The uncertain geographic context problem (UGCoP) is a source of statistical bias that can significantly impact the results of spatial analysis when dealing with aggregate data. 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. 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. 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. Examples of research that needs to consider the UGCoP include food access and human mobility.
The uncertain geographic context problem, or UGCoP, was first coined by Dr. Mei-Po Kwan in 2012. 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. 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. 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. Thus, the geographic phenomena under investigation extends beyond the delineated boundary . Different individuals,or groups may have completely different activity spaces, making an enumeration unit that is relevant for one person meaningless to another. For example, a map that aggregates people by school districts will be more meaningful when studying a population of students than the general population. 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.
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Technical geography is the branch of geography that involves using, studying, and creating tools to obtain, analyze, interpret, understand, and communicate spatial information. The other branches, most commonly limited to human geography and physical geography, can usually apply the concepts and techniques of technical geography. However, the methods and theory are distinct, and a technical geographer may be more concerned with the technological and theoretical concepts than the nature of the data.
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 (e.g., days, months, years) can affect the analysis results and lead to inconsistencies or errors in statistical hypothesis testing. The MTUP is closely related to the modifiable areal unit problem or MAUP, in that they both relate to the scale of analysis and the issue of choosing an appropriate analysis.
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.
We focus on distinctive data-driven measures of the fate of ongoing epidemics. The relevance of our pursuit is suggested by recent results proving that the short-term temporal evolution of infection spread is described by an epidemicity index related to th ...
Visual exploration of spatial data relies heavily on spatial aggregation queries that slice and summarize the data over different regions. These queries comprise computationally-intensive point-in-polygon tests that associate data points to polygonal regio ...
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Introduction Evidence suggests that the built environment can influence the intensity of physical activity. However, despite the importance of the geographic context, most of the studies do not consider the spatial framework of this association. We aimed t ...