Abstract: In this article, we report on our experiences with refactoring a spatial analysis library to support parallelization. Python Spatial Analysis Library PySAL is a library of spatial analytical functions written in the open-source language, Python. As part of a larger scale effort toward developing cyberinfrastructure of GIScience, we examine the particular case of choropleth map classification through alternative parallel implementations of the Fisher-Jenks optimal classification method using a multi-core, single desktop environment. The implementations rely on three different parallel Python libraries, PyOpenCL, Parallel Python, PP and Multiprocessing. Our results point to the dominance of the CPU-based Parallel Python and Multiprocessing implementations over the Graphical Processing Unit GPU-based PyOpenCL approach.

Abstract: In mapping population characteristics, data are usually portrayed as accurate without error. However, many population datasets provide estimates derived from surveys or samples, and a certain level of uncertainty is associated with each estimate. Ignoring estimated uncertainty information in mapping may produce misleading maps and generate spurious spatial patterns. In this paper, we introduce a measure of separability to indicate the likelihood that units assigned to different classes are truly different statistically. A series of map symbolization techniques is proposed to communicate class separability to the cartographer or map reader, and presented in four series of maps of American Community Survey data on median household income for Iowa counties. These map series illustrate several different techniques: a legend designed to communicate separability between classes, graduated line symbols to communicate separability between individual map units, and a color scheme in which perceptual color differen...

Abstract: In this chapter we introduce an improved parallel optimal choropleth map classification algorithm to support spatial analysis. This work contributes to the development of a Distributed Geospatial CyberInfrastructure and offers an implementation of the Fisher-Jenks optimal classification method suitable for multi-core desktop environments. We provide a description of both a single-core vectorized implementation and a parallelized implementation. Our results show that single core vectorization alone provides computational speedups compared to previous parallel implementations and that a combined, parallel and vectorized, implementation offers significant speed improvements.

Abstract: Today the scientific world shows great interest in visual culture. This is a transversal phenomenon to national disciplines and contexts, given that the same tendency to reorient knowledge and organize it around visual paradigms is to be found in different areas of contemporary western thought. In this reevaluation of the visual culture is collocated the present rediscovery of the heuristic value of the geographical map, the use of which today has undoubtedly crossed the narrow ambit of geographical studies to find growing use with specialists of other disciplines too, attracted by the capacity of maps to synthetically highlight significant spatial correlations of the phenomena being studied. Nonetheless, like every scientific instrument it comes up with processes of adaptation to the changing scientific contexts, just as the traditional Cartesian configuration of the map needs to be updated in order to be in line with the new post-modern scientific paradigms and with the reality of the contemporary world. The analysis of these dynamics of contemporary cartography is here traced back to the case of a specific cartographic method: the choropleth map, or mosaic diagram. This represents one of the most fortunate intuitions in the history of cartography, introduced by Charles Dupin in 1826 and is an exemplary application of positivist scientific thought. Even though the introduction of the choropleth map was the start of a fruitful period for the subject with the ceaseless development of statistical cartography, today it seems inadequate for the understanding of the multi-faceted contemporary reality. After highlighting the reasons for the success of the choropleth map, this paper makes a number of considerations on its present limitations and the need, as far as cartographical studies are concerned, to press on beyond the frontier of innovation. In particular, stimulating starting points to reason on the future of the geographical map are offered by the recent success of the anamorphic maps.

Abstract: Fig. 1. Three applications created with Unfolding: An animated map showing subways in Boston (left), an interactive choropleth map showing population density (middle), and a visualization showing public transit trips in Singapore for a multitouch tabletop (right). Abstract—Many thematic maps and geovisualizations nowadays are being created by designers, journalists, and other non cartographers. Yet, with existing tools it is often difficult to create interactive data visualizations tailored for a particular domain or a specific dataset. We present Unfolding, a library to simplify the creation of interactive maps and geovisualizations. Unfolding provides an API to quickly create and customize visualization applications. In this paper, we introduce the design and functionality of our library. We demonstrate its usability through a collection of examples, and confirm the apparent need of such map library by describing its acceptance in the community.

Abstract: A visual demonstration of the benefits behind using cartograms, over the traditional choropleth map. Poster shows iGeology app activity in cartogram format.

Abstract: Spatial statistics allows to assess geographic distribution of phenomena – its concentration, magnitude and orientation of dispersion as well regularity or trends in occurrence within a space. The paper presents adaptation of point-based methods to measure spatial distribution of areal phenomena that concern agriculture: area of agricultural land, area of fertile agricultural land and soil pH. The source data in a form of chorochromatic maps (e.g. a vector soil map) are processed to 1 x 1 km grid data with use of the algorithm created in Model Builder. The research area – Lower Silesia – characterizes various environmental conditions that results in changeability of agricultural land productivity. Spatial statistics performed for a whole region would bring only global information on spatial distribution. Hence the Authors propose to conduct analysis within subareas that depict local changeability of studied phenomena. As the research is conducted in agricultural context, the subareas of similar agricultural land areas are created regarding the administrative units. Spatial distribution is described by: mean centre, standard distance and standard deviational ellipse. All three measures are weighted by a variable (i.e. the intensity of the phenomenon) as spatial distribution is not only about location, but the value of the phenomenon in particular location is important. Measures of spatial distribution drawn on a map yields clear and usually easy to interpret information on spatial character of a phenomenon. In some cases it may be useful to present these qualitative characteristics complemented with another type of cartographic visualization (e.g. a choropleth map. This paper presents maps about the application of spatial distribution measures into assessment of agricultural land productivity in the research.