An Innovative Context-Based Crystal-Growth Activity Space Method for Environmental Exposure Assessment: A Study Using GIS and GPS Trajectory Data Collected in Chicago
Abstract
:1. Introduction
2. Methodology
2.1. GPS Tracking and Context Data
2.2. Core Areas of Daily Activities
2.3. Context-Based Hexagonal Accessibility-Weighted Planes
2.4. Hexagon-Grid Crystal-Growth Activity Space
2.5. Other Existing Activity Space Methods
3. Results
3.1. Context-Based Crystal-Growth Activity Space
3.2. Comparing the CCG Activity Spaces with Other Activity Spaces
3.2.1. Comparing the Activity Spaces
3.2.2. Comparing Physical-Activity-Friendly Contextual Exposures Measured by Different Activity Spaces
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
- Koohsari, M.J.; Mavoa, S.; Villianueva, K.; Sugiyama, T.; Badland, H.; Kaczynski, A.T.; Owen, N.; Giles-Corti, B. Public open space, physical activity, urban design and public health: Concepts, methods and research agenda. Health Place 2015, 33, 75–82. [Google Scholar] [CrossRef] [PubMed]
- Millstein, R.A.; Yeh, H.-C.; Brancati, F.L.; Batts-Turner, M.; Gary, T.L. Food availability, neighborhood socioeconomic status, and dietary patterns among blacks with type 2 diabetes mellitus. Medscape J. Med. 2009, 11, 15. [Google Scholar] [PubMed]
- Epstein, D.H.; Tyburski, M.; Craig, I.M.; Phillips, K.A.; Jobes, M.L.; Vahabzadeh, M.; Mezghanni, M.; Lin, J.L.; Furr-Holden, C.D.M.; Preston, K.L. Real-time tracking of neighborhood surroundings and mood in urban drug misusers: Application of a new method to study behavior in its geographical context. Drug Alcohol Depend. 2014, 134, 22–29. [Google Scholar] [CrossRef] [PubMed]
- Shareck, M.; Kestens, Y.; Vallée, J.; Datta, G.; Frohlich, K.L.; Vallee, J.; Datta, G.; Frohlich, K.L. The added value of accounting for activity space when examining the association between tobacco retailer availability and smoking among young adults. Tob. Control 2015, 25, 1–7. [Google Scholar] [CrossRef] [PubMed]
- Lee, C.; Moudon, A.V. Correlates of walking for transportation or recreation purposes. J. Phys. Act. Health 2006, 3, S77–S98. [Google Scholar] [CrossRef] [PubMed]
- Saelens, B.E.; Sallis, J.F.; Frank, L.D. Environmental correlates of walking and cycling: Findings from the transportation, urban design, and planning literatures. Ann. Behav. Med. 2003, 25, 80–91. [Google Scholar] [CrossRef] [PubMed]
- Giles-Corti, B.; Broomhall, M.H.; Knuiman, M.; Collins, C.; Douglas, K.; Ng, K.; Lange, A.; Donovan, R.J. Increasing walking: How important is distance to, attractiveness, and size of public open space? Am. J. Prev. Med. 2005, 28, 169–176. [Google Scholar] [CrossRef] [PubMed]
- Ewing, R.; Schmid, T.; Killingsworth, R.; Zlot, A.; Raudenbush, S. Relationship between urban sprawl and physical activity, obesity, and morbidity. Am. J. Health Promot. 2003, 18, 47–57. [Google Scholar] [CrossRef] [PubMed]
- Frank, L.D.; Andresen, M.A.; Schmid, T.L. Obesity relationships with community design, physical activity, and time spent in cars. Am. J. Prev. Med. 2004, 27, 87–96. [Google Scholar] [CrossRef] [PubMed]
- Chaix, B.; Merlo, J.; Evans, D.; Leal, C.; Havard, S. Neighbourhoods in eco-epidemiologic research: Delimiting personal exposure areas. A response to Riva, Gauvin, Apparicio and Brodeur. Soc. Sci. Med. 2009, 69, 1306–1310. [Google Scholar] [CrossRef] [PubMed]
- Lee, B.A.; Reardon, S.F.; Firebaugh, G.; Farrell, C.R.; Matthews, S.A.; O’Sullivan, D. Beyond the Census Tract: Patterns and Determinants of Racial Segregation at Multiple Geographic Scales. Am. Sociol. Rev. 2008, 73, 766–791. [Google Scholar] [CrossRef] [PubMed]
