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Uncertain geographic context problem

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The uncertain geographic context problem or UGCoP is a source of statistical bias that can significantly impact the results of spatial analysis when dealing with aggregate data.[1][2][3] The UGCoP is very closely related to the Modifiable areal unit problem (MAUP), and like the MAUP, arises from how we divide the land into areal units.[4][5] It is caused by the difficulty, or impossibility, of understanding how phenomena under investigation (such as people within a census tract) in different enumeration units interact between enumeration units, and outside of a study area over time.[1][6] It is particularly important to consider the UGCoP within the discipline of time geography, where phenomena under investigation can move between spatial enumeration units during the study period.[2] Examples of research that needs to consider the UGCoP include food access and human mobility.[7][8]

Schematic and example of a space-time prism using transit network data: On the right is a schematic diagram of a space-time prism, and on the left is a map of the potential path area for two different time budgets.[9]

The uncertain geographic context problem, or UGCoP, was first coined by Dr. Mei-Po Kwan in 2012.[1][2] The problem is highly related to the ecological fallacy, edge effect, and Modifiable areal unit problem (MAUP) in that, it relates to aggregate units as they apply to individuals.[5] The crux of the problem is that the boundaries we use for aggregation are arbitrary and may not represent the actual neighborhood of the individuals within them.[4][5] While a particular enumeration unit, such as a census tract, contains a person's location, they may cross its boundaries to work, go to school, and shop in completely different areas.[10][11] Thus, the geographic phenomena under investigation extends beyond the delineated boundary .[6][12][13] Different individuals, or groups may have completely different activity spaces, making an enumeration unit that is relevant for one person meaningless to another.[7][14] For example, a map that aggregates people by school districts will be more meaningful when studying a population of students than the general population.[15] Traditional spatial analysis, by necessity, treats each discrete areal unit as a self-contained neighborhood and does not consider the daily activity of crossing the boundaries.[1][2]

Implications

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The UGCoP has further implications when considering the area outside of a study area. Tobler's second law of geography states, "the phenomenon external to a geographic area of interest affects what goes on inside."[16][12] As a study area is often a subset of the planet, data on the edges of the study area will be excluded.[17] If the boundary demarcating the study area is permeable to travel, then the phenomena under investigation within it may extend beyond, and be impacted by, forces excluded from the analysis.[6][18] This uncertainty contributes to the UGCoP.[1][2]

All maps are wrong, and a cartographer must ensure that their maps' limitations are well documented to avoid misleading the users.[19] With modern technology, there is an emphasis on individual-level data and understanding how individuals interact with their environment.[5][8] When making maps with this individual-level data, the UGCoP is one source of bias that can impact the results of an analysis.[1] When these results inform policy, they can have real world ramifications.[19]

The UGCoP is particularly important when understanding food access and human mobility.[6][7]

Suggested solutions

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Geographic information systems, along with technologies that can monitor the position of individuals in real time, are possible methods for addressing the UGCoP.[2] These technologies allow scientists to analyze and visualize the 3D space-time path of people moving through a study area, and better understand their actual activity space.[2] Web GIS has also been employed to address the UGCoP by allowing researchers to better contextualize subjects' real and perceived activity space.[2][15] These technologies have helped to address the problem by moving away from aggregate data and introducing a temporal component to the modeling of subject activity.[2][15]

