Choropleth map

Abstract: Advances in remote sensing, dasymetric mapping techniques, and the ever-increasing availability of spatial datasets have enhanced global human population distribution databases. These datasets demonstrate an enormous improvement over the conventional use of choropleth maps to represent population distribution and are vital for analysis and planning purposes including humanitarian response, disease mapping, risk analysis, and evacuation modeling. Dasymetric mapping techniques have been employed to address spatial mismatch, but also to develop finer resolution population distributions in areas of the world where subnational census data are coarse or non-existent. One such implementation is the LandScan Global model which provides a 30 arc-second global population distribution based on ancillary datasets such as land cover, slope, proximity to roads, and settlement locations. This work will review the current state of the LandScan model, future innovations aimed at increasing spatial and demographic resolution, and situate LandScan within the landscape of other global population distribution datasets.

Abstract: Often when the topic of the accuracy of spatial databases arises one thinks in terms of the positional accuracy of the punctiform, linear, areal or volumetric features of the earth's surface. In many GIS applications, this type of focus is appropriate and necessary for the task at hand which may involve map overlay operations or terrain modelling in a natural resources context or may have legal implications in a cadastral database context. For most GIS applications in human geography, the research context involves the use of census data or data tabulated by census enumeration units. For these applications, which may involve dozens or even hundreds of data layers with the same polygonal boundaries (choropleth maps), the concept of the accuracy of the spatial database is quite different. This paper will briefly review the types of error on choropleth maps which are commonly held to be important, and introduce an overlooked component of choropleth map accuracy as the Small Number Problem. A short history of the Small Number Problem, and a review of various attempts to minimize it or solve it will be given with specific examples from epidemiologic, health care delivery and health care resource allocation contexts.

Abstract: Background Disparities in health outcomes across communities are a central concern in public health and epidemiology. Health disparities research often links differences in health outcomes to other social factors like income. Choropleth maps of health outcome rates show the geographical distribution of health outcomes. This paper illustrates the use of cumulative frequency map legends for visualizing how the health events are distributed in relation to social characteristics of community populations. The approach uses two graphs in the cumulative frequency legend to highlight the difference between the raw count of the health events and the raw count of the social characteristic like low income in the geographical areas of the map. The approach is applied to mapping publicly available data on low birth weight by town in Connecticut and Lyme disease incidence by town in Connecticut in relation to income. The steps involved in creating these legends are described in detail so that health analysts can adopt this approach.

Abstract: An essential purpose of choropleth maps is the visual perception of spatial patterns (such as the detection of hot spots or extreme values). This requires an effective and as intuitive as possible comparison of color values between different regions. Accordingly, a number of design requirements must be considered. Due to the lack of empirical evidence regarding some elementary design aspects, an online study with 260 participants was conducted. Three closely related effects were examined: the “dark-is-more bias” (i.e., the intuitive ranking of color lightness), the “area-size bias” (i.e., the neglect of small areas, since these are less dominant in perception than larger ones) and the “data-classification effect” (i.e., attention to data classification when interpreting spatial patterns). For each hypothesis, one or more maps in connection with single or multiple choice questions were presented. Users should detect extreme values, central tendencies or homogeneities of values as well as comment on their task solving certainty. In general, the hypotheses regarding the mentioned effects could be confirmed by statistical analysis. The results are used to derive conclusions and topics for future research. In particular, further comparative empirical studies are recommended to determine the best possible map types for given applications, also considering alternatives to choropleth maps.