Abstract: spmap is aimed at visualizing several kinds of spatial data, and is particularly suited for drawing thematic maps and displaying the results of spatial data analyses. Proper specification of spmap options and suboptions, combined with the availability of properly formatted spatial data, allows the user to draw several kinds of maps, including choropleth maps, proportional symbol maps, pin maps, pie chart maps, and noncontiguous area cartograms. spmap completely supersedes its predecessor tmap.

Abstract: Choropleth maps are often used to visualize the spatial distribution of information collected for enumeration units. Such maps, however, are normally produced without considering the effect of uncertainty associated with data, which can contribute to incorrect interpretation. The purpose of this paper is to develop a method that can be used to evaluate the classification robustness of choropleth maps when the attribute uncertainty associated with the data is known or can be estimated. We first develop a measure to indicate the robustness of classification schemes. We then design a set of experiments to examine the robustness of different choropleth map classifications under various levels and types of uncertainty. Our experiments suggest that the robustness of a choropleth classification scheme is a function of uncertainty and the number of classes used. Increases in data uncertainty will decrease map robustness. However, it is possible to increase map robustness by choosing a smaller number of classes. We also discuss a visualization approach that can be used to display the classification robustness of each enumeration unit within a choropleth map.

Abstract: In the beginning of the 19th century, thematic cartography was enriched with subjects related to the human world. In particular, the enthusiasm for statistics put on the foreground several topics drawn from what we would call today “social sciences”: demography, political economics, and moral statistics. In this context, most of the methods of quantitative mapping were invented within a short period, between 1826 and 1850. This article deals with the first and the most popular among these methods, the shaded map, which is credited to the Frenchman Charles Dupin. We explore the circumstances in which it was imagined, and its modes of diffusion in European cartography. Dupin’s shaded map became quickly famous and was imitated by scholars in the field of vital and moral statistics, then of medicine and anthropology. We suggest that these thematic maps were not neutral illustrations, but were primarily conceived as arguments in scientific or ideological debates, and that their sign system played a major role in their persuasive effect.

Abstract: Traditional regression analysis describes a modelled relationship between a dependent variable and a set of independent variables. When applied to spatial data, the regression analysis often assumes that the modelled relationship is stationary over space and produces a global model that is supposed to describe the relationship at every location in the study area. This can be misleading, as the relationships in spatial data are often intrinsically different across space. One of the spatial statistical methods that attempts to solve this problem and explain local variation in complex relationships is Geographically Weighted Regression – GWR (Fotheringham et al. 2000). In a global regression model the dependent variable is often modelled as a linear combination of independent variables, where a parameter belonging to each variable is assumed to be stationary over the whole area (i.e. the model returns exactly one constant value for each parameter). GWR extends this framework by dropping the stationarity assumption: the parameters are assumed to be continuous functions of location. The result of the GWR analysis are continuous localised parameter estimate surfaces, which describe the geography of the parameter space (Fotheringham et al. 2002). The variability and behaviour of the parameters are influenced by the underlying spatial processes. In order to provide insight into the processes, the parameter surfaces can be visualised – this is usually done by univariate mapping, such as producing a choropleth map of each separate parameter surface. These visualisations serve as an informal inference tool (Fotheringham and Brunsdon 2004) for interpretation of the GWR results. If the goal is to discover complex spatial and other multivariate patterns in the parameter space and form new hypotheses about the spatial variability of the parameters, simple univariate visualisations of the parameters might not be adequate. Instead, the parameter estimates can be regarded as a multivariate dataset, which can be examined in an integrated automatic-visual exploratory environment, as for example the one presented in figure 1, consisting of a Self-Organising Map and several multivariate and spatial visualisations (Demsar 2007). The exploratory approach can help answer questions such as “Do there exist areas of stability where all the parameters keep relatively constant values?”, or “Are there any predominant groupings of parameters that behave in a similar way everywhere in the area of investigation?”. Such analysis can also raise new questions about the spatial distribution of parameters. The combined statistical-exploratory approach offers new insights into the results of the statistical method that would otherwise remain unnoticed and thereby facilitates analytical reasoning, which is one of the goals of visual and geovisual analytics (NVAC 2007).

Abstract: In this paper, an approach to simulate the spatial distribution of urban population is proposed using urban land use and population statistical data through the geographical information systems (GIS). Then, the spatial population distribution of Urumqi as a case is simulated by the approach mentioned above and its varying patterns are analyzed by the raster population surface. As a result, producing raster population surface is more accurate and natural than the traditional choropleth map of population density. Concerning the spatial population distribution of Urumqi, the population density declines from south to north and the population distribution mainly presents “T-type”, the population distribution presents multi-centre agglomeration and the population distribution of the districts shows different features. The population density varies significantly with the increase in the distance from central business district (CBD). Finally, it is found in this paper that the development history of dist...

Abstract: spmap is aimed at visualizing several kinds of spatial data, and is particularly suited for drawing thematic maps and displaying the results of spatial data analyses. Proper specification of spmap options and suboptions, combined with the availability of properly formatted spatial data, allows the user to draw several kinds of maps, including choropleth maps, proportional symbol maps, pin maps, pie chart maps, and noncontiguous area cartograms. spmap completely supersedes its predecessor tmap.

Abstract: The potential of unclassed animated choropleth maps as a solution to false patterns of geographic change arising from data classification is investigated. Old concerns about unclassed choropleth maps may be mitigated through map interactivity that offers four advantages over traditional data legends, and previous insights from testing static choropleth maps do not necessarily translate to animated cartography. Data from user testing revealed unclassed animated choropleth maps neither help nor hurt the ability of map readers to understand patterns of geographic change. However, the unclassed map (1) appeared 'less jumpy' to participants and was perceived to run at a slower pace (despite running at the same number of frames per second), and (2) subtle geographic shifts (e.g., seasonal unemployment cycles) were more readily noticed on the unclassed maps. Preliminary results also suggest classed data emphasise stability over time – while their unclassed counterparts improve our ability to see changes...