scholarly journals Environment and Resident Perception: Using Geographic Information Systems and Survey Methods

2020 ◽  
Vol 35 (2) ◽  
Author(s):  
Sabina Deitrick ◽  
Scott Beach ◽  
Ellen Kinnee ◽  
Meg Streiff
Keyword(s):  
Information Systems ◽  
Spatial Analysis ◽  
Geographic Information Systems ◽  
Survey Methods ◽  
Well Being ◽  
Geographic Information ◽  
Neighborhood Environment ◽  
Objective Measures ◽  
Subjective Well Being ◽  
Neighborhood Conditions

This paper examines the relationship between subjective measures of quality of life in Pittsburgh, Pennsylvania, and objective measures of neighborhood environment using Geographic Information Systems. The research analyzes differences in participant perceptions of health and environment by combining spatial cluster analysis with survey methods to understand the spatial context of subjective well-being. Environmental Justice Areas are used as objective measures of socio-economic differences in the spatial analysis. The results found significant statistical differences as well as spatial clustering of survey responses and contribute to recent research analyzing subjective well-being through spatial analysis of objectively measured neighborhood conditions.

Validation of the Neighborhood Environment Walkability Scale (NEWS) Items Using Geographic Information Systems

2009 ◽  
Vol 6 (s1) ◽  
pp. S113-S123 ◽  
Author(s):  
Marc A. Adams ◽  
Sherry Ryan ◽  
Jacqueline Kerr ◽  
James F. Sallis ◽  
Kevin Patrick ◽  
...  
Keyword(s):  
Physical Activity ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Built Environment ◽  
Traffic Congestion ◽  
Concurrent Validity ◽  
Geographic Information ◽  
Activity Level ◽  
Neighborhood Environment ◽  
Objective Measures

Background:Concurrent validity of Neighborhood Environment Walkability Scale (NEWS) items was evaluated with objective measures of the built environment using geographic information systems (GIS).Methods:A sample of 878 parents of children 10 to 16 years old (mean age 43.5 years, SD = 6.8, 34.8% non-White, 63.8% overweight) completed NEWS and the International Physical Activity Questionnaire. GIS was used to develop 1-mile street network buffers around participants’ residences. GIS measures of the built environment within participants’ buffers included percent of commercial and institutional land uses; number of schools and colleges, recreational facilities, parks, transit stops, and trees; land topography; and traffic congestion.Results:Except for trees and traffic, concordance between the NEWS and GIS measures were significant, with weak to moderate effect sizes (r = −0.09 to −0.36, all P ≤ 01). After participants were stratified by physical activity level, stronger concordance was observed among active participants for some measures. A sensitivity analysis of self-reported distance to 15 neighborhood destinations found a 20-minute (compared with 10- or 30-minute) walking threshold generally had the strongest correlations with GIS measures.Conclusions:These findings provide evidence of the concurrent validity of self-reported built environment items with objective measures. Physically active adults may be more knowledgeable about their neighborhood characteristics.

A Survey of Spatial Data Mining Methods Databases and Statistics Point of Views

2011 ◽  
pp. 229-242 ◽  
Author(s):  
Karine Zeitouni
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Spatial Analysis ◽  
Geographic Information Systems ◽  
Spatial Statistics ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Data Mining ◽  
Geographic Information ◽  
Mining Methods

This chapter reviews the data mining methods that are combined with Geographic Information Systems (GIS) for carrying out spatial analysis of geographic data. We will first look at data mining functions as applied to such data and then highlight their specificity compared with their application to classical data. We will go on to describe the research that is currently going on in this area, pointing out that there are two approaches: the first comes from learning on spatial databases, while the second is based on spatial statistics. We will conclude by discussing the main differences between these two approaches and the elements they have in common.

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