Choropleth maps are a common form of online cartographic visualization. They reveal patterns in spatial distributions of a variable by associating colors with data values measured at areal units. Although this capability of pattern revelation has popularized the use of choropleth maps, existing methods for their online delivery are limited in supporting dynamic map generation from large areal data. This limitation has become increasingly problematic in online choropleth mapping as access to small area statistics, such as highresolution census data and real-time aggregates of geospatial data streams, has never been easier due to advances in geospatial web technologies. The current literature shows that the challenge of large areal data can be mitigated through tiled maps where pre-processed map data are hierarchically partitioned into tiny rectangular images or map chunks for efficient data transmission. Various approaches have emerged lately to enable this tile-based choropleth mapping, yet little empirical evidence exists on their ability to handle spatial data with large numbers of areal units, thus complicating technical decision making in the development of online choropleth mapping applications. To fill this knowledge gap, this dissertation study conducts a scalability evaluation of three tile-based methods discussed in the literature: raster, scalable vector graphics (SVG), and HTML5 Canvas. For the evaluation, the study develops two test applications, generates map tiles from five different boundaries of the United States, and measures the response times of the applications under multiple test operations. While specific to the experimental setups of the study, the evaluation results show that the raster method scales better across various types of user interaction than the other methods. Empirical evidence also points to the superior scalability of Canvas to SVG in dynamic rendering of vector tiles, but not necessarily for partial updates of the tiles. These findings indicate that the raster method is better suited for dynamic choropleth rendering from large areal data, while Canvas would be more suitable than SVG when such rendering frequently involves complete updates of vector shapes.

Tile-based methods for online choropleth mapping: a scalability evaluation

Spatial profiling of community food security data can help the targeting of geographic areas and populations most vulnerable to food insecurity. While multiple poverty mapping systems support spatial profiling, they often lack capabilities to disseminate mapping results to a wide range of audiences and to spatially link qualitative data to quantitative analysis. To address these limitations, this study presents a web mapping framework which integrates a variety of publicly available software tools to enable spatial exploration of both quantitative and qualitative data. Specifically, our framework allows online choropleth mapping and thematic data exploration through a mixture of free mapping Application Programming Interfaces (APIs) and open source software tools for spatial data processing and desktop-like user interfaces. The study demonstrates this framework by developing a web prototype for informing food insecurity issues in Bogota, Colombia. The prototype implementation reveals that the proposed framework facilitates the development of scalable and functionally-extensible mapping systems and the identification of community-specific food insecurity problems (e.g., food kitchens inaccessible from workplaces of low-income residents). This suggests that web-based cartographic visualization using publicly available software tools can be useful for spatial examination of community food insecurity as well as for cost-effective distribution of the resulting map information.

Integrating publicly available web mapping tools for cartographic visualization of community food insecurity: a prototype

In this paper we explore the use of state-of-the-art web technologies to facilitate access to advanced spatial analytical software tools. The specific focus is on the development of web services to disseminate advanced algorithms for spatial weights manipulation. The core functionality is contained in an open source library for spatial analysis called PySAL. Among others, this library provides a variety of methods for creating, transforming, and converting spatial weights. Web services using the Simple Object Access Protocol (SOAP) are created to support machine or developer interfaces to those methods via the Internet. We also develop a web application client for direct access to the weights tools via a browser. The system is illustrated with an example use case in which spatial weights operations are applied to epidemiological data for Ohio counties.

/pdf/manipulation-of-spatial-weights-using-web-services-2mlfgyhduy.pdf

Manipulation of spatial weights using web services

Spatial analytical tools are mostly provided in a desktop environment, which tends to restrict user access to the tools. This project intends to exploit up-to-date web technologies to extend user accessibility to spatial analytic tools. The first step is to develop web services for widely used spatial analysis such as spatial weights manipulation and provide easy-to-use web-based user interface to the services. Users can create, transform, and convert spatial weights for their data sets on web browsers without installing any specialized software.

Dynamic Manipulation of Spatial Weights Using Web Services

Reducing latency in presenting map interfaces at client devices

In this paper we propose a solution to several key limitations of current web based mapping systems: slow rendering speeds and the restriction of online map viewing to a small number of areal units as well as a limited number of users. Our approach is implemented as a Scalable Tile Map Service that distributes dynamic choropleth maps in real-time through a new caching methodology. This new Map Service lays the foundation for advances in web based applications reliant on dynamic map rendering such as emergency management systems and interactive exploratory spatial data analysis. We present the results of an empirical illustration in which this new methodology is used to facilitate collaborative decision making by visualizing spatial outcomes of simulation results on the fly.

A Scalable Tile Map Service for Distributing Dynamic Choropleth Maps

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A Scalable Tile Map Service for Distributing Dynamic Choropleth Maps