Computers, Environment and Urban Systems, Volume 35, Issue 3, May 2011, Pages 192-207
Brian Tomaszewski, Justine Blanford, Kevin Ross, Scott Pezanowski, and Alan M. MacEachren
- We present the SensePlace analytical environment.
- SensePlace advances Sensemaking and Geovisual Analytics research.
- We present analysis results centered on measles epidemics in Niger conducted using SensePlace.
“This paper reports on the development and application of strategies and tools for geographic information seeking and knowledge building that leverages unstructured text resources found on the web. Geographic knowledge building from unstructured web sources starts with web document foraging during which the quantity, scope and diversity of web-based information create incredible cognitive burdens on an analyst’s or researcher’s ability to judge information relevancy. Determining information relevancy is ultimately a process of sensemaking. In this paper, we present our research on visually supporting web document foraging and sensemaking. In particular, we present the Sense-of-Place (SensePlace) analytic environment. The scientific goal of SensePlace is to visually and computationally support analyst sensemaking with text artifacts that have potential place, time, and thematic relevance to an analytical problem through identification and visual highlighting of named entities (people, places, times, and organizations) in documents, automated inference to determine document relevance using stored knowledge, and a visual interface with coupled geographic map, timeline, and concept graph displays that are used to contextualize the contexts of potentially relevant documents. We present the results of a case study analysis using SensePlace to uncover potential population migration, geopolitical, and other infectious disease dynamics drivers for measles and other epidemics in Niger. Our analysis allowed us to demonstrate how our approach can support analysis of complex situations along (a) multi-scale geographic dimensions (i.e., vaccine coverage areas), (b) temporal dimensions (i.e., seasonal population movement and migrations), and (c) diverse thematic dimensions (effects of political upheaval, food security, transient movement, etc.).”
International Journal of Geographical Information Science, Volume 25, Issue 7, 2011
Bin Jiang and Xintao Liu
“In this article, we introduce a novel approach to computing the fewest-turn map directions or routes based on the concept of natural roads. Natural roads are joined road segments that perceptually constitute good continuity. This approach relies on the connectivity of natural roads rather than that of road segments for computing routes or map directions. Because of this, the derived routes possess the fewest turns. However, what we intend to achieve are the routes that not only possess the fewest turns but are also as short as possible. This kind of map direction is more effective and favored by people because they bear less cognitive burden. Furthermore, the computation of the routes is more efficient because it is based on the graph encoding the connectivity of roads, which is substantially smaller than the graph of road segments.
Routes with shortest distance (dashed blue lines) and fewest turns (dotted red lines) shown in (a) geometry-oriented representation and (b) topology-oriented representation.
“We experimented on eight urban street networks from North America and Europe to illustrate the above-stated advantages. The experimental results indicate that the fewest-turn routes possess fewer turns and shorter distances than the simplest paths and the routes provided by Google Maps. For example, the fewest-turn-and-shortest routes are on average 15% shorter than the routes suggested by Google Maps, whereas the number of turns is just half as much. This approach is a key technology behind FromToMap.org – a web mapping service using openstreetmap data.”
View the current Esri Map Book (Volume 26)
Online submissions are now being accepted for the Esri Map Book, Volume 27. If you have an ArcGIS map you would like to be considered for publication, please visit the Map Book online submission site at http://www.esri.com/apps/mapbook. There you will find contact and permission forms plus details about how to submit image files.
The submission deadline is Friday, November 18, 2011, at 5:00 p.m. (PST). If your map is chosen, you will receive an e-mail notification by December. The Esri Map Book, Volume 27 will be released in July 2012 at the Esri International User Conference.
Environmental Modelling & Software, Available online 02 July 2011
Rohan Wickramasuriya, Laurie A. Chisholm, Marji Puotinen, Nicholas Gill, and Peter Klepeis
- We developed an automated tool to generate realistic land subdivision layouts.
- We tested the tool on a southeastern Australian case study area.
- The tool does well when streets are parallel and lots are roughly equal in size.
- We identified the opportunities to further improve the tool.
Simulation of the land subdivision process is useful in many applied and research areas. Planners use such tools to understand potential impacts of planning regulations prior to their implementation. While the credibility of both land-use change and urban growth models would be enhanced by integrating capabilities to simulate land subdivision, such research is lacking in the published literature. Of the few subdivision tools that exist, most are either not fully-automated or are unable to generate realistic subdivision layouts. This limits their applicability, particularly for high resolution land-use change models. In this paper, we present a fully-automated land subdivision tool that uses vector data and is capable of generating layouts with both lot and street arrangements for land parcels of any shape. When the new streets are generally parallel to each other and lots are of approximately the same size, the simulation’s output very closely resembles observed subdivision patterns in our southeastern Australia study area. From this, we identify opportunities to improve the subdivision tool for a next version. Future research will also explore how this subdivision tool could be used in conjunction with a land-use change model for urban and regional planning.”