The National Geospatial Program

Fragmentary river segments have to be reconnected before addressing various routing and tracking problems. Elevation determines drainage directions, so the partial heights available through LiDAR may provide useful hints on how the segments should be joined. However, it is not trivial how this information can be applied. This paper bridges this gap by proposing the induced structure approach, which first approximates a terrain compatible with those observations, and then derives a river network from that induced terrain. Since the network is derived from an induced terrain that honors the partial observations, we expect that the derived river network will enforce most restrictions imposed by the partial observations. This paper also provides specifics on the implementation. In the first step regarding terrain reconstruction, we find that the optimal scheme depends on the height sample distribution. If the samples are sparsely yet evenly distributed, natural neighbor interpolation with stream burning (NN-SB) is the most cost-effective. If the samples are offered only at the given river locations, the hydrology-aware version of Over determined Palladian Partial Differential Equation (HA-ODETLAP) should be used instead. In the second step concerning river derivation, we find it necessary to favor those given river locations. Otherwise they will be missed out. We set their respective initial water amounts to the critical accumulation level to ensure a river flows across them. In the subsequent branch thinning process, those locations are protected from being trimmed. We foresee applications of our solution framework in a few 2D and 3D network tracing problems with similar observation distribution, like dendrite network reconstruction.     

The Scope and Conceptual Content of Analytical Cartography

Over the last three decades analytical cartography has grown from Tobler's concept of "solving cartographic problems" into a broader and deeper scientific specialization that includes the development and expansion of analytical/mathematical spatial theory and model building. In many instances Tobler himself has led the way to these new insights and developments. Fundamental concepts begin with Tobler's cartographic transformations; Nyerges' deep and surface structure and data levels; and Moellering's real and virtual maps; the sampling theorem; and concepts of spatial primitives and objects. This list can be expanded to include additional analytical concepts such as spatial frequencies, spatial surface neighborhood operators, information theory, fractals, Fourier theory, topological network theory, and analytical visualization, to name a few. This base of analytical theory can be employed to analyze and/or develop such things as spatial surfaces, terrain analysis, spatial data schemas, spatial data structures, spatial query languages, spatial overlay and partitioning, shape analysis, surface generalization, cartographic generalization, and analytical visualization. More analytical uses of theory, strategies of analysis, and implementations are being developed and continue to multiply as the field continues to grow and mature. A primary goal is to expand the mathematical/analytical theory of spatial data analysis, and theory building and analytical visualization as analytical cartography takes its place in the geographic information sciences. The research future for this area appears very bright indeed.     

Boundary Problem for Triangulated Irregular Networks

The triangulated irregular network (TIN) is a data structure commonly used for approximating continuous surfaces and for analyzing point data sets. Unfortunately, the locations of the data points may limit the areal extent of a TIN such that some regions within a study area's boundary are outside the scope of the analysis. The use of pseudopoints generated outside the boundary is recommended as a method for increasing the extent of a TIN. Criteria for selecting pseudopoints and for assigning values to them are discussed. This comparative study demonstrates several variations of the general method.     

Use of Graph Theory to Support Map Generalization

In the generalization of a concept, we seek to preserve the essential characteristics and behavior of objects. In map generalization, the appropriate selection and application of procedures (such as merging, exaggeration, and selection) require information at the geometric, attribute, and topological levels. This article highlights the potential of graph theoretic representations in providing the topological information necessary for the efficient and effective application of specific generalization procedures. Besides ease of algebraic manipulation, the principal benefit of a graph theoretic approach is the ability to detect and thus preserve topological characteristics of map objects such as isolation, adjacency, and connectivity. While it is true that topologically based systems have been developed for consistency checking and error detection during editing, this article emphasizes the benefits from a map-generalization perspective. Examples are given with respect to specific generalization procedures and are summarized as a partial set of rules for potential inclusion in a cartographic knowledge-based system.     

An Artificial-Neural-Network-based,Constrained CA Model for Simulating Urban Growth

Insufficient research has been done on integrating artificial-neural-network-based cellular automata (CA) models and constrained CA models, even though both types have been studied for several years. In this paper, a constrained CA model based on an artificial neural network (ANN) was developed to simulate and forecast urban growth. Neural networks can learn from available urban land-use geospatial data and thus deal with redundancy, inaccuracy, and noise during the CA parameter calibration. In the ANN-Urban-CA model we used, a two-layer Back-Propagation (BP) neural network has been integrated into a CA model to seek suitable parameter values that match the historical data. Each cell's probability of urban transformation is determined by the neural network during simulation. A macro-scale socio-economic model was run together with the CA model to estimate demand for urban space in each period in the future. The total number of new urban cells generated by the CA model was constrained, taking such exogenous demands as population forecasts into account. Beijing urban growth between 1980 and 2000 was simulated using this model, and long-term (2001–2015) growth was forecast based on multiple socio-economic scenarios. The ANN-Urban-CA model was found capable of simulating and forecasting the complex and non-linear spatial-temporal process of urban growth in a reasonably short time, with less subjective uncertainty.     

Dasymetric Estimation of Population Density and Areal Interpolation of Census Data

This paper describes techniques to compute and map dasymetric population densities and to areally interpolate census data using dasymetrically derived population weights. These techniques are demonstrated with 1980-2000 census data from the 13-county Atlanta metropolitan area. Land-use/land-cover data derived from remotely sensed satellite imagery were used to determine the areal extent of populated areas, which in turn served as the denominator for dasymetric population density computations at the census tract level. The dasymetric method accounts for the spatial distribution of population within administrative areas, yielding more precise population density estimates than the choroplethic method, while graphically representing the geographic distribution of populations. In order to areally interpolate census data from one set of census tract boundaries to another, the percentages of populated areas affected by boundary changes in each affected tract were used as adjustment weights for census data at the census tract level, where census tract boundary shifts made temporal data comparisons difficult. This method of areal interpolation made it possible to represent three years of census data (1980, 1990, and 2000) in one set of common census tracts (1990). Accuracy assessment of the dasymetrically derived adjustment weights indicated a satisfactory level of accuracy. Dasymetrically derived areal interpolation weights can be applied to any type of geographic boundary re-aggregation, such as from census tracts to zip code tabulation areas, from census tracts to local school districts, from zip code areas to telephone exchange prefix areas, and for electoral redistricting.     

Mapping Apeldoorn

Abstract

Navigation around urban areas is often constraining for the mobility-impaired due to the fabric of the urban landscape, and there is a need to provide maps tailored to individual abilities. Barriers to effective navigation, such as slope, surface type and dropped kerbs, differ for able-bodied pedestrians and wheelchair users. This study identifies and quantifies such differences, and develops a Geographical Information Systems (GIS) network model for the creation of accessibility maps for wheelchair users. The measurement of barriers uses Digital Elevation Models, calculation of rolling resistance, and surveys in the field using hand-held GIS. A spatial database has been constructed which contains the pedestrian route network and barriers to navigation. A GIS application runs the model, providing a user-friendly interface to define and calculate routes through the pedestrian route network that take account of impedances to accessibility. The model, application and interface has been tested with wheelchair users and the route selection provides a good correspondence with patterns of route finding already established through experience. The interface and individually tailored maps generated, provide a tool suitable to assist wheelchair users new to an area; to enable better navigation for existing users, and a means for planners to consider the way in which access is restricted for wheelchair users in their designs for more inclusive urban environments.