Transactions in GIS

The concept of Volunteered Geographic Information (VGI) has recently emerged from the new Web 2.0 technologies. The OpenStreetMap project is currently the most significant example of a system based on VGI. It aims at producing free vector geographic databases using contributions from Internet users. Spatial data quality becomes a key consideration in this context of freely downloadable geographic databases. This article studies the quality of French OpenStreetMap data. It extends the work of Haklay to France, provides a larger set of spatial data quality element assessments (i.e. geometric, attribute, semantic and temporal accuracy, logical consistency, completeness, lineage, and usage), and uses different methods of quality control. The outcome of the study raises questions such as the heterogeneity of processes, scales of production, and the compliance to standardized and accepted specifications. In order to improve data quality, a balance has to be struck between the contributors' freedom and their respect of specifications. The development of appropriate solutions to provide this balance is an important research issue in the domain of user‐generated content.

The research field of transportation demand forecasting has started to focus on disaggregate travel behavior and micro‐simulation models. To create data infrastructure, disaggregate trip surveys are conducted and large numbers of observations are collected. To efficiently exploit these surveys, the transfer of the individual trip data to a GIS must start with the development of a solid conceptual data model that fully captures the semantic richness of the application domain and emphasizes its spatio‐temporal properties. This paper presents a data modeling process that is based on a combination of complex system theory and the object‐oriented paradigm and produced an object‐oriented spatio‐temporal data model. Main domain entities are modeled as highly structured classes. They encapsulate a memory of their time bound connections and states. Observation data sets are sampled from the origin‐destination survey conducted in the Québec region in 1991. This survey incorporated street networks and activity places. The model was smoothly implemented into a proof‐of‐concept database prototype hosted by an object‐oriented GIS shell. The prototype offers a means to navigate through a nested hierarchy of objects, providing a description of an individual’s travel behavior over space and time. The objects have a solid conceptual basis and can meet the needs of scientific research such as hypothesis formulation, simulation, forecasting and induction.

It is well known that the grid cell size of a raster digital elevation model has significant effects on derived terrain variables such as slope, aspect, plan and profile curvature or the wetness index. In this paper the quality of DEMs derived from the interpolation of photogrammetrically derived elevation points in Alberta, Canada, is tested. DEMs with grid cell sizes ranging from 100 to 5 m were interpolated from 100 m regularly spaced elevation points and numerous surface‐specific point elevations using the ANUDEM interpolation method. In order to identify the grid resolution that matches the information content of the source data, three approaches were applied: density analysis of point elevations, an analysis of cumulative frequency distributions using the Kolmogorov‐Smirnov test and the root mean square slope measure. Results reveal that the optimum grid cell size is between 5 and 20 m, depending on terrain com‐plexity and terrain derivative. Terrain variables based on 100 m regularly sampled elevation points are compared to an independent high‐resolution DEM used as a benchmark. Subsequent correlation analysis reveals that only elevation and local slope have a strong positive relationship while all other terrain derivatives are not represented realistically when derived from a coarse DEM. Calculations of root mean square errors and relative root mean square errors further quantify the quality of terrain derivatives.

SLEUTH is a computational simulation model that uses adaptive cellular automata to simulate the way cities grow and change their surrounding land uses. It has long been known that models are of most value when calibrated, and that using back‐casting (testing against known prior data) is an effective calibration method. SLEUTH's calibration uses the brute force method: every possible combination and permutation of its control parameters is tried, and the outcomes tested for their success at replicating prior data. Of the SLEUTH calibration approaches tried so far, there have been several suggested rules to follow during the brute force procedure to deal with problems of tractability, most of which leave out many of the possible parameter combinations. In this research, we instead attempt to create the complete set of possible outcomes with the goal of examining them to select the optimum from among the millions of possibilities. The self‐organizing map (SOM) was used as a data reduction method to pursue the isolation of the best parameter sets, and to indicate which of the existing 13 calibration metrics used in SLEUTH are necessary to arrive at the optimum. As a result, a new metric is proposed that will be of value in future SLEUTH applications. The new measure combines seven of the current measures, eight if land use is modeled, and is recommended as a way to make SLEUTH applications more directly comparable, and to give superior modeling and forecasting results.