- Miller, H. Place-Based versus People-Based Geographic Information Science. Geogr. Compass 2007, 3, 503–535. [Google Scholar] [CrossRef]
- Perchoux, C.; Chaix, B.; Cummins, S.; Kestens, Y. Conceptualization and measurement of environmental exposure in epidemiology: Accounting for activity space related to daily mobility. Health Place 2013, 21, 86–93. [Google Scholar] [CrossRef] [PubMed]
- Kwan, M. The Uncertain Geographic Context Problem. Ann. Assoc. Am. Geogr. 2012, 102, 958–968. [Google Scholar] [CrossRef]
- Weber, J.; Kwan, M.-P. Evaluating the Effects of Geographic Contexts on Individual Accessibility: A Multilevel Approach 1. Urban Geogr. 2003, 24, 647–671. [Google Scholar] [CrossRef]
- Inagami, S.; Cohen, D.A.; Finch, B.K. Non-residential neighborhood exposures suppress neighborhood effects on self-rated health. Soc. Sci. Med. 2007, 65, 1779–1791. [Google Scholar] [CrossRef] [PubMed]
- Saarloos, D.; Kim, J.-E.; Timmermans, H. The built environment and health: Introducing individual space-time behavior. Int. J. Environ. Res. Public Health 2009, 6, 1724–1743. [Google Scholar] [CrossRef] [PubMed]
- Houston, D. Implications of the modifiable areal unit problem for assessing built environment correlates of moderate and vigorous physical activity. Appl. Geogr. 2014, 50, 40–47. [Google Scholar] [CrossRef]
- Rainham, D.; McDowell, I.; Krewski, D.; Sawada, M. Conceptualizing the healthscape: Contributions of time geography, location technologies and spatial ecology to place and health research. Soc. Sci. Med. 2010, 70, 668–676. [Google Scholar] [CrossRef] [PubMed]
- Chaix, B.; Méline, J.; Duncan, S.; Jardinier, L.; Perchoux, C.; Vallée, J.; Merrien, C.; Karusisi, N.; Lewin, A.; Brondeel, R.; et al. Neighborhood environments, mobility, and health: Towards a new generation of studies in environmental health research. Rev. Epidemiol. Sante Publique 2013, 61, 139–145. [Google Scholar] [CrossRef] [PubMed]
- Duncan, M.J.; Badland, H.M.; Mummery, W.K. Applying GPS to enhance understanding of transport-related physical activity. J. Sci. Med. Sport 2009, 12, 549–556. [Google Scholar] [CrossRef] [PubMed]
- Maddison, R.; Ni Mhurchu, C. Global positioning system: A new opportunity in physical activity measurement. Int. J. Behav. Nutr. Phys. Act. 2009, 6, 73. [Google Scholar] [CrossRef] [PubMed]
- Krause, C. An Activity Space Based Approach for Capturing Long Distance Travel Using Longitudinal GPS Survey Data. In Proceedings of the Transportation Research Board 92nd Annual Meeting, Washington, DC, USA, 13–17 January 2012; pp. 1–23. [Google Scholar]
- Shen, Y.; Chai, Y.W. Daily activity space of suburban mega-community residents in Beijing based on GPS data. Acta Geogr. Sin. 2013, 68, 506–516. [Google Scholar]
- Albert, D.P.; Gesler, W.M. How spatial analysis can be used in medical geography. In Spatial Analysis, GIS and Remote Sensing; CRC Press: Boca Raton, FL, USA, 2003; pp. 19–46. [Google Scholar]
- Golledge, R.G. Spatial Behavior: A Geographic Perspective; Guilford Press: New York, NY, USA, 1997; ISBN 1572300507. [Google Scholar]
- Gesler, W.M.; Meade, M.S. Locational and population factors in health care-seeking behavior in Savannah, Georgia. Health Serv. Res. 1988, 23, 443–462. [Google Scholar] [PubMed]
- Sharp, G.; Denney, J.T.; Kimbro, R.T. Multiple contexts of exposure: Activity spaces, residential neighborhoods, and self-rated health. Soc. Sci. Med. 2015, 146, 204–213. [Google Scholar] [CrossRef] [PubMed]
