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]

Contents

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. Space-time prism 1500x1125.png
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.

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

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

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

Related Research Articles

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Waldo Rudolph Tobler was an American-Swiss geographer and cartographer. Tobler is regarded as one of the most influential geographers and cartographers of the late 20th century and early 21st century. He is most well known for coining what has come to be referred to as Tobler's first law of geography. He also coined what has come to be referred to as Tobler's second law of geography.

<span class="mw-page-title-main">Choropleth map</span> Type of data visualization for geographic regions

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<span class="mw-page-title-main">Tobler's first law of geography</span> The first of several proposed laws of geography

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<span class="mw-page-title-main">Spatial analysis</span> Formal techniques which study entities using their topological, geometric, or geographic properties

Spatial analysis is any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques using different analytic approaches, especially spatial statistics. It may be applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, or to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is geospatial analysis, the technique applied to structures at the human scale, most notably in the analysis of geographic data. It may also be applied to genomics, as in transcriptomics data.

<span class="mw-page-title-main">Modifiable areal unit problem</span> Source of statistical bias

The modifiable areal unit problem (MAUP) is a source of statistical bias that can significantly impact the results of statistical hypothesis tests. MAUP affects results when point-based measures of spatial phenomena are aggregated into spatial partitions or areal units as in, for example, population density or illness rates. The resulting summary values are influenced by both the shape and scale of the aggregation unit.

<span class="mw-page-title-main">Thematic map</span> Type of map that visualizes data

A thematic map is a type of map that portrays the geographic pattern of a particular subject matter (theme) in a geographic area. This usually involves the use of map symbols to visualize selected properties of geographic features that are not naturally visible, such as temperature, language, or population. In this, they contrast with general reference maps, which focus on the location of a diverse set of physical features, such as rivers, roads, and buildings. Alternative names have been suggested for this class, such as special-subject or special-purpose maps, statistical maps, or distribution maps, but these have generally fallen out of common usage. Thematic mapping is closely allied with the field of Geovisualization.

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<span class="mw-page-title-main">Field (geography)</span> Property that varies over space

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<span class="mw-page-title-main">Mei-Po Kwan</span> Geographer

Mei-Po Kwan is a geographer known for her research contributions in Geographic Information Science, and human geography, particularly as they apply to time geography and human mobility. She is the Choh-Ming Li Professor of Geography and Resource Management at The Chinese University of Hong Kong (CUHK), Director of the Institute of Space and Earth Information Science (ISEIS) of CUHK, Director of the Institute of Future Cities of CUHK, and Head of Chung Chi College of CUHK.

<span class="mw-page-title-main">Tobler's second law of geography</span> One of several proposed laws of geography

The second law of geography, according to Waldo Tobler, is "the phenomenon external to a geographic area of interest affects what goes on inside." This is an extension of his first. He first published it in 1999 in reply to a paper titled "Linear pycnophylactic reallocation comment on a paper by D. Martin" and then again in response to criticism of his first law of geography titled "On the First Law of Geography: A Reply." Much of this criticism was centered on the question of if laws were meaningful in geography or any of the social sciences. In this document, Tobler proposed his second law while recognizing others have proposed other concepts to fill the role of 2nd law. Tobler asserted that this phenomenon is common enough to warrant the title of 2nd law of geography. Unlike Tobler's first law of geography, which is relatively well accepted among geographers, there are a few contenders for the title of the second law of geography. Tobler's second law of geography is less well known but still has profound implications for geography and spatial analysis.

<span class="mw-page-title-main">Arbia's law of geography</span> One of several proposed laws of geography

Arbia's law of geography states, "Everything is related to everything else, but things observed at a coarse spatial resolution are more related than things observed at a finer resolution." Originally proposed as the 2nd law of geography, this is one of several laws competing for that title. Because of this, Arbia's law is sometimes referred to as the second law of geography, or Arbia's second law of geography.

<span class="mw-page-title-main">Modifiable temporal unit problem</span> Source of statistical bias

The Modified Temporal Unit Problem (MTUP) is a source of statistical bias that occurs in time series and spatial analysis when using temporal data that has been aggregated into temporal units. In such cases, choosing a temporal unit can affect the analysis results and lead to inconsistencies or errors in statistical hypothesis testing.

<span class="mw-page-title-main">Qualitative geography</span> Subfield of geographic methods

Qualitative geography is a subfield and methodological approach to geography focusing on nominal data, descriptive information, and the subjective and interpretive aspects of how humans experience and perceive the world. Often, it is concerned with understanding the lived experiences of individuals and groups and the social, cultural, and political contexts in which those experiences occur. Thus, qualitative geography is traditionally placed under the branch of human geography; however, technical geographers are increasingly directing their methods toward interpreting, visualizing, and understanding qualitative datasets, and physical geographers employ nominal qualitative data as well as quanitative. Furthermore, there is increased interest in applying approaches and methods that are generally viewed as more qualitative in nature to physical geography, such as in critical physical geography. While qualitative geography is often viewed as the opposite of quantitative geography, the two sets of techniques are increasingly used to complement each other. Qualitative research can be employed in the scientific process to start the observation process, determine variables to include in research, validate results, and contextualize the results of quantitative research through mixed-methods approaches.

The neighborhood effect averaging problem or NEAP delves into the challenges associated with understanding the influence of aggregating neighborhood-level phenomena on individuals when mobility-dependent exposures influence the phenomena. The problem confounds the neighbourhood effect, which suggests that a person's neighborhood impacts their individual characteristics, such as health. It relates to the boundary problem, in that delineated neighborhoods used for analysis may not fully account for an individual's activity space if the borders are permeable, and individual mobility crosses the boundaries. The term was first coined by Mei-Po Kwan in the peer-reviewed journal "International Journal of Environmental Research and Public Health" in 2018.

<span class="mw-page-title-main">Waldo Tobler bibliography</span> Geographer Waldo Toblers publications

Waldo Tobler's publications span between 1957 and 2017, with his most productive year being 1973. Despite retirement in 1994, he continued to be involved with research for the remainder of his life. Most of his publications consist of peer-reviewed journals, without single-issue textbooks or monographs, and the quantity of publications is noted as being unremarkable compared to modern geographers. Many of his works are foundational to modern geography and cartography, and still frequently cited in modern publications, including the first paper on using computers in cartography, the establishment of analytical cartography, and coining Tobler's first and second laws of geography. His work covered a wide range of topics, with many of his papers considered to be "cartographic classics", that serve as required reading for both graduate and undergraduate students.

References

  1. 1 2 3 4 5 6 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. 1 2 3 4 5 6 7 8 9 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. 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. doi:10.1002/9781118786352.wbieg0599.
  4. 1 2 Openshaw, Stan (1983). The Modifiable Aerial Unit Problem (PDF). GeoBooks. ISBN   0-86094-134-5.
  5. 1 2 3 4 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. doi: 10.1007/s44212-022-00021-1 . S2CID   255206315.
  6. 1 2 3 4 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. 1 2 3 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. 1 2 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. 1 2 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. 1 2 3 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. 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. 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.739n0033F. 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. 1 2 Monmonier, Mark (10 April 2018). How to lie with maps (3 ed.). University of Chicago Press. ISBN   978-0226435923.
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