It is challenging to map large spatial flow data due to the problem of occlusion and cluttered display, where hundreds of thousands of flows overlap and intersect each other. Existing flow mapping approaches often aggregate flows using predetermined high‐level geographic units (e.g. states) or bundling partial flow lines that are close in space, both of which cause a significant loss or distortion of information and may miss major patterns. In this research, we developed a flow clustering method that extracts clusters of similar flows to avoid the cluttering problem, reveal abstracted flow patterns, and meanwhile preserves data resolution as much as possible. Specifically, our method extends the traditional hierarchical clustering method to aggregate and map large flow data. The new method considers both origins and destinations in determining the similarity of two flows, which ensures that a flow cluster represents flows from similar origins to similar destinations and thus minimizes information loss during aggregation. With the spatial index and search algorithm, the new method is scalable to large flow data sets. As a hierarchical method, it generalizes flows to different hierarchical levels and has the potential to support multi‐resolution flow mapping. Different distance definitions can be incorporated to adapt to uneven spatial distribution of flows and detect flow clusters of different densities. To assess the quality and fidelity of flow clusters and flow maps, we carry out a case study to analyze a data set of 243,850 taxi trips within an urban area.

Recent literature has reported inaccuracies associated with some popular home range estimators such as kernel density estimation, especially when applied to point patterns of complex shapes. This study explores the use of characteristic hull polygons (CHPs) as a new method of home range estimation. CHPs are special bounding polygons created in GIS that can have concave edges, be composed of disjoint regions, and contain areas of unoccupied space within their interiors. CHPs are created by constructing the Delaunay triangulation of a set of points and then removing a subset of the resulting triangles. Here, CHPs consisting of 95% of the smallest triangles, measured in terms of perimeter, are applied for home range estimation. First, CHPs are applied to simulated animal locational data conforming to five point pattern shapes at three sample sizes. Then, the method is applied to black‐footed albatross (Phoebastria nigripes) locational data for illustration and comparison to other methods. For the simulated data, 95% CHPs produced unbiased home range estimates in terms of size for linear and disjoint point patterns and slight underestimates (8–20%) for perforated, concave, and convex ones. The estimated and known home ranges intersected one another by 72–96%, depending on shape and sample size, suggesting that the method has potential as a home range estimator. Additionally, the CHPs applied to estimate albatross home ranges illustrate how the method produces reasonable estimates for bird species that intensively forage in disjoint habitat patches.

The last decade has seen a renaissance in spatial modeling. Increased computational power and the greater availability of spatial data have aided in the creation of new modeling techniques for studying and predicting the growth of cities and urban areas. Cellular automata is one modeling technique that has become widely used and cited in the literature; yet there are still some very basic questions that need to be answered with regards to the use of these models, specifically relating to the spatial resolution during calibration and how it can impact model forecasts. Using the SLEUTH urban growth model (Clarke et al. 1997), urban growth for San Joaquin County (CA) is projected using three different spatial grains, based on four calibration routines, and the spatial differences between the model outputs are examined. Model outputs show that calibration at finer scaled data results in different parameter sets, and forecasting of urban growth in areas that was not captured through the use of more coarse data.

Heterogeneous geographic databases contain multiple views of the same geographic objects at different levels of spatial resolution. When users perceive geographic objects as one spatial unit, although they are physically separated into multiple parts, appropriate methods are needed to assess the consistency among the aggregate and the parts. The critical aspect is that the overall spatial relationships with respect to other geographic objects must be preserved throughout the aggregation process. We developed a systematic model for the constraints that must hold with respect to other spatial objects when two parts of an object are aggregated. We found three sets of configurations that require increasingly more information in order to make a precise statement about their consistency: (1) configurations that are satisfied by the topological relations between the two parts and the object of interest; (2) configurations that need further information about the topological relation between the object of concern and the connector in order to be resolved unambiguously; and (3) configurations that require additional information about the topological relation between the aggregate's boundary and the boundary or interior of the object of interest to be uniquely described. The formalism extends immediately to relations between two regions with disconnected parts as well as to relations between a region and an arbitrary number of separations.