Dean M. Hanink

Coupling land use allocation models with raster GIS

Abstract. As geographic information systems (GIS) have moved from information storage and retrieval operations towards more decision support functions, there is a need for more integration of spatial analytical modules that can assist in locational decisions. This paper presents a methodology for coupling land use allocation models with a raster GIS. For raster systems, the integration of any decision module has been limited by the size of raster datasets that may contain hundreds of thousands of pixels. Therefore, decision heuristics have been used rather than exact methods such as mathematical programming models. For the problem of land use allocation, the special structure of the generalized assignment problem is used here to handle large scale datasets. The advantage of the mathematical programming approach is the additional information associated with the dual variables and opportunity costs that can be used in subsequent sensitivity analyses.

Distance Effects in the Demand for Wildland Recreational Services: The Case of National Parks in the United States

Distance decay in spatial demand usually is taken as axiomatic. There are, however, a number of situations in which distance decay cannot be taken for granted. In recreational pursuits, for example, spatial interaction is often marked by a confounding distance effect in which nearby and more distant destinations are equally attractive. The research reported in this paper concerns an examination of the distance effect in the spatial demand for a specific type of recreation: the use of national parks in the USA. The paper contains a review of the related spatial demand literature, including the travel cost model used in calculating economic values of national parks and related places. A central-place-type model of park use is described and put into operation in the form of two linear spatial demand models. One focuses on regional demand for park use and the other focuses on a national market. The initial model specifications are expanded in order to examine the drift of their distance parameters over two variables intended to quantify park quality: age and area. Empirical tests of the models indicate distance decay in the demand for park use is pronounced when distance is considered in the context of park quality.

Spatial demand for national battlefield parks

Abstract A spatial demand model is developed to examine some of the factors contributing to the popularity of battlefields as tourism destinations. Specified in a mixed cross-section time-series format, the model is applied to a pooled data set of annual visits to 19 national battlefield parks in the years 1990, 1993, and 1996. In general, the results of an empirical test of the model indicate that a battlefields spatial market potential, its vintage, and the number of casualties that occurred there contribute to its popularity. The aggregate model indicates, however, that proximity to other battlefields depresses visitation and that the parks appear to be competing with one another.

Land‐Use Allocation in the Absence of Complete Market Values

This paper describes a method of land-allocation that can be used byplanners and other land managers in the face of market failure. The method integrates theland-allocation approach used in geographic information systems with that used in a generalizedassignment problem. Suitability scores, instead of market prices, are used in assigning competingland uses to individual parcels (pixels) of land. The method is illustrated using a hypotheticalexample involving three competing land uses within a region.

Geographically Weighted Colocation Quotients: Specification and Application

The spatial correlation, or colocation, of two or more variables is a fundamental issue in geographical analysis but has received much less attention than the spatial correlation of values within a single variable, or autocorrelation. A recent paper by Leslie and Kronenfeld (2011) contributes to spatial correlation analysis in its development of a colocation statistic for categorical data that is interpreted in the same way as a location quotient, a frequently used measure in human geography and other branches of regional analysis. Geographically weighted colocation measures for categorical data are further developed in this article by generalizing Leslie and Kronenfelds global measure as well as specifying a local counterpart for each global statistic using two different types of spatial filters: fixed and adaptive. These geographically weighted colocation quotients are applied to the spatial distribution of housing types to demonstrate their utility and interpretation.

A Quantile Regression Approach to Areal Interpolation

Areal interpolation has been developed to provide attribute estimates whenever data compilation or an analysis requires a change in the measurement support. Over time numerous approaches have been proposed to solve the problem of areal interpolation. Quantile regression is used in this study as the basis of the areal interpolator because it provides estimates conditioned on local parameters rather than global ones. An empirical case study is provided using a data set in northern New England. Land cover data, provided by the National Oceanic Atmospheric Administration, derived from remotely sensed images for 2001 captured by the LANDSAT Thematic Mapper at a resolution of 30 × 30 meters, are used for the ancillary variables for the regression model. The utility of quantile regression as an areal interpolation method is evaluated against simple averages, areal weighting, dasymetric interpolation, and ordinary least squares and spatial regression methods. For the empirical data set used in the study, results show that quantile regression was a better interpolator for the given data set but that binary dasymetric interpolation was a close second. These results were only for one data set and further evaluation is necessary before more general conclusions can be made.

Geographic Change with Trade Based on Comparative Advantage

Abstract This article describes a multimarket von Thünen model that concerns change in the internal economic geography of two countries once trade develops between them. Using the principle of comparative advantage, an initial allocation of four industries is established across each of the countries based on local variation in factor endowments, regional demand at several centers, and internal transport costs. Once trade begins, reallocation of production in the four industries in each country is modeled as the response to comparative factor endowments across the countries, trade costs, transport costs, and differences in demand. The models results indicate that geography matters in trade both within and across countries. Proximity is especially important. Regions of the trading countries that are effectively neighbors are affected more by the introduction of trade than are regions that are peripheral in the true geographical sense. At the national scale, results suggest that a high tariff in one country can raise aggregate production and distribution costs in another country.

Scale-independent Land-use Allocation Modeling in Raster GIS

: A common application of raster-based geographic information systems (GIS) is as an aid in multi-criteria, multi-objective land-use decision problems. However, as the cell resolution increases by reducing cell size, the number of rows and columns in the raster representation also increases. The size of raster representations of land-use problems is often a determining factor in the type of methodology used in solving such problems. Previous land-use allocation models integrated with a raster GIS have used either decision heuristics or exact methods based on linear programming models. The former is fairly scale independent but produces only approximate answers, whereas the latter produces optimal solutions but remains more scale dependent. This paper presents a specialized dual simplex method adapted to the generalized assignment problem that can be used to solve large-scale land-use allocation problems. The dual approach only requires that information for one pixel be stored at a time thus allowing the solution of problems based on any size raster database.

Evaluating the use of publicly available remotely sensed land cover data for areal interpolation

Areal interpolation is used to transfer attribute data between geographically incongruous zonal systems. Remotely sensed land cover data are widely used in intelligent areal interpolation methods to solve this problem. This article examines the usefulness of different publicly available remotely sensed land cover data sets as ancillary data used in conjunction with different areal interpolation methods. Two land cover data sets were compiled at the national scale; one by the Multi-Resolution Land Characteristics Consortium (the National Land Cover Dataset or NLCD) and one by the Coastal Change Analysis Program. A third land cover data set was compiled at a regional scale for the state of Connecticut by the Center for Land Use Education and Research. Results show that for areal interpolation, greater detail in the classification of developed areas was important whether the data were developed for use at a national or a regional scale. Even more important is the further enhancement of remotely sensed land use categories by incorporating local road or parcel data layers. The worst performing interpolation method using enhanced remote sensing-derived land cover data produced more accurate results than the best performing method using only the original land cover data. The results also show that parcels produce better enhancements than road buffers because they remove the areas of the roads themselves from population consideration.

How 'Local' are Local Office Markets?

This paper describes empirical investigations of the geographical extent of office markets in the United States during the 1980s. A mixed temporal autoregressive model was estimated for pooled downtown office markets and pooled suburban markets. Results indicate that while the temporal autoregressive effect is stronger for office market vacancies than is the effect of the national trend, their linkages to national trends are significant. However, a mixed spatial autoregression analysis of the data pooled over time indicates that the regional office vacancy effect is stronger than the national office vacancy effect in both downtown and suburban office markets.