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. p

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Urban land use information is increasingly important for a variety of purposes. With their increasing coverage and availability, airborne light detection and ranging (LiDAR) data, high resolution orthoimagery (HRO), and Google Street View (GSV) images are showing great potential for accurate land use classification. However, no study mapped land use in megacity using GSV-derived features or the three kinds of data together for land use classification. The main objectives of this study are (1) to test the performance of a parcel-based land use classification method using a Random Forest classifier with LiDAR data, HRO, and GSV images in a megacity, and (2) to explore the use of GSV in separating parcels of mixed residential & commercial buildings from other land use parcels. Two neighbouring community districts in Brooklyn, New York, were selected as the study area. Thirteen automaticallyderived parcel features, including nine common parcel features and four GSV-derived parcel features, were used in land use classification. The average overall classification accuracy was 77.5%, with producer's accuracies exceeding 92% for single-family housing. Comparing the results of classifications with and without GSV-derived parcel features shows that GSV-derived parcel features on average contribute to the classification accuracy of mixed residential & commercial buildings by 10 percentage points, improving it from 41.3% to 51.4%. In general, the results show that even in a complex megacity, the parcel-based land use classification technique, with parcel features extracted from airborne LiDAR, HRO, and GSV, is able to discriminate among different land use classes, such as single-family house, commercial & industrial building, and open space & park, with acceptable accuracies, and that integrating GSV into classification improves the classification accuracy of some urban land use classes, especially mixed residential & commercial building.

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