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Presentation on theme: "KEEP THIS TEXT BOX this slide includes some ESRI fonts. when you save this presentation, use File > Save As > Tools (upper right) > Save Options > Embed."— Presentation transcript:

1 KEEP THIS TEXT BOX this slide includes some ESRI fonts. when you save this presentation, use File > Save As > Tools (upper right) > Save Options > Embed TrueType Fonts (all characters) this will allow vector maps created with common ESRI symbols to show on computers that do not have ESRI software loaded a a a a a a a a a a a a a a a Quantifying Built Environment Exposure with Objective High-Resolution Data Philip M. Hurvitz, PhD Urban Form Lab College of Built Environments University of Washington 1 of 52 CSSS Seminar University of Washington 2011-11-02

2 Overview  A statistical challenge  Background  How built environment (BE) exposure is commonly measured  Limitations of common methods  Expanding the conceptual model  A new approach explained  A pilot study using the new approach  Limitations & Future research directions 2 of 52

3 A statistical challenge With modern methods of measuring location at very high frequency, accuracy, and precision, how can we quantify environmental exposure across space and time and estimate its effects on behavior or health outcomes? Given inherent properties of such data, how can adjustments be made for problems such as spatial dependence and temporal lag? 3 of 52

4 Overview  A statistical challenge  Background  How built environment (BE) exposure is commonly measured  Limitations of common methods  Expanding the conceptual model  A new approach explained  A pilot study using the new approach  Limitations & Future research directions 4 of 52

5 Background  Changes in chronic disease prevalence: 5 top killers in US 5 of 52

6 Background  Changes in chronic disease prevalence: obesity (adults > 20 y) 6 of 52 Percent 20082004 www.cdc.gov/diabetes

7 Background  Changes in chronic disease prevalence: diabetes (adults > 20 y) 7 of 52 20082004 www.cdc.gov/diabetes Percent

8 Background  Changes in some chronic disease prevalences over the last few decades:  Behavioral changes Evolutionary changes are unlikely Biological agents have not been identified)  Is built environment (BE) to blame? 8 of 52

9 Background  Theoretical model  Social Ecologic Model of Behavior (SEM), based on e.g., Bronfenbrenner, Lewin Built Environment Behavior/ Health outcomes Individual Characteristics Social Environment 9 of 52

10 Overview  A statistical challenge  Background  How built environment (BE) exposure is commonly measured  Limitations of common methods  Expanding the conceptual model  A new approach explained  A pilot study using the new approach  Limitations & Future research directions 10 of 52

11 How BE exposure is commonly measured  A mix of objective and subjective (self- reported) data  BE exposure is equated with “neighborhood” of residence  Census tract in which residence is located  Euclidean or network-based buffer around centroid of residential parcel or building 11 of 52

12 Census areas  Objective measures based on census area of residence 12 of 52 Block JP, Scribner RA, DeSalvo KB. Am. J. Prev. Med. 2004;27(3):211-217.

13 Residential neighborhood  Objective measures based on individually defined neighborhood 13 of 52 Lee C, Moudon AV. J. Plan. Lit. 2004;19(2):147-181. Frank LD, Schmid TL, Sallis JF, Chapman JE, Saelens BE Am J Prev Med 2005;28(2):117-125.

14 Overview  A statistical challenge  Background  How built environment (BE) exposure is commonly measured  Limitations of common methods  Expanding the conceptual model  A new approach explained  A pilot study using the new approach  Limitations & Future research directions 14 of 52

15 Limitations of common methods 15 of 52 Hurvitz PM, Moudon AV, Rehm C, Streichert L, Drewnowski A. IJBNPA 2009;6(1):46    Persons 1 & 2 have the same “exposure” Person 3 has less “exposure” than person 2 Especially sensitive to large variation across small spatial range

16 Spatial variation in exposure 16 of 52 Bassok A, Hurvitz PM, Bae C-HC, Larson T. JEPM 2010;53(1):23-39  Fine particulate matter measured over a small spatial range

17 Limitations of common methods  Both census area and residential neighborhoods overlook a fundamental behavioral reality 17 of 52

18 Limitations of common methods  People move around and are exposed to “multiple neighborhoods” through the course of their daily lives 18 of 52