- Tamura, K.; Elbel, B.; Chaix, B.; Regan, S.D.; Al-Ajlouni, Y.A.; Athens, J.K.; Meline, J.; Duncan, D.T. Residential and GPS-Defined Activity Space Neighborhood Noise Complaints, Body Mass Index and Blood Pressure Among Low-Income Housing Residents in New York City. J. Commun. Health 2017, 42, 974–982. [Google Scholar] [CrossRef] [PubMed]
- Cummins, S. Commentary: Investigating neighbourhood effects on health—Avoiding the “local trap”. Int. J. Epidemiol. 2007, 36, 355–357. [Google Scholar] [CrossRef] [PubMed]
- Adams, B.; Kapan, D.D. Man bites mosquito: Understanding the contribution of human movement to vector-borne disease dynamics. PLoS ONE 2009, 4, e6763. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Diez Roux, A.V. Investigating Neighbourhood and Area Effects on Health. Am. J. Public Health 2001, 91, 1783–1789. [Google Scholar] [CrossRef] [PubMed]
- Oakes, J.M.; Forsyth, A.; Schmitz, K.H. The effects of neighborhood density and street connectivity on walking behavior: The Twin Cities walking study. Epidemiol. Perspect. Innov. 2007, 4, 16. [Google Scholar] [CrossRef] [PubMed]
- Spielman, S.; Yoo, E. The spatial dimensions of neighborhood effects. Soc. Sci. Med. 2009, 68, 1098–1105. [Google Scholar] [CrossRef] [PubMed]
- Kwan, M. How GIS can help address the uncertain geographic context problem in social science research. Ann. GIS 2012, 18, 245–255. [Google Scholar] [CrossRef]
- Almanza, E.; Jerrett, M.; Dunton, G.; Seto, E.; Ann Pentz, M. A study of community design, greenness, and physical activity in children using satellite, GPS and accelerometer data. Health Place 2012, 18, 46–54. [Google Scholar] [CrossRef] [PubMed]
- Wiehe, S.E.; Hoch, S.C.; Liu, G.C.; Carroll, A.E.; Wilson, J.S.; Fortenberry, J.D. Adolescent Travel Patterns: Pilot Data Indicating Distance from Home Varies by Time of Day and Day of Week. J. Adolesc. Health 2008, 42, 418–420. [Google Scholar] [CrossRef] [PubMed]
- Kestens, Y.; Thierry, B.; Shareck, M.; Steinmetz-Wood, M.; Chaix, B. Integrating activity spaces in health research: Comparing the VERITAS activity space questionnaire with 7-day GPS tracking and prompted recall. Spat. Spatiotemporal. Epidemiol. 2018, 25, 1–9. [Google Scholar] [CrossRef]
- Kestens, Y.; Thierry, B.; Chaix, B. Re-creating daily mobility histories for health research from raw GPS tracks: Validation of a kernel-based algorithm using real-life data. Health Place 2016, 40, 29–33. [Google Scholar] [CrossRef] [PubMed]
- Ta, N.; Chai, Y.; Kwan, M.P. Suburbanization, daily lifestyle and space-behavior interaction: A study of suburban residents in Beijing, China. Acta Geogr. Sin. 2015, 70, 1271–1280. [Google Scholar]
- Shen, Y.; Chai, Y.; Kwan, M.P. Space-time fixity and flexibility of daily activities and the built environment: A case study of different types of communities in Beijing suburbs. J. Transp. Geogr. 2015, 47, 90–99. [Google Scholar] [CrossRef]
- Tana; Kwan, M.P.; Chai, Y. Urban form, car ownership and activity space in inner suburbs: A comparison between Beijing (China) and Chicago (United States). Urban Stud. 2016, 53, 1784–1802. [Google Scholar] [CrossRef]
- Shen, Y.; Kwan, M.P.; Chai, Y. Investigating commuting flexibility with GPS data and 3D geovisualization: A case study of Beijing, China. J. Transp. Geogr. 2013, 32, 1–11. [Google Scholar] [CrossRef]
