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Grid-based urban growth model (New Jersey).

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Grid-based urban growth model (New Jersey).
作者:	Agung, Anak Agung Gde.
面頁冊數:	185 p.
附註:	Director: Richard K. Brail.
附註:	Source: Dissertation Abstracts International, Volume: 61-05, Section: A, page: 2068.
Contained By:	Dissertation Abstracts International61-05A.
標題:	Urban and Regional Planning.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9973262
ISBN:	0599785594

Grid-based urban growth model (New Jersey).
Agung, Anak Agung Gde.

Grid-based urban growth model (New Jersey). [electronic resource] - 185 p.

Director: Richard K. Brail.

Thesis (Ph.D.)--Rutgers The State University of New Jersey - New Brunswick, 2000.

Being able to effectively predict the location of new development is a key requirement in many activities of policy formulation. Prediction of new development is needed in order to assess impacts of new infrastructures such as the construction of a new highway or the implementation of a new policy such as open space conservation. Regardless of the complexity of the data and techniques involved in the analysis, prediction usually has to be made with the best analytical resources that one can use. A simple form of prediction that is commonly used is extrapolation of a certain measure of development, such as population, based on historical data. A geographical unit of analysis, such as a municipality or county, is often assumed. In some cases, this may be an acceptable or mostly feasible approach, but in many other situations prediction at a more spatially disaggregate level is required.

ISBN: 0599785594Subjects--Topical Terms:

212416
Urban and Regional Planning.

Grid-based urban growth model (New Jersey).
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520 # $a Being able to effectively predict the location of new development is a key requirement in many activities of policy formulation. Prediction of new development is needed in order to assess impacts of new infrastructures such as the construction of a new highway or the implementation of a new policy such as open space conservation. Regardless of the complexity of the data and techniques involved in the analysis, prediction usually has to be made with the best analytical resources that one can use. A simple form of prediction that is commonly used is extrapolation of a certain measure of development, such as population, based on historical data. A geographical unit of analysis, such as a municipality or county, is often assumed. In some cases, this may be an acceptable or mostly feasible approach, but in many other situations prediction at a more spatially disaggregate level is required.
520 # $a Growth probability is estimated based on the changes of land use that occurred between 1986 and 1995/1997. Three counties in New Jersey are selected as the test for the model: Burlington, Camden, and Atlantic. (Abstract shortened by UMI.)
520 # $a This thesis focuses on a grid-based model to predict the location of new development. The model attempts to answer the following question. If we can project overall development in a region, can we predict where the locations of new development within the region are? Overall development in a region may be projected in terms of population or total developed land. The model utilizes the concept of growth probability. If the whole region is divided into equal size grid cells, and we are interested in analyzing the change of land from undeveloped to developed status, then every grid cell has a growth probability that can be estimated based on its characteristics. The factors characterizing growth probability of a grid cell can be systematically classified into four groups: (1) spatial interaction factors indicated by accessibility of a location to other places and to the center of economic activities, (2) environmental factors, (3) characteristics of neighboring locations, (4) growth trends. The model developed in this thesis involves two main steps. The first step is a statistical analysis to estimate parameters of all significant variables in the logistic regression to predict growth probability. The second step is to allocate new development based on the growth probability estimated in the first step.
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