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Using location-allocation models for regional planning in GIS environment
S. H. Sanaei-Nejad
Ferdowsi University of Mashhad, P.O.Box: 91775-1163,
Mashhad, Iran,
Sanaei_h@yahoo.co.uk
H. A. Faraji-Sabokbar
University of Mashhad, P.O.Box: 9177948883 Mashhad, Iran
Faraji_sabokbar_ya@yahoo.com
Introduction
When there are some resources and many demands, there should be a function to propose the most optimized routes for accessing the resources. This is the problem of many business and government institutes (Lea and Simmons, 2000). This subject is of great interest in third world countries where there is large demand for Infrastructure development communication, drinking water, educational facilities, public distribution system, electricity, financial institutions, markets, medical facilities & public health, transport, veterinary services, recreation & tourism which is the major concern of developing nations. (Inter-graph, 2002)
The basis of the models can be explained when we consider that, there must be transportation or movement through the network which connect different resources and demands throughout an area. For example, when there are limited sites to collect and store agricultural products in a farming area or assignment of specified number of schools with limited seats for students in a residential area, we need to use "allocation models" to find the best solution. Different model functions are applied depending on type and aim of the allocation issue (Klinkenberg, 1997). First we discuss different models.
- Private sector allocation model
This model is used to minimize costs and maximize efficiency. In fact there is an option, which the model can minimize total distance traveled from all demand points access resources.
- Public sector allocation model
This model applied to private fair service with maximum efficiency. To achieve these, we need to maximize the assignment of demands to each center. In this case we consider a linear likelihood function for assignment. The total distance traveled is also minimized, where the distance measured according to a power function. We should also consider the minimum total distance ensuring that no further demand point is in the given distance.
- Emergency service location model
In some cases we need to serve as many people as possible within a given distance from a center. Using this model we intend to provide a service with no limitation in covering demand points, except for those area which practical constraints exist.
We need to find a mathematical equation to structure the whole issues of allocation properly. Therefore, first the aims and constraints need to be settled and then the optimizing the factors must be optimized to solve the problem.
For instance, this model can be used to utilize and organize rural settlements, in order of allocating two schools in the rural points. In this case we need to find appropriate places for the schools, so that the costs and traveling are minimized. Therefore the constraints are:
Two schools are considered to be allocated and their capacity is specific number.
Obviously, students will choose the closest school to attend
- Distance limitation:
No student will travel to a school, if it is very far from his/her settlement when the constraints and the aim of the plan are determined. The problem can be solved by optimizing the parameters in the model. In this example, location of the school and allocating students to them will be outputs of the model
Geographic Information Systems (GIS) is a software and hardware tool applied to geographical data far integration, collection, storing, retrieving transforming and displaying spatial data for solving complex planning and management problems. (Sarangi, et al., 2001)
Indeed, location-allocation methods are one of the few modeling and spatial analysis tools offered in proprietary GISs today (Kim, Openshaw, 2002). Most of GIS software are capable to solve optimizing problems and run allocation models, such as ARC/INFO, ILWIS, ARCVIEW, IDRISI, and CARIS. In this papers we used ARC/INFO (ESRI, 1998) to run the allocation model for the specified data.
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