Spatial Intelligence for Regional Analysis

Spatial Intelligence for Regional Analysis

Chenfeng Zhang (East China University of Science and Technology, China), Shuming Bao (University of Michigan, USA & East China University of Science and Technology, China), Bing She (Wuhan University, China), Xinyan Zhu (Wuhan University, China) and Xu Zhang (Wuhan University, China)
DOI: 10.4018/978-1-4666-9845-1.ch016
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Abstract

With the announcement of open information policy by the Chinese government, there has been lots of development in government statistics, Census data, and GIS data. That information provides rich reference for regional analysis in China. The primary challenges under rapid data growth include how to efficiently integrate those space-time data, provide methodology for different research targets, and promote their applications in different fields. This report will present some recent developments of spatial intelligence technologies for spatial data integration, data analysis, as well as their applications. The report will introduce the newly developed spatial explorer (China Geo-Explorer) as a joint effort by the University of Michigan China Data Center and Wuhan University. It will demonstrate how space-time data of different formats and sources can be integrated, visualized, analyzed and reported in a web based system. Some applications in disaster assessment, environment and health, cultural and religious studies will be discussed for China and global studies.
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Introduction

The applications of Geographic Information Systems (GIS) nowadays are not limited to natural sciences anymore. Social scientists have found the spatial data analysis with GIS increasingly helpful and necessary. For example, it helps researchers in the identification of the distribution of social problems like alcohol outlets (Ellaway, Macdonald, Forsyth, & Macintyre, 2010). More and more people and organizations are using GIS to assist them in solving real-world complex problems and decision making, such as the spatial planning and transportation improvements (Nyerges & Jankowski, 2009).

Many studies on complex systems require data from different sources with different formats. It is usually difficult and costly for users with no GIS background to integrate and analyze the data in a timely and efficient manner. With the advent of Web 2.0 technology and the increased popularity of internet mapping in recent years, both commercial and open-source map APIs have emerged to help make GIS technology available to general users. The concept of spatial intelligence is raised to address these problems, which allows efficient data integration for spatial and attribute data, and generates time-saving, easy-to-use, and pre-formatted reports as well as customized reports (She, Zhu & Bao, 2010; Zhang, Zhu, She & Bao, 2009). This paper will introduce China Geo-Explorer, a web based platform with spatial intelligence for spatial data integration, analysis and solution deployment, built on Web 2.0 technology (See Figure 1). It can integrate data with different formats such as raster data, vector data, space-time series data, and text data, allow flexible data selection in traditional or spatial approaches, offer simple and advanced data analysis such as graphic analysis and spatial statistical data analysis, and provide solution support with pre-defined/customized reports, charts, tables, and maps for spatial data exploration and visualizations.

Figure 1.

The framework of China geo-explorer

The rest of the paper is organized as follows: the next section discusses the technology of our platform implementation. Section III introduces the methodology applied in the system. Section IV describes the system architecture. Section V describes data sources and functionalities. Section VI gives several case studies on domains such as disaster management and environment modeling. Section VII ends this paper with a summary and discussions on future directions.

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Technology Background

The Open Geospatial Consortium (OGC) (www.opengeospatial.org) has made great efforts in establishing spatial standards adopted by the GIS domain, including major GIS, database system vendors, government and research institutions (Caldeweyher, Zhang & Pham, 2006). It has released a series of standards for data sharing, and mapping, such as Web Map Service Implementation Specifications (WMS) (Beaujardiere, 2006), Web Feature Service Implementation Specifications (WFS) (Vretanos, 2005), and Web Coverage Service Implementation Specifications (WCS) (Whiteside, 2006). WMS specifies the way to obtain map images according to the queried geographic region, and provides service metadata outlining general capability. WFS differs from WMS in providing access and exchange of vector data, which enables basic spatial analysis, such as spatial query and spatial overlay. Like WFS, WCS also enables exchange of spatial data in a format that permits client side processing, but it focuses on raster data representing space-varying phenomena. These standards form a basis for distributed geospatial data sharing, in which users and developers only need to care about the specification interface rather than detailed implementation of service functionality, but only follow the interface defined by these specifications. Many organizations, universities, and GIS vendors have been involved in the process of OGC standards making. They also follow these standards by implementing OGC-compliant clients and servers. In the OGC website, special links for stating implementations by specification are available. The above mentioned three specifications are the most popular ones implemented in mainstream open source projects such as GeoServer (geoserver.sourceforge.net), degree (deegree.sourceforge.net) and some commercial GIS products such as ArcIMS from ESRI.

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