- Zenk, S.N.; Schulz, A.J.; Matthews, S.A.; Odoms-Young, A.; Wilbur, J.E.; Wegrzyn, L.; Gibbs, K.; Braunschweig, C.; Stokes, C. Activity space environment and dietary and physical activity behaviors: A pilot study. Health Place 2011, 17, 1150–1161. [Google Scholar] [CrossRef] [PubMed]
- Rodriguez, D.A.; Brown, A.L.; Troped, P.J. Portable global positioning units to complement accelerometry-based physical activity monitors. Med. Sci. Sports Exerc. 2005, 37, S572–S581. [Google Scholar] [CrossRef] [PubMed]
- Rodríguez, D.A.; Cho, G.-H.; Evenson, K.R.; Conway, T.L.; Cohen, D.; Ghosh-Dastidar, B.; Pickrel, J.L.; Veblen-Mortenson, S.; Lytle, L.A. Out and about: Association of the built environment with physical activity behaviors of adolescent females. Health Place 2012, 18, 55–62. [Google Scholar] [CrossRef] [PubMed]
- Wheeler, B.W.; Cooper, A.R.; Page, A.S.; Jago, R. Greenspace and children’s physical activity: A GPS/GIS analysis of the PEACH project. Prev. Med. (Baltim) 2010, 51, 148–152. [Google Scholar] [CrossRef] [PubMed]
- Arem, H.; Moore, S.C.; Patel, A.; Hartge, P.; Berrington De Gonzalez, A.; Visvanathan, K.; Campbell, P.T.; Freedman, M.; Weiderpass, E.; Adami, H.O.; et al. Leisure time physical activity and mortality: A detailed pooled analysis of the dose-response relationship. JAMA Intern. Med. 2015, 175, 959–967. [Google Scholar] [CrossRef] [PubMed]
- O’Donovan, G.; Lee, I.M.; Hamer, M.; Stamatakis, E. Association of “weekend warrior” and other leisure time physical activity patterns with risks for all-cause, cardiovascular disease, and cancer mortality. JAMA Intern. Med. 2017, 177, 335–342. [Google Scholar] [CrossRef] [PubMed]
- Lahti, J.; Holstila, A.; Lahelma, E.; Rahkonen, O. Leisure-time physical activity and all-cause mortality. PLoS ONE 2014, 9, e101548. [Google Scholar] [CrossRef] [PubMed]
- Moore, S.C.; Lee, I.M.; Weiderpass, E.; Campbell, P.T.; Sampson, J.N.; Kitahara, C.M.; Keadle, S.K.; Arem, H.; De Gonzalez, A.B.; Hartge, P.; et al. Association of leisure-time physical activity with risk of 26 types of cancer in 1.44 million adults. JAMA Intern. Med. 2016, 176, 816–825. [Google Scholar] [CrossRef] [PubMed]
- Roberts, H.V. Using Twitter data in urban green space research: A case study and critical evaluation. Appl. Geogr. 2017, 81, 13–20. [Google Scholar] [CrossRef]
- Maroko, A.R.; Maantay, J.A.; Sohler, N.L.; Grady, K.L.; Arno, P.S. The complexities of measuring access to parks and physical activity sites in New York City: A quantitative and qualitative approach. Int. J. Health Geogr. 2009, 8, 34. [Google Scholar] [CrossRef] [PubMed]
- Mota, J.; Almeida, M.; Santos, P.; Ribeiro, J.C. Perceived neighborhood environments and physical activity in adolescents. Prev. Med. (Baltim) 2005, 41, 834–836. [Google Scholar] [CrossRef] [PubMed]
- Santos, M.P.; Page, A.S.; Cooper, A.R.; Ribeiro, J.C.; Mota, J. Perceptions of the built environment in relation to physical activity in Portuguese adolescents. Health Place 2009, 15, 548–552. [Google Scholar] [CrossRef] [PubMed]
- Handy, S.L.; Boarnet, M.G.; Ewing, R.; Killingsworth, R.E. How the built environment affects physical activity: Views from urban planning. Am. J. Prev. Med. 2002, 23, 64–73. [Google Scholar] [CrossRef]
- Berke, E.M.; Koepsell, T.D.; Moudon, A.V.; Hoskins, R.E.; Larson, E.B. Association of the built environment with physical activity and obesity in older persons. Am. J. Public Health 2007, 97, 486–492. [Google Scholar] [CrossRef] [PubMed]
- ESRI Data Classification Methods. Available online: http://pro.arcgis.com/en/pro-app/help/mapping/layer-properties/data-classification-methods.htm (accessed on 31 January 2017).
