Siting Obnoxious Facilities

                                            using an Integrated GIS-DSS

                                                                                                            

                                                                                        Jayanthi Rajamani

                                                                      GIS in Water Resources - CE394K

                                                                      Fall 2002 

                                                                                          
 

Table of Contents

 

1. Background

    1.1 GIS and DSS

    1.2 MCDA and AHP

    1.3 Obnoxious Facilities

2. Objectives and Methodology

3. Data Collection

4. Spatial Analysis

5. Integration with MCDA

6. Results and Conclusions

7. Limitations and Future work

8. Acknowledgements

9. References

 

 

 

 

1. Background

 

            1.1 GIS and DSS

 

A decision support system can be viewed as “an integrated, interactive computer system, consisting of analytical tools and information management capabilities, designed to aid decision makers in solving relatively large, unstructured problems” (1995, Watkins and McKinney).  Decision support systems operate within the spatial-temporal context and permit planners and policy makers to (1) integrate large quantities of existing space-time data, (2) use these data as inputs to sophisticated forecasting models for predicting the results of alternative policy choices, and (3) display the model results in easily understood ways to public officials and private citizens as well as to the scientific community. Basic to the use of the DSS is the ability to examine various "what if" situations within the operational context of the spatial or temporal problem. Building a spatial or spatial­-temporal DSS generally involves four major activities: (1) data acquisition and evaluation, (2) database design and construction, (3) construction and testing of analytic space-time forecast (or "what­if") models, and (4) primary data and end product visualization.

 

GIS technologies have been extensively used for decision-making alongside DSS models. In recent times, SDSS (Spatial Decision Support Systems) technologies have been widely built and marketed by all the big names in the field of GIS (Go to http://mis.ucd.ie/iswsdss/sdss.html). The unique capability of a Geographic Information System (GIS) is that not only can it act as a database but also as a powerful tool, wherein complicated operations can be performed with greater ease and better quality information gleaned. When incorporated in this environment, Decision Support Systems can become flexible and fortified. Ushering in such systems in the administrative scenario has become nearly mandatory, to meet the challenges of management in the digital era. By integrating GIS and decision support systems an improvement of access to information can be achieved. “Decision makers may become active participants in a regional planning analysis, rather than selectors among a few, preplanned alternatives” (Jones, 1998).

 

 

 

                                   

                                                                Fig.1 Role of GIS in Decision Support Systems

 

           

            The integration of GIS and DSS model designs may bring about the following advantages: (i) a GIS serves as a basic platform from which a DSS may be developed; (ii) the system integration provides an effective functional coupling of spatial data, spatial simulation and optimization models; and (iii) it supports various analysis techniques for solving spatial conflicts of different objectives, and for spatial decision making.

 

1.2 MCDA and AHP

 

Decision making is an every day activity, common to individuals and groups. In the decision making science, there is a common method called   Multi-Criteria Decision Analysis (MCDA). MCDA deals with a decision making process involving, for instance, policy priorities, trade-offs, uncertainty, etc. The general objective of MCDA is to assist the decision maker (DM) in selecting the 'best' alternative(s) from the number of feasible choice-alternatives under the presence of multiple choice criteria and diverse criterion priorities. The problem of multicriterion (multi-objective) choice in decision making is the paramount challenge faced by individuals, public, and private corporations. The nature of the challenge is two-fold:
(i) How to identify choice alternatives satisfying the objectives of parties involved in the decision-making processes; and  (ii) How to reduce/order the set of feasible choice alternatives to identify the most preferred alternative

 

An example study involving the use of MCDA and GIS is one by M. Ghribi, A. Altobelli and E. Feoli, in which, they created a suitability map showing the industrial sensitivity of the coastal zone of the Gulf of Tunis, Tunisia. It consisted of landscape evaluation for industrial siting and included a GIS tool developed using GRASS geographic information software. They attempted to combine a set of criteria to achieve a single composite basis for a decision according to a specific objective in the multi-criteria evaluation framework. A similar study was undertaken by a group of researchers East Godavari District, Andhra Pradesh, India by M. Ghribi, S. Betapudi, A. Altobelli and E. Feoli. The purpose of this study was to create a method for using a GIS based decision support (DSS) tool, by which the most appropriate site for Agro based industrial sites such as 'packaging and cold storage' and 'food processing' could be determined. The GIS results had to be integrated and evaluated using Multi Criteria Evaluation (MCE). Weighted Linear Combination (WLC) and concordance/ dis-concordance analysis (Voogd, 1983 and Carver, 1991) were the two most common procedures in GIS based multi-criteria evaluations. The ranking and evaluation criteria included environmental and economic factors.

