Design and Implementation of a Model,Web-based,GIS-Enabled Cancer Atlas

Abstract

The design and development of a highly interactive web-based, GIS-enabled atlas is reported. The atlas is a prototype, designed as a model for implementation of atlases to support government cancer-control activities. This model integrates symbolisation and design principles from print cartography, interaction strategies from exploratory geovisualisation, and web-map/web-feature service advances from GIS. The atlas has been implemented using a client-server architecture. It makes use of two open-source GIS tools, PostGIS (as the system database) and GeoServer (to connect the database to the client mapping application). The client mapping application has been built in Macromedia Flash. The entire client-server architecture is described, then direct primary emphasis is focused on the client mapping application. For this component of the system, the interface design strategy is detailed, the approach taken to implement this strategy in Flash is documented, and the mechanisms developed to build dynamic links from the client to the underlying database through the server are outlined. Features of the atlas are presented through a prototypical use scenario for a target user.     

An Automated System for Image-to-Vector Georeferencing

Although modern imagery usually has latitude and longitude or similar coordinates to provide true world location, much potentially useful imagery lacks this information. This includes historical imagery, as well as modern imagery that may have lost its locational information through inappropriate processing. Its provision through the process of georeferencing is still primarily a manual procedure. This paper proposes an efficient, fully- automated solution for massively asymmetric image-to-vector georeferencing whereby an image of a relatively small geographic area is automatically located relative to a substantially larger vector map base. For control points, road intersections are automatically extracted from high resolution aerial or satellite imagery using the new concepts of reference circles and central pixels. For automated control point pair identification between image and vector map, an invariant point pattern matching approach is proposed based on shape invariance for similarity transforms and invariant area ratios for affine transforms. The matching algorithm necessitates only a small subset of image points and requires no additional information beyond pixel and map coordinates. Further, it tolerates inaccurate, missing and spurious points, and provides high performance with linear scalability. A final step performs transformation verification, globalization and optimization based upon an Iterative Closest Point Greedy algorithm. Experimentation shows that images covering a few city blocks with as few as 6 to 17 extracted road intersection points can be efficiently and correctly located using the road network of Dallas County, Texas, with over 80,000 intersections.     

Introduction

We introduce and test an algorithm for extracting high-point locations from statistical surface data. The algorithm uses map algebra and local neighborhood analysis via three key parameters: minimum vertical gain, vertical gain neighborhood, and horizontal separation neighborhood. Though the method is applicable to any x,y,z data set, we tested it on 1:250,000 digital elevation models (DEMs) for Arizona. The resulting high points were compared quantitatively with an independent data set of named summits from the USGS Geographic Names Information System (GNIS). The comparison showed that, on an aggregate basis, the extraction method can approximate the number and spatial pattern of high points when compared to the GNIS points. However, extraction by neighborhood analysis may consistently misdiagnose certain features, such as the edges of troughs (e.g., canyon rims).     

An Automated Method of Scale Selection and Sheet Configuration for Multiple Sheet Census Maps with Insets

In order to create a useful map, the cartographer must select a scale at which the map reader can distinguish features shown on the map and read their labels. However, the choice of scale for a paper map is also constrained by the size of the map sheet and by the cost of working with a large number of sheets. When the feature density pattern allows, space can be conserved by making the map at more than one scale: a small scale suitable for most of the map, while dense features are shown on inset maps at larger scales. Creating inset maps requires the cartographer to make a series of complex, interrelated decisions regarding the most effective overall sheet configuration, which is dependent upon the scale chosen for the main map and how the inset maps are created. The Census Automated Map Production System (CAMPS) applies cartographic logic and density analysis to make these decisions in a fully automated mapping environment.