- Birch, C.P.D.; Oom, S.P.; Beecham, J.A. Rectangular and hexagonal grids used for observation, experiment and simulation in ecology. Ecol. Modell. 2007, 206, 347–359. [Google Scholar] [CrossRef]
- Zook, M. Small Stories in Big Data: Gaining Insights From Large Spatial Point Pattern Datasets. Cityscape A J. Policy Dev. Res. 2015, 17, 151–160. [Google Scholar]
- Feick, R.; Robertson, C. A multi-scale approach to exploring urban places in geotagged photographs. Comput. Environ. Urban Syst. 2015, 53, 96–109. [Google Scholar] [CrossRef]
- Schaudt, B.F.; Drysdale, R.L.S. Multiplicatively weighted crystal growth Voronoi diagrams (extended abstract). In Proceedings of the Seventh Annual Symposium on Computational Geometry—SCG ’91; North Conway, NH, USA, 10–12 June 1991; ACM Press: New York, NY, USA, 1991; pp. 214–223. [Google Scholar]
- Wang, J.; Kwan, M.-P.; Ma, L. Delimiting service area using adaptive crystal-growth Voronoi diagrams based on weighted planes: A case study in Haizhu District of Guangzhou in China. Appl. Geogr. 2014, 50, 108–119. [Google Scholar] [CrossRef]
- Lin, C.; Huang, I. A Crystal Growth Approach for Topographical Global Optimization. J. Glob. Optim. 1998, 13, 255–267. [Google Scholar] [CrossRef]
- Schönfelder, S.; Axhausen, K.W. Activity spaces: Measures of social exclusion? Transp. Policy 2003, 10, 273–286. [Google Scholar] [CrossRef]
- James, P.; Berrigan, D.; Hart, J.E.; Aaron Hipp, J.; Hoehner, C.M.; Kerr, J.; Major, J.M.; Oka, M.; Laden, F. Effects of buffer size and shape on associations between the built environment and energy balance. Health Place 2014, 27, 162–170. [Google Scholar] [CrossRef] [PubMed]
- Oliver, L.N.; Schuurman, N.; Hall, A.W. Comparing circular and network buffers to examine the influence of land use on walking for leisure and errands. Int. J. Health Geogr. 2007, 6, 41. [Google Scholar] [CrossRef] [PubMed]
- Zhao, P.; Kwan, M.P.; Zhou, S. The uncertain geographic context problem in the analysis of the relationships between obesity and the built environment in Guangzhou. Int. J. Environ. Res. Public Health 2018, 15, 308. [Google Scholar] [CrossRef] [PubMed]
- Chaix, B.; Kestens, Y.; Perchoux, C.; Karusisi, N.; Merlo, J.; Labadi, K. An Interactive Mapping Tool to Assess Individual Mobility Patterns in Neighborhood Studies. Am. J. Prev. Med. 2012, 43, 440–450. [Google Scholar] [CrossRef] [PubMed]
- Kestens, Y.; Lebel, A.; Chaix, B.; Clary, C.; Daniel, M.; Pampalon, R.; Theriault, M.; p Subramanian, S.V. Association between activity space exposure to food establishments and individual risk of overweight. PLoS ONE 2012, 7, e41418. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Shareck, M.; Kestens, Y.; Frohlich, K.L. Moving beyond the residential neighborhood to explore social inequalities in exposure to area-level disadvantage: Results from the Interdisciplinary Study on Inequalities in Smoking. Soc. Sci. Med. 2014, 108, 106–114. [Google Scholar] [CrossRef] [PubMed]
- Kestens, Y.; Wasfi, R.; Naud, A.; Chaix, B. “Contextualizing Context”: Reconciling Environmental Exposures, Social Networks, and Location Preferences in Health Research. Curr. Environ. Health Rep. 2017, 4, 51–60. [Google Scholar] [CrossRef] [PubMed]
Sociodemographic Variables | Percentage | |
---|---|---|
Gender | Male | 61.3% |
Female | 38.7% | |
Age | 18–30 | 17.2% |
31–40 | 17.2% | |
41–50 | 24.1% | |
51–65 | 41.4% | |
Race | White | 9.7% |
African American | 41.9% | |
Latino/Hispanic | 41.9% | |
Other | 6.5% | |
Education | Elementary School | 6.5% |