 

            The Analytic Hierarchy Process (AHP) is a multi-attribute modeling methodology which was first developed by Saaty. Since its inception, this approach has been employed to solve a variety of multi-attribute decision making problems. The applications of AHP range from finance to land use planning. The fundamental concept of AHP lies in proceeding from a pairwise comparison of criteria to evaluate the weights that assign relative importance to these criteria. A huge list of references on applications of AHP may be found at http://www.expertchoice.com/hierarchon/references/reflist.htm. The AHP method revolves around two principles:

  1. Build a hierarchy of criteria, in the form of a graph, where, the decision is represented on the left and the alternatives on the right.
  2. At each node of the criterion hierarchy, a weight is assigned such that the weights sum to unity. This weight indicates the relative importance of the decision maker at this level of the hierarchy, for the object(s) linked to the node.

 

             1.3 Obnoxious Facilities

 

Obnoxious faciltiies such as incinerators and landfills involve huge capital investment and can pose serious environmental problems. Incinerators can handle three kinds of waste: (a) municipal solid waste, (b) hazardous industrial waste, and (c) medical waste. This project deals with municipal waste incinerators only. Municipal solid waste is defined as the solid portion of  the waste (not classified as toxic or hazardous) generated by households, commercial establishments, public and private institutions, government agencies, and other sources. This includes food and yard wastes and a multitude of durable and nondurable products and packaging. Locating obnoxious facilities such as incinerators and landfills is a critical issue in the US since waste generation has continually been on the rise despite government attention to waste reduction and recycling. This increase can be further attributed to population growth as well as increase in per capita consumption of waste-generating disposable items and extensive use of packaging. Cities and municipalities face the difficult task of siting these facilities. Their decision is extremely crucial as it can have great  economic, environmental, and socio-political implications.

 

       

2. Objectives and Methodology

 

This project aims to develop an efficient method to make the incinerator location decision. This project demonstrates the process and results in the context of the city of Austin. The methodology followed was two-fold. First, a spatial analysis using ArcGIS was done to zero in on some of the feasible incinerator locations. Second, a multi-criterion decision analysis (MCDA) was conducted with the feasible locations to select one or more specific sites. Several steps were followed in achieving the final target. First, a set of spatial criteria was developed to select possible locations. Second, ArcGIS was used to deploy the criteria and come up with a choice set of feasible alternatives for siting the incinerator facility. Third, a set of economic and environmental criteria was developed and implemented as a linear optimization problem. Fourth, Excel Solver was used as an optimization tool to choose the "best" or most optimal alternative, given the set of objectives and constraints. Fifth, the solution was analyzed and compared under different constraints to demonstrate the logical correctness of the approach. 

 

3. Data Collection

 

Data used for this project included the zip code areas for the city of Austin, which was obtained from the 1990 census data of the US Census Bureau (www.census.gov). Shapefiles of land uses, parks and facilities, and the street network were obtained from the City of Austin resource (ftp://issweb.ci.austin.tx.us/pub/coa_gis.html). The data were all converted to conform to the same coordinate system, namely the Lambert Conformal Conic with NAD83 as the datum.

 

                                                       

                                                      

 

 

 

The street network including the major and minor arterials and the local streets were added to the geodatabase as shapefiles, obtained from the City of Austin website.  

 

 

 

 

                                                               

 

4. Spatial Analysis

The ArcHydro watershed processing tool was used to find the centroids of each zip code area. For that,  a  HydroID for each zip code was assigned so that it is recognized as a drainage area by ArcHydro. An attribute called waste_Annual_Tonne was then computed as follows:

                                            

 

      

 

 

 

Waste_Annual_tonne = 0.002*population of zip code*300 , assuming each person generates 2kg of waste each day 300 days of the year. This number was taken from EPA. A thematic map was generated showing the waste produced in each zip code area through graduated symbols.

 

 

 

 

 

 

 

The set of spatial criteria developed to obtain the admissible incinerator sites is as follows:

 

               

 

 

The parcels in dark blue are the parcels owned by the City of Austin. These were further queried to obtain the parcels that exceed 2 hectares in area.

 

                 

 

 

                                                                   

 

                                                         

 

 

                                            

 

                                                                       

                                                                       

 

 

                                                                             

 

The number of feasible locations for siting the incinerator facility were reduced to 40 after the above-mentioned buffer analyses.

