Locations, Commitments and Activity Spaces

2004 ◽  
pp. 205-230 ◽  
Author(s):  
Kay W. Axhausen ◽  
Arnd König ◽  
Darren M. Scott ◽  
Claudia Jürgens
Keyword(s):  
Activity Spaces

Operationalizing Neighborhood Definitions in Health Research

2018 ◽  
Author(s):  
Dustin T. Duncan ◽  
Seann D. Regan ◽  
Basile Chaix
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Global Positioning System ◽  
Health Research ◽  
Self Report ◽  
Activity Space ◽  
Positioning System ◽  
Activity Spaces ◽  
Global Positioning ◽  
Neighborhood Boundaries

Defining neighborhoods for health research continues to be challenging. This chapter discusses different methods to operationalize neighborhood boundaries, including self-report, administrative definitions, geographic information system buffers and activity spaces, including global positioning system (GPS)–defined activity spaces. It discusses the strengths and limitations of each method of examining neighborhood boundaries (e.g., spatial misclassification, technical difficulties, assumptions). Readers are provided with examples of neighborhood definitions frequently applied in the epidemiology and population health literature. In addition, the chapter provides a rigorous overview of theories for selecting neighborhood definitions, including spatial polygamy theory for GPS-defined activity space neighborhoods.

scholarly journals Activity spaces: assessing differences in alcohol exposures and alcohol use for parents

GeoJournal ◽  
2019 ◽  
Author(s):  
Bridget Freisthler ◽  
Nancy J. Kepple ◽  
Jennifer Price Wolf ◽  
Leslie Carson
Keyword(s):  
Alcohol Use ◽  
Activity Spaces

Accessibility and Intraurban Travel

1987 ◽  
Vol 19 (6) ◽  
pp. 735-748 ◽  
Author(s):  
S Hanson ◽  
M Schwab
Keyword(s):  
Travel Behavior ◽  
Activity Spaces ◽  
Household Travel Survey ◽  
Trip Frequency ◽  
Potential Activity ◽  
Using Data ◽  
Household Travel ◽  
Automobile Use ◽  
The Relationship ◽  
Travel Distances

This paper contains an examination of the fundamental assumption underlying the use of accessibility indicators: that an individual's travel behavior is related to his or her location vis-à-vis the distribution of potential activity sites. First, the conceptual and measurement issues surrounding accessibility and its relationship to travel are reviewed; then, an access measure for individuals is formulated. Using data from the Uppsala (Sweden) Household Travel Survey and controlling for sex, automobile availability, and employment status, the authors explore the relationship between both home- and work-based accessibility and five aspects of an individual's travel: mode use, trip frequencies and travel distances for discretionary purposes, trip complexity, travel in conjunction with the journey to work, and size of the activity space. From the results it can be seen that although all of these travel characteristics are related to accessibility to some degree, the travel–accessibility relationship is not as strong as deductive formulations have implied. High accessibility levels are associated with higher proportions of travel by nonmotorized means, lower levels of automobile use, reduced travel distances for certain discretionary trip purposes, and smaller individual activity spaces. Furthermore, the density of activity sites around the workplace affects the distances travelled by employed people for discretionary purposes. Overall, accessibility level has a greater impact on mode use and travel distance than it does on discretionary trip frequency. This result was unexpected in light of the strong trip frequency–accessibility relationship posited frequently in the literature.

Personalized crime location prediction

2016 ◽  
Vol 27 (3) ◽  
pp. 422-450 ◽  
Author(s):  
MOHAMMAD A. TAYEBI ◽  
UWE GLÄSSER ◽  
MARTIN ESTER ◽  
PATRICIA L. BRANTINGHAM
Keyword(s):  
Law Enforcement ◽  
Urban Crime ◽  
Crime Analysis ◽  
Urban Landscapes ◽  
Location Prediction ◽  
Prevention Strategies ◽  
Pattern Theory ◽  
Activity Spaces ◽  
Crime Pattern Theory ◽  
Crime Pattern

Crime reduction and prevention strategies are vital for policymakers and law enforcement to face inevitable increases in urban crime rates as a side effect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concentrates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionally higher crime density. In this paper, we present CrimeTracer1, a personalized random walk-based approach to spatial crime analysis and crime location prediction outside of hotspots. We propose a probabilistic model of spatial behaviour of known offenders within their activity spaces. Crime Pattern Theory concludes that offenders, rather than venture into unknown territory, frequently select targets in or near places they are most familiar with as part of their activity space. Our experiments on a large real-world crime dataset show that CrimeTracer outperforms all other methods used for location recommendation we evaluate here.

scholarly journals Capturing Human Activity Spaces

2007 ◽  
Vol 2021 (1) ◽  
pp. 70-80 ◽  
Author(s):  
R. K. Rai ◽  
Michael Balmer ◽  
Marcel Rieser ◽  
V. S. Vaze ◽  
Stefan Schönfelder ◽  
...  
Keyword(s):  
Human Activity ◽  
Activity Spaces

Body Weight Stigma in Physical Activity Settings

2018 ◽  
Vol 33 (1) ◽  
Author(s):  
Andrew C. Pickett ◽  
George B. Cunningham
Keyword(s):  
Physical Activity ◽  
Body Weight ◽  
Weight Stigma ◽  
Structured Interviews ◽  
Activity Settings ◽  
Activity Spaces ◽  
Strong Barrier ◽  
Body Ideals

