Estimating Neighborhood Environments from Survey Data

Transcription

1 Estimating Neighborhood Environments from Survey Data Ikuho Yamada Interfaculty Initiative in Information Studies Center for Spatial Information Science ersity of Tokyo

2 Research themes Keywords Geographic information science (GIScience), spatial statistics & quantitative spatial analysis, space time analysis, health/medical geography, urban engineering, Neighborhood (NB) effects on human health, NB walkability, detection of spatial patterns in health issues, Research questions How do NB environments relate lt to health of residents? How can NB environments be modified to contribute improved human health? Lifestyle related illnesses, obesity, physical inactivity,

3 Obesity and NB walkability Obesity epidemic in the U.S. 65% of U.S. adults overweight or obese (Hedley et al. 2004) 280,000 annual deaths attributable to obesity (Allison et al Flegal et al. 2005) Individual based approach Nutrition guidelines Physical activity guidelines Population based approach Healthy environments Food environments Built environments Neighborhood walkability Obesity rate in % 19% 20% 24% 25% 29% 30%

4 Measures of NB walkability 3D s of walkability (Cervero & Kockelman 1997) Population Density Pedestrian friendly Design Street connectivity, quality sidewalks Land use Diversity Mixed land use, various destinations Geographic Information Systems (GIS) are frequently used to measure 3D s for individuals NB by combining Survey data (e.g., census) Geographic feature data (e.g., street networks, parks, stations, ) Remote sensing data (e.g., satellite imageryofvegetation) vegetation), etc.

5 Definitions of NB boundaries No definite consensus yet, neither conceptually nor spatially Spatial definitions Administrative or census geographical units Censustracts tracts, blockgroups groups, ZIP code zones (ZCTAs), Easily available Not well personalized Inconsistent in terms of geographical sizes Buffers generated around individuals residence Circular buffers using straight line (i.e., Euclidean) distance Network buffers using shortest path (i.e., network) distance Well localized/personalized to capture individuals situations Computationally very intensive

6 Estimation of 3D measures for NB units Most of large scale survey data are available based on administrative/census units NB boundaries using those predefined units Easy to compose 3D measures E.g., D for population density = (pop. in census)/(area) NB boundaries using personalized buffers Require geometric operations to combine values assigned to different administrative/census units For the green buffer in the figure, 6 units need to be combined Proportional to areas of overlapping parts Again, computationally intensive

7 Case study in Salt Lake Co., Utah Examined relationships between 3D measures and individual level BMI (body mass index) using three NB definitions Census tract, t census block group, 1km network kbuffer Results Best NB definitions vary between male and female as well as among different 3D measures Best in terms of explanatory power of statistical models Network buffers, theoretically most closely capturing individuals NB environments, do not always work best Localized/personalized, closely related to walking, consistent geographic dfiii definition

8 Question How can estimation of NB characteristics for buffers be improved? Survey values are combined proportionally to the area of overlapping parts But population and other human activities are not uniformly distributed over each unit Reflecting such variability might be a solution

9 Question (cont.) Additional data might be utilized to do so Driver license records (available for the Utah study) Street network data to capture street density Volunteered geographic information (VGI) Geo tagged tweets, locations of mobile phone users,

10 Question (cont.) Other types of network buffers may also be useful Home only home + workplace/school Point location daily travel paths (time geographic approach) Static dynamic Etc. etc.