 

                                           

 

In the above screen capture, the pink points represent the 40 selected feasible incinerator locations and the blue points are the zip code centroids. The attribute table of the incinerator locations have a DrainID and an Enabled field, similar to Monitoring Points in a typical hydrologic network.

                                                  

5. Integration with MCDA

The next step was to select amongst these 40 possible locations, the optimal locations. The original idea was to incorporate economic, environmental, and socio-political criteria. The economic criteria include the transportation cost and the investment cost. The environmental criteria include exposure to NOx and transportation nuisance. The socio-political criteria include what is called the "Neighborhood" criterion, which means that people generally consider incinerators as obnoxious facilities and would not like to be staying anywhere close.

                                       

However, all of these could not be incorporated due to lack of adequate data.  The optimization problem that was solved was as follows:

 

                                           

              

Cij = unit transportation cost from zip code i to incinerator site j
xij = tonnage of waste transported from zip code i to site j
Fj = investment cost for site j
yj = {0 if site j is not selected
1 if site j is selected}
vi = waste generated by zip code i
γj = plant capacity at site j
ej = population exposed to transportation nuisance at site j
E = global nuisance indicator

       

          

The objective function involves two costs, the transportation cost and the investment cost. There are five constraints: 1) minimum sum of quantities of waste transported from one zip code area to all the sites must equal the yearly volume of waste produced in that zip code area 2) the total waste at a site (received from all the zip codes) must not exceed the capacity of that plant if that site is selected 3) this is an environmental constraint ; the net pollution or nuisance caused to the affected population must not exceed the maximum allowable global pollution indicated by the global nuisance indicator.

Since Excel Solver can handle only upto 200 variables, the number of alternatives was reduced to 4 sites.

 

                                       

 

The transportation cost matrix was developed in TransCAD be converting all zip code centroids and site centroids to nodes and identifying the streets as links. TransCAD recognized the network and displayed the route system according to the shortest path algorithm. After obtaining the shortest paths between every zip code-site pair, the impedance values were computed from the distances along these paths to obtain the transportation cost matrix. The shortest paths were stored as shapefile in TransCAD and  imported into ArcGIS geodatabase as a feature class.

                                               

                           

 

                                              

 

                                                       
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For computation of xij, research judgment was used to distribute the waste from an area among two or three of the four selected sites and then xij was computed from the total yearly waste generated by each zip code area. An investment cost of $500m for a plant was assumed (Source: Solid Waste & Community Economic Development). vi was previously computed and added as an attribute to each zip code area. Plant capacity is assumed as 120000 tonnes/year (Source: Solid Waste & Community Economic Development). ej was computed from a spatial buffer analysis. A 1 km. buffer was built around each of the sites, following which, the population exposed was computed by proportioning the population of the zip code area according to the ratio of areas of the buffer and the zip code area. This had to be done manually because of the unavailability of a population distribution raster. The global nuisance indicator is a transportation nuisance index, and this is the value that was varied in order to find different feasible solutions.

 

Values were input into an Excel spreadsheet and the Solver was invoked to solve the linear optimization problem at hand.

 

 

                                             

6. Results and Conclusions

The optimization results showed the following two important things:

  1. As the nuisance index decreases, the cost of installation of the incinerator facility rises quite rapidly as shown by the following graph. In other words, nuisance and cost are conflicting criteria and we have computed trade-offs between the two criteria to fine efficient "compromise" solutions.

 

                                                             

 

 

    2.    As the nuisance index is decreased, the number of plants to be installed increases in order to distribute the traffic and reduce the pollution in sensitive areas.

 

                                                                       

 

7. Limitations and future Work

Certain criteria were neglected in selection of sites for locating the incinerator facilties due to lack of adequate data. These criteria may include proximity to airports, geologic faults, floodplains; depth to groundwater, adverse impacts on wild life; and political and social acceptance.

An accurate DSS framework would typically be exhaustive in terms of the criteria considered and the assumptions would have a very firm background. Future work may include the above-mentioned neglected criteria and a more thorough optimization-based multi-criterion decision analysis resulting in better decisions.

 

8. Acknowledgements

Dr. David R. Maidment

Hema Gopalan

Sandeep Conoor

 

9. References

www.census.gov

ftp://issweb.ci.austin.tx.us/pub/coa_gis.html

http://ecolu-info.unige.ch/~haurie/mutate/Mutate_final/Lectures/

TransCAD User's Manual

 

 

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