Given societal body ideals praising thinness and muscularity, physical activity spaces can bedifficult to navigate for those in larger bodies. Thus, stigma serves as a strong barrier to participation. Inthis study, the authors explore ways that body stigma affects larger individuals’ participation in physicalactivity. This authors employed qualitative, semi-structured interviews (N = 9), regarding personal experi-ences of body weight stigma. Results suggest that body stigma is common and that various discriminatoryexperiences led participants away from participating. Given the prevalence of prejudicial behaviors, whichexclude larger individuals, the authors argue for more inclusive physical activity spaces and practices.

scholarly journals Use of Activity Space in a Tuberculosis Outbreak: Bringing Homeless Persons Into Spatial Analyses

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Mary Claire Worrell ◽  
Michael Kramer ◽  
Aliya Yamin ◽  
Susan M. Ray ◽  
Neela D. Goswami
Keyword(s):  
Health Department ◽  
Homeless Persons ◽  
Activity Space ◽  
Exposure Science ◽  
Activity Spaces ◽  
Space Analysis ◽  
Location Data ◽  
Density Methods ◽  
Us Cities ◽  
Traditional Surveillance

Abstract Background Tuberculosis (TB) causes significant morbidity and mortality in US cities, particularly in poor, transient populations. During a TB outbreak in Fulton County, Atlanta, GA, we aimed to determine whether local maps created from multiple locations of personal activity per case would differ significantly from traditional maps created from single residential address. Methods Data were abstracted for patients with TB disease diagnosed in 2008–2014 and receiving care at the Fulton County Health Department. Clinical and activity location data were abstracted from charts. Kernel density methods, activity space analysis, and overlay with homeless shelter locations were used to characterize case spatial distribution when using single versus multiple addresses. Results Data were collected for 198 TB cases, with over 30% homeless US-born cases included. Greater spatial dispersion of cases was found when utilizing multiple versus single addresses per case. Activity spaces of homeless and isoniazid (INH)-resistant cases were more spatially congruent with one another than non-homeless and INH-susceptible cases (P < .0001 and P < .0001, respectively). Conclusions Innovative spatial methods allowed us to more comprehensively capture the geography of TB-infected homeless persons, who made up a large portion of the Fulton County outbreak. We demonstrate how activity space analysis, prominent in exposure science and chronic disease, supports that routine capture of multiple location TB data may facilitate spatially different public health interventions than traditional surveillance maps.

Abstract P317: An Activity Space Approach to Assessing the Food Environments of Food Secure and Insecure Women

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Ilana G Raskind ◽  
Michelle C Kegler ◽  
Michael R Kramer
Keyword(s):  
Food Security ◽  
Food Insecurity ◽  
Spatial Behavior ◽  
Activity Space ◽  
Food Sources ◽  
Food Environments ◽  
Spatial Mobility ◽  
Activity Spaces ◽  
Food Security Status ◽  
Space Approach

Introduction: Community food environments (FE) are an important correlate of diet- and weight-related CV health. Conventional approaches to measuring the FE focus on residential neighborhoods, and do not assess the full extent of food sources regularly encountered and used. Further, little attention has been given to how individual diet-related experiences, like food insecurity, may interact with features of the FE to affect health. To address these limitations, we use an activity space approach, defined by the locations women routinely visit, to measure FE exposure and use, and assess differences by food security status. Hypothesis: Food-related spatial behavior and features of the FE differ between a) conventional and activity space definitions, and b) food secure and insecure women. Methods: We present initial results (n=51) from an ongoing clinic-based study of low-income African American women in Atlanta, GA. Data are collected in-person using a Google Map-powered activity space questionnaire. USDA’s 10-item adult scale is used to measure food insecurity. Retail FE data are from Dun & Bradstreet. ArcGIS 10.5 was used to define three environments: residential census tract (CT), and convex hull polygons of overall and food-specific activity spaces. We tested differences, by food security status, in mean behaviors and FE features with one-way ANOVAs. Results: Eighty-eight percent of women were food insecure. Food insecure women were lower income, less often employed, and less often had access to a car. CTs contained fewer supermarkets (μ=1.2 SD =1.4) and fast food restaurants (μ=3.9 SD =3.2) than activity spaces (μ=7.9 SD =7.0; μ=55.5 SD =44.1, respectively). On average, 6.7% ( SD =13.5) of utilized food sources fell within CT bounds, while 53.4% ( SD =35.5) fell within activity spaces. Compared to food secure women, food insecure women had smaller overall (μ=329.8km 2 SD =340.4 vs. μ=548.3km 2 SD =422.4; p =0.16) and food-specific (μ=48.1km 2 SD =74.3 vs. μ=85.6km 2 SD =106.4; p =0.28) activity spaces, and a smaller proportion of their utilized supermarkets fell within their activity spaces (μ=60.9% SD =42.4 vs. μ=81.9% SD =21.4; p =0.24). FE features did not differ by food security status. Conclusions: Conventional FE definitions likely underestimate the number of food sources women encounter, and do not capture the majority of sources used. Smaller activity spaces among food insecure women suggest that routine spatial mobility may be constrained by factors like transportation access. Still, food insecure women more often traveled outside of their activity spaces to utilize supermarkets, suggesting a dual burden of constrained spatial mobility and access. Interestingly, FE features did not differ by food security status. In planned future analyses, any observed differences in diet and weight may indicate variation in how women interact with the FE, rather than differences in exposure.

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