See also

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References

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  1. ^ a b c d e f Kwan, Mei-Po (2012). "The Uncertain Geographic Context Problem". Annals of the Association of American Geographers. 102 (5): 958–968. doi:10.1080/00045608.2012.687349. S2CID 52024592.
  2. ^ a b c d e f g h i Kwan, Mei-Po (2012). "How GIS can help address the uncertain geographic context problem in social science research". Annals of GIS. 18 (4): 245–255. Bibcode:2012AnGIS..18..245K. doi:10.1080/19475683.2012.727867. S2CID 13215965. Retrieved 4 January 2023.
  3. ^ Matthews, Stephen A. (2017). International Encyclopedia of Geography: People, the Earth, Environment and Technology: Uncertain Geographic Context Problem. Wiley. doi:10.1002/9781118786352.wbieg0599.
  4. ^ a b Openshaw, Stan (1983). The Modifiable Aerial Unit Problem (PDF). GeoBooks. ISBN 0-86094-134-5.
  5. ^ a b c d Chen, Xiang; Ye, Xinyue; Widener, Michael J.; Delmelle, Eric; Kwan, Mei-Po; Shannon, Jerry; Racine, Racine F.; Adams, Aaron; Liang, Lu; Peng, Jia (27 December 2022). "A systematic review of the modifiable areal unit problem (MAUP) in community food environmental research". Urban Informatics. 1 (1): 22. Bibcode:2022UrbIn...1...22C. doi:10.1007/s44212-022-00021-1. S2CID 255206315.
  6. ^ a b c d Gao, Fei; Kihal, Wahida; Meur, Nolwenn Le; Souris, Marc; Deguen, Séverine (2017). "Does the edge effect impact on the measure of spatial accessibility to healthcare providers?". International Journal of Health Geographics. 16 (1): 46. doi:10.1186/s12942-017-0119-3. PMC 5725922. PMID 29228961.
  7. ^ a b c Chen, Xiang; Kwan, Mei-Po (2015). "Contextual Uncertainties, Human Mobility, and Perceived Food Environment: The Uncertain Geographic Context Problem in Food Access Research". American Journal of Public Health. 105 (9): 1734–1737. doi:10.2105/AJPH.2015.302792. PMC 4539815. PMID 26180982.
  8. ^ a b Zhou, Xingang; Liu, Jianzheng; Gar On Yeh, Anthony; Yue, Yang; Li, Weifeng (2015). "The Uncertain Geographic Context Problem in Identifying Activity Centers Using Mobile Phone Positioning Data and Point of Interest Data". Advances in Spatial Data Handling and Analysis. Advances in Geographic Information Science. pp. 107–119. doi:10.1007/978-3-319-19950-4_7. ISBN 978-3-319-19949-8.
  9. ^ Allen, Jeff (2019). "Using Network Segments in the Visualization of Urban Isochrones". Cartographica: The International Journal for Geographic Information and Geovisualization. 53 (4): 262–270. doi:10.3138/cart.53.4.2018-0013. S2CID 133986477.
  10. ^ Zhao, Pengxiang; Kwan, Mei-Po; Zhou, Suhong (2018). "The Uncertain Geographic Context Problem in the Analysis of the Relationships between Obesity and the Built Environment in Guangzhou". International Journal of Environmental Research and Public Health. 15 (2): 308. doi:10.3390/ijerph15020308. PMC 5858377. PMID 29439392.
  11. ^ Zhou, Xingang; Liu, Jianzheng; Yeh, Anthony Gar On; Yue, Yang; Li, Weifeng (2015). "The Uncertain Geographic Context Problem in Identifying Activity Centers Using Mobile Phone Positioning Data and Point of Interest Data". Advances in Spatial Data Handling and Analysis. Advances in Geographic Information Science. pp. 107–119. doi:10.1007/978-3-319-19950-4_7. ISBN 978-3-319-19949-8. Retrieved 22 January 2023.
  12. ^ a b Tobler, Waldo (2004). "On the First Law of Geography: A Reply". Annals of the Association of American Geographers. 94 (2): 304–310. doi:10.1111/j.1467-8306.2004.09402009.x. S2CID 33201684. Retrieved 10 March 2022.
  13. ^ Salvo, Deborah; Durand, Casey P.; Dooley, Erin E.; Johnson, Ashleigh M.; Oluyomi, Abiodun; Gabriel, Kelley P.; Van Dan Berg, Alexandra; Perez, Adriana; Kohl, Harold W. (June 2019). "Reducing the Uncertain Geographic Context Problem in Physical Activity Research: The Houston TRAIN Study". Medicine & Science in Sports & Exercise. 51 (6S): 437. doi:10.1249/01.mss.0000561808.49993.53. S2CID 198375226.
  14. ^ Thrift, Nigel (1977). An Introduction to Time-Geography (PDF). Geo Abstracts, University of East Anglia. ISBN 0-90224667-4.
  15. ^ a b c Shmool, Jessie L.; Johnson, Isaac L.; Dodson, Zan M.; Keene, Robert; Gradeck, Robert; Beach, Scott R.; Clougherty, Jane E. (2018). "Developing a GIS-Based Online Survey Instrument to Elicit Perceived Neighborhood Geographies to Address the Uncertain Geographic Context Problem". The Professional Geographer. 70 (3): 423–433. Bibcode:2018ProfG..70..423S. doi:10.1080/00330124.2017.1416299. S2CID 135366460. Retrieved 22 January 2023.
  16. ^ Tobler, Waldo (1999). "Linear pycnophylactic reallocation comment on a paper by D. Martin". International Journal of Geographical Information Science. 13 (1): 85–90. Bibcode:1999IJGIS..13...85T. doi:10.1080/136588199241472.
  17. ^ Franch-Pardo, Ivan; Napoletano, Brian M.; Rosete-Verges, Fernando; Billa, Lawal (2020). "Spatial analysis and GIS in the study of COVID-19. A review". Sci Total Environ. 739: 140033. Bibcode:2020ScTEn.73940033F. doi:10.1016/j.scitotenv.2020.140033. PMC 7832930. PMID 32534320. S2CID 219637515.
  18. ^ Ge, Haoxuan; Wang, Jue (January 2023). "Spatial Non-Stationarity Effects of Unhealthy Food Environments and Green Spaces for Type-2 Diabetes in Toronto". Sustainability. 15 (3): 1762. doi:10.3390/su15031762.
  19. ^ a b Monmonier, Mark (10 April 2018). How to lie with maps (3 ed.). University of Chicago Press. ISBN 978-0226435923.
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