19 Limitations of common methods  Studies associating home neighborhood BE and health/behavior have had varying degrees of “success”  A syllogism:  Exposure occurs at a person’s location  A person’s location changes across space and time Exposure occurs across space and time Q: So why do we only consider neighborhood of residence? A: Convenience a.k.a. software limitation(?) 19 of 52

20 Limitations of common methods Saelens BE, Handy SL. Built environment correlates of walking: a review. Med Sci Sports Exerc 2008;40(7 Suppl):S550-566.  32 studies that correlated walking with environment (2005-2006)  12 used exclusively subjective data  17 used objective data 7 used administrative unit (range in size: BG to country) 10 used individual household location  No individual-level studies examined environmental properties of non-home locations 20 of 52

21 Limitations of common methods  There has been acknowledgment of the “home alone” problem in recent literature  Kwan MP. Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set. Transp. Res. Pt. C-Emerg. Technol. 2000;8(1-6):185-203.  Rodríguez D, Brown AL, Troped PJ. Portable global positioning units to complement accelerometry-based physical activity monitors. Med Sci Sports Exerc 2005;37(11 Suppl):S572-81.  Troped PJ, Wilson JS, Matthews CE, Cromley EK, Melly SJ. The built environment and location-based physical activity. American Journal of Preventive Medicine 2010;38(4):429- 438.  Rodríguez D, Cho G-H, Evenson KR, Conway TL, Cohen D, Ghosh-Dastidar B, Pickrel JL, Veblen-Mortenson S, Lytle L a. Out and about: association of the built environment with physical activity behaviors of adolescent females. Health Place 2011.  Zenk SN, Schulz AJ, Matthews S a, Odoms-Young A, Wilbur J, Wegrzyn L, Gibbs K, Braunschweig C, Stokes C. Activity space environment and dietary and physical activity behaviors: a pilot study. Health Place 2011;17(5):1150-61. 21 of 52

22 Limitations of common methods  Recent approaches use GPS data to measure location through space and time  Most use either aggregation of data or sampling to overcome GIS software limitations  An exposure measurement framework is needed for a more accurate & complete representation of environmental exposures within individual activity space 22 of 52

23 Overview  A statistical challenge  Background  How built environment (BE) exposure is commonly measured  Limitations of common methods  Expanding the conceptual model  A new approach explained  A pilot study using the new approach  Limitations & Future research directions 23 of 52

24 Expanding the conceptual model  Over relatively short time scales, we can expect the individual and social characteristics of the Social Ecologic Model to become space-time constant “background” conditions, while built and natural environmental effects maintain variability Built/Natural Environment Behavior Individual Characteristics Social Environment Built/Natural Environment Behavior Individual Characteristics Social Environment Built/Natural Environment Behavior 25 of 52

25 Expanding the conceptual model Individual characteristics Social Environment  A trace through space and time conceptualized as a series of localized environmental exposures that may affect space-time localized behavior 26 of 52

26 Overview  A statistical challenge  Background  How built environment (BE) exposure is commonly measured  Limitations of common methods  Expanding the conceptual model  A new approach explained  A pilot study using the new approach  Limitations & Future research directions 27 of 52

27 A new approach explained  We have developed a method for assigning BE measures to any number of point locations  Individual residences  Millions of GPS locations  BE measures represent local conditions within a specified radius of each location  “SmartMaps” provide the framework for quantification of local BE properties continuously across map space 24 of 52

28 Operationalization: SmartMap construction 30 m grid focal processing and interpolation; continuous values across XY space 28 of 52 local neighborhood (833 m=10 minutes walk) BE measurement 12 2 6 5

29 Fitness facilities SmartMap 29 of 52

30 Park count SmartMap 30 of 52

31 Supermarket count SmartMap 31 of 52

32 Fast food restaurant count SmartMap 32 of 52

33 FFR SmartMap with GPS trace  Each GPS point is marked with its local FFR density 33 of 52

34 Overview  A statistical challenge  Background  How built environment (BE) exposure is commonly measured  Limitations of common methods  Expanding the conceptual model  A new approach explained  A pilot study using the new approach  Limitations & Future research directions 34 of 52

