analytical tasks that are developed based on the a-priori assumption of two isometric spaces where Natural Language Processing techniques are used to find spatial clusters from geotagged tweets in a Social Space which are later used to aggregate the Call Detail Records generated by antennas located in the Mobility Space. The dynamic weighted centroids that are given by the mean location of the number of calls per hour of all antennas that belong to a particular cluster are used to compute Standard Deviation Ellipses. The longer the period of time a weighted centroid stays outside of the 99.7% probability region of an ellipse, the highest the likelihood that they are spatial outliers. The workflow was implemented for the city of Dakar in Senegal. The results indicate that the further the hourly weighted centroids are skewed from the normal mean of an ellipse, the stronger the influence of a cluster is in finding spatial outliers. Furthermore, the longer the period of time the outliers stays outside of the 99.7% probability region of an ellipse, the highest the likelihood that the outliers are genuine and can be associated to extraordinary events such as natural disasters and national holidays.     

A novel methodology for identifying environmental exposures using GPS data

Aim: While studies using global positioning systems (GPS) have the potential to refine measures of exposure to the neighbourhood environment in health research, one limitation is that they do not typically identify time spent undertaking journeys in motorised vehicles when contact with the environment is reduced. This paper presents and tests a novel methodology to explore the impact of this concern.

Methods: Using a case study of exposure assessment to food environments, an unsupervised computational algorithm is employed in order to infer two travel modes: motorised and non-motorised, on the basis of which trips were extracted. Additional criteria are imposed in order to improve robustness of the algorithm.

Results: After removing noise in the GPS data and motorised vehicle journeys, 82.43% of the initial GPS points remained. In addition, after comparing a sub-sample of trips classified visually of motorised, non-motorised and mixed mode trips with the algorithm classifications, it was found that there was an agreement of 88%. The measures of exposure to the food environment calculated before and after algorithm classification were strongly correlated.

Conclusion: Identifying non-motorised exposures to the food environment makes little difference to exposure estimates in urban children but might be important for adults or rural populations who spend more time in motorised vehicles.     

Standards-Directed Education: The Good,the Bad,and the Ugly

Abstract

GIS Concepts and ArcGIS Methods, David M. Theobald Fort Collins, CO: Conservations Planning Technologies, 2003. 342 pp. ISBN 0-9679208-2-5 $55.00 (paper)

Lies My Teacher Told Me: Everything Your American History Textbook Got Wrong, James W. Loewen New York: A Touchstone Book, Simon &; Schuster. 1995. 383 pp. Figures, references, and index, ISBN: 0684818868. $14.00 (paper).     

Geographic information systems and health: An educational resource

Abstract

The use of geographic information systems to analyze spatial dimensions of health care and disease ecology is becoming a realistic prospect for investigators in the social sciences. This review of the literature, bringing together a diverse collection of professional and academic journals, can be grouped into four categories: potential, cautionary, preliminary, and application. Enough references have been collected and reviewed to provide instructors with material for a classroom unit about 1) using GIS in a medical geography class: 2) medical applications in a GIS class; or 3) using GIS in classes which have a health and disease component.     

Scale effects in food environment research: Implications from assessing socioeconomic dimensions of supermarket accessibility in an eight-county region of South Carolina

Choice of neighborhood scale affects associations between environmental attributes and health-related outcomes. This phenomenon, a part of the modifiable areal unit problem, has been described fully in geography but not as it relates to food environment research. Using two administrative-based geographic boundaries (census tracts and block groups), supermarket geographic measures (density, cumulative opportunity and distance to nearest) were created to examine differences by scale and associations between three common U.S. Census–based socioeconomic status (SES) characteristics (median household income, percentage of population living below poverty and percentage of population with at least a high school education) and a summary neighborhood SES z-score in an eight-county region of South Carolina. General linear mixed-models were used. Overall, both supermarket density and cumulative opportunity were higher when using census tract boundaries compared to block groups. In analytic models, higher median household income was significantly associated with lower neighborhood supermarket density and lower cumulative opportunity using either the census tract or block group boundaries, and neighborhood poverty was positively associated with supermarket density and cumulative opportunity. Both median household income and percent high school education were positively associated with distance to nearest supermarket using either boundary definition, whereas neighborhood poverty had an inverse association. Findings from this study support the premise that supermarket measures can differ by choice of geographic scale and can influence associations between measures. Researchers should consider the most appropriate geographic scale carefully when conducting food environment studies.