High School | 58.1% | |
College/University | 32.3% | |
Graduate School | 3.2% | |
Marital Status | Married | 17.9% |
Divorced | 14.3% | |
Single | 67.9% | |
Annual Income | Less than $10,000 | 58.1% |
$10,000–$24,999 | 19.4% | |
$25,000–$50,000 | 9.7% | |
$50,000–$99,000 | 9.7% | |
$100,000 or more | 3.2% |
Context Type | Cell Value | Average Moving Velocity | Growth Speed (Cells/Cycle) |
---|---|---|---|
Out of Research Area | 0 | - | 0 |
Restricted Area | 10 | - | 0 |
Water Bodies | 11 | - | 0 |
Woods | 12 | - | 0 |
Buildings | 13 | - | 0 |
Railway | 14 | - | 0 |
Expressway | 21 | About 35 miles/h | 12 |
Primary Road | 22 | About 25 miles/h | 8 |
Secondary Road | 23 | About 15 miles/h | 5 |
Tertiary Road | 24 | About 9 miles/h | 3 |
Pedestrian Trail | 30 | About 3 miles/h | 1 |
Walkable Area | 31 | About 3 miles/h | 1 |
Seed Points | 100 | About 3 miles/h | 1 |
Context Type | Cell Value | Average Moving Velocity | Growth Speed (Cells/Cycle) |
---|---|---|---|
Out of Research Area | 0 | - | 0 |
Restricted Area | 10 | - | 0 |
Water Bodies | 11 | - | 0 |
Woods | 12 | - | 0 |
Buildings | 13 | - | 0 |
Railway | 14 | - | 0 |
Expressway | 15 | - | 0 |
Metro Routes | 21 | About 18 miles/h | 6 |
Metro Stations | 22 | About 3 miles/h | 1 |
Bus Routes | 23 | About 9 miles/h | 3 |
Local Roads | 30 | About 3 miles/h | 1 |
Walkable Area | 31 | About 3 miles/h | 1 |
Seed Points | 100 | About 3 miles/h | 1 |
CCG | GTB | KDS | MCP | SDE1 | SDE2 | ||
---|---|---|---|---|---|---|---|
Person A | Area (km2) | 8.21 | 0.91 | 6.58 | 0.52 | 0.0373 | 0.15 |
%GTPC | 100% | 100% | 100% | 100% | 93.28% | 93.45% | |
Person B | Area (km2) | 11.74 | 31.14 | 66.66 | 65.52 | 12.704 | 50.82 |
%GTPC | 87.97% | 100% | 100% | 100% | 76.47% | 85.71% | |
Person C | Area (km2) | 11.89 | 19.14 | 81.20 | 105.10 | 7.71 | 30.83 |
%GTPC | 96.60% | 100% | 100% | 100% | 82.04% | 93.72% | |
Person D | Area (km2) | 18.76 | 28.92 | 101.28 | 109.16 | 39.12 | 156.48 |
%GTPC | 91.69% | 100% | 100% | 100% | 75.69% | 90.83% | |
Average | Area (km2) | 12.65 | 20.03 | 63.93 | 70.08 | 14.89 | 59.57 |
%GTPC | 94.04% | 100% | 100% | 100% | 81.87% | 90.93% |
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Wang, J.; Kwan, M.-P.; Chai, Y. An Innovative Context-Based Crystal-Growth Activity Space Method for Environmental Exposure Assessment: A Study Using GIS and GPS Trajectory Data Collected in Chicago. Int. J. Environ. Res. Public Health 2018, 15, 703. https://doi.org/10.3390/ijerph15040703
Wang J, Kwan M-P, Chai Y. An Innovative Context-Based Crystal-Growth Activity Space Method for Environmental Exposure Assessment: A Study Using GIS and GPS Trajectory Data Collected in Chicago. International Journal of Environmental Research and Public Health. 2018; 15(4):703. https://doi.org/10.3390/ijerph15040703
Chicago/Turabian StyleWang, Jue, Mei-Po Kwan, and Yanwei Chai. 2018. "An Innovative Context-Based Crystal-Growth Activity Space Method for Environmental Exposure Assessment: A Study Using GIS and GPS Trajectory Data Collected in Chicago" International Journal of Environmental Research and Public Health 15, no. 4: 703. https://doi.org/10.3390/ijerph15040703
APA StyleWang, J., Kwan, M. -P., & Chai, Y. (2018). An Innovative Context-Based Crystal-Growth Activity Space Method for Environmental Exposure Assessment: A Study Using GIS and GPS Trajectory Data Collected in Chicago. International Journal of Environmental Research and Public Health, 15(4), 703. https://doi.org/10.3390/ijerph15040703