35 A pilot study using the new approach  Aim: to determine whether BE characteristics are significantly different between home and non-home locations  Have studies that measured only home neighborhoods suffered from missing data when estimating the effects of BE exposure? 35 of 52

36 A pilot study using the new approach: methods  Convenience sample of 51 UW students, staff, and faculty (41 in final sample)  GPS measurements during waking hours  November, 2007—May, 2008  One week sampling period, 1 Hz GPS sampling interval 36 of 52

37 A pilot study using the new approach: methods  15 SmartMap layers were used, to represent domains related to physical activity & obesity  Neighborhood composition—counts of: employees residential units  Utilitarian destinations—counts of: supermarkets fast food and traditional restaurants, coffee shops fitness facilities count, area, and percent of neighborhood covered by parks 37 of 52

38 A pilot study using the new approach: methods  SmartMap layers  Transportation infrastructure—density of:  street intersections  streets  trails  Traffic conditions:  estimated traffic volume  bus ridership  Result: 4.3 million records (3.8 million in final sample), each with 15 marks representing BE variables 38 of 52

39 A pilot study using the new approach: methods  Dichotomized “home” and “non-home” GPS records  Home = 0-833 m of geocoded home location  Non-home > 1666 m of home 39 of 52

40 A pilot study using the new approach: methods  Bootstrap sampling used to deal with significance inflation  6 records per hour from home and non-home bins  difference of medians between home and non- home samples  10,000 bootstrap iterations  construction of 95% confidence interval from set of difference of medians 40 of 52

41 A pilot study using the new approach: results  Distribution of difference of medians from one BE variable for one subject 95% CI contains 0: Conclude that home value is not different from non-home value 41 of 52

42 A pilot study using the new approach: results  Much intra and inter-subject variation home value higher non-home value higher 42 of 52

43 A pilot study using the new approach: results  “Summary” view of home & non-home differences 43 of 52 Nonhome > home Home > nonhome

44 A pilot study using the new approach: results  Home > non-home  residential units  supermarkets  fitness facilities  parks count percent  street & intersection density 44 of 52

45 A pilot study using the new approach: results  Home < non-home  employees  traditional & fast food restaurants, coffee shops  parks size  trail density  traffic density  bus ridership 45 of 52

46 A pilot study using the new approach: conclusions  Most variables had expected values, e.g.,  Different results based on operationalization of construct 46 of 52 residential densityhome>non-home employment densityhomenon-home % of area in parkhome>non-home park sizehome { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/11/3054825/slides/slide_46.jpg", "name": "A pilot study using the new approach: conclusions  Most variables had expected values, e.g.,  Different results based on operationalization of construct 46 of 52 residential densityhome>non-home employment densityhomenon-home % of area in parkhome>non-home park sizehomenon-home employment densityhomenon-home % of area in parkhome>non-home park sizehome

47 Overview  A statistical challenge  Background  How built environment (BE) exposure is commonly measured  Limitations of common methods  Expanding the conceptual model  A new approach explained  A pilot study using the new approach  Limitations & Future research directions 47 of 52

48 Limitations  Convenience sample (lots of students)  Incomplete GPS data (battery failure): mean < 4 h/d  Cross-sectional  Self-selection for home and non-home locations  Circles used for SmartMap measures  Only a single bandwidth (833 m ≈ 10 min. walk) 48 of 52

49 Future directions  Incorporate network service areas in SmartMaps  Develop SmartMaps using different bandwithds  Apply to better data sets  Larger/random sample of individuals  Greater temporal coverage  Address temporal lag  Address spatial dependence 49 of 52

50 Acknowledgments  University of Washington Royalty Research Fund  NIH  R21AG032232-01 PI: Dr. G. Duncan  R01DK076608-01A1 PI: Dr. A. Drewnowski  R01HL091881 PI: Dr. B. Saelens  Supervisory committee  Moudon, Drewnowski, Logsdon, Borriello  UW Statistical Consulting  Dr. Paul Sampson, Nevena Lalic, Eric Meier  Colleagues in Urban Form Lab 50 of 52

51 Powered By 51 of 52

52 Questions? 52 of 52


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