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A GIS-based Site Search Tool

Kenneth S.S. TANG, Erin S.L. YEUNG,

Roy K.H. TANG and H.Y. WONG1

Abstract

Site search is one of the main tasks of the planning authorities for identifying suitable sites

for various types of land uses, taking into account a number of quantitative and qualitative

criteria. In view of the limitations of the existing technologies and tools available for site

search, the Planning Department (PlanD) has recently developed a new site search tool by

adopting a self-developed algorithm that could overcome a number of known problems

related to this kind of tools. These problems include the handling of contiguity, shape and

compactness of the potential sites during the site search process.

Due to its innovation and cost-effectiveness, this new tool developed by PlanD has won two

awards in the Hong Kong Information and Communication Technology (ICT) Awards 2012,

namely Best Public Service Application Grand Award and Best Public Service Application

(Small Project) Gold Award.

1. Introduction

In the field of urban planning, site search is never an easy task especially when it comes to

identifying sites for obnoxious facilities (e.g. landfill or gas tank) due to the so-called

“Not-In-My-Back-Yard” (NIMBY) syndrome (Chiu, et al., 2009). Most often, wrong siting

or undesirable-facility location problem would lead to strong public opposition and

environmental degradation (Baxter, et al., 1999; Babalola, et al., 2011).

In view of the complexity of site search exercise which involves the consideration of a large

number of criteria and factors, a number of decision support tools adopting various kinds of

information technologies have been developed. Nevertheless, these tools have their respective

advantages and limitations. To meet the operational needs of local planners in site search,

PlanD considers that there is a need to develop a “tailor-made” tool rather than relying on

off-the-shelf software and adopting available methodologies without addressing the known

1 Dr. Kenneth S.S. Tang and Ms. Erin S.L. Yeung are Senior Town Planner and Town Planner of the Planning

Department, HKSARG respectively while Messrs. Roy K.H. Tang and H.Y. Wong are respectively Analyst Programmer and Former Survey Assistant of the Department.

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limitations of this kind of tools.

In order to design an effective tool with appropriate integration of available technologies and

self-developed algorithm to suit PlanD’s needs, a review of the current available technologies

for such exercise has been carried out.

2. Review of Current Technologies and Known Limitations of Site Search Tool

In what follows is a review of the available technologies currently adopted for site search and

the known limitations of site search tools.

2.1 Technologies

Geographic Information System (GIS) and Multi-criteria Decision Analysis (MCDA) are the

two most common technologies adopted at various stages of site search.

GIS

GIS information is made up of two components, the spatial units and the information in

various formats which are linked up to individual spatial units of the spatial framework. Due

to the special nature of GIS, it enables spatial analysis to be performed in either vector or

raster format.

A distinctive advantage of GIS is its capability to provide analytical procedures that can make

analyses more realistic. Most important of all, it provides a platform for consensus-building

upon which constructive discussion can be fostered. Hence, it is desirable to construct

decision-making models in a GIS environment. The operation of the traditional

decision-making model involves the tasks of providing and specifying the necessary ‘input’

and waiting for the “black box” model to transform the input into output. The whole process

is completely automatic and mechanical. The complexity and uniqueness of each decision

situation may become standardised for the purpose of ease of implementation. It should be

noted that decision-making is not just a simple and standardised process because it entails

subjective interpretation. Hence, introducing GIS into the decision-making process will

improve the situation by making it more coherent, consistent and objective, allowing the

users and policy-makers to make a certain degree of subjective judgment. One of the main

objectives of using GIS techniques for public policy analysis especially land use planning is

to provide as much as possible high quality information to policy-makers as required, namely

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the information that can describe important spatial relationship, assess the impact of selected

decisions, and evaluate the possible results and consequences of the decision (Malczewski,

2004).

In the site search exercise, one common approach for identifying appropriate sites using GIS

is by overlaying various data layers of information. As pointed out by Janssen, since many

factors and criteria would need to be considered in this process, it would be difficult to

comprehend outputs involving more than four layers (Janssen, el al., 1990). In order to

overcome this limitation, site search could be much improved in most cases by integrating

Multi-criteria Decision Analysis (MCDA) techniques and GIS (Carver, 1991).

MCDA

The MCDA procedures define a relationship between the input maps and the output maps.

The procedures involve the utilisation of geographical data, the decision-makers’ preferences

and the manipulation of data and preferences according to specified decision rules. The

decision rules can be classified into multi-attribute and multi-objective decision-making.

Over the past few decades, a number of multi-attribute evaluation methods have been

implemented in the GIS environment including Weighted Linear Combination (WLC) and

Boolean overlay operations of which the most often used methods are Intersection and Union

(Malczewski J., 2004).

WLC is based on the concept of a weighted average. The decision-makers assign the weights

of relative importance to each attribute map layers. A total score of each spatial unit is

obtained by multiplying the importance weight assigned for each attribute by a “suitability

score” given to the cells for that attribute, and summing the products over all attributes. Both

WLC and overlay operations could be implemented by using GIS systems having overlay

capabilities (Carver, 1991). In order to reduce the degree of subjectivity of determining

importance weights in the WLS approach, some researchers put forward a number of

effective methods. One of these approaches is by making use of the Analytical Hierarchy

Process (AHP) methodology which is considered very useful in obtaining a relatively

consistent set of importance weights for each criterion (Satty, 1990). It uses pairwise

comparison approach to “capture” the preference of the users and decision-makers.

In 2004, Lagrue pointed out that in some cases, it is hard or even impossible to order the

criteria or constraints as they may not be comparable. He proposed an approach to “partially”

order those constraints or criteria for subsequent analysis by MCDA (Lagrue, et al., 2004).

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Nevertheless, it is noted that no matter making use of GIS and/or MCDA for site search,

some known problems related to the physical features of the sites (formed by cluster of cells),

such as contiguity, shape and compactness (Table 1a, 1b and 1c), are found to be difficult to

resolve.

Contiguity Feature

“Contiguity” is measured by the continuity of a cluster of cells.

Undesirable

Cluster of Cells

(Site)

Desirable

Cluster of Cells

(Site)

Table 1a: Contiguity of a Cluster of Cells

Non-continuous cluster

(with gaps between cells)

Continuous cluster

(with no gap between cells)

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Shape Feature

“Shape” is assessed by the “roughness” of the outer edge of a cluster of

cells.

Undesirable

Cluster of Cells

(Site)

Desirable

Cluster of Cells

(Site)

Table 1b: Shape of a Cluster of Cells

Odd shape

(more rough outer edge)

Not odd shape

(less rough outer edge)

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Compactness Feature

“Compactness” is measured by how far a cluster of cells deviates from

a specific norm (e.g. circle).

Undesirable

Cluster of Cells

(Site)

Desirable

Cluster of Cells

(Site)

Table 1c: Compactness of a Cluster of Cells

+

+

More compact

(not deviate much from a circle)

Less compact

(deviate much from a circle)

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2.2 Known Limitations of Site Search Tools

Cova and Church (2000) put forward a Neighborhood Operator Approach (NOA) to handle

optimisation problem in site search, making use of Linear Programming (LP) and the results

of suitability analysis in a grid-based GIS environment (Cova, et al., 2000). In addition,

attempt has been made to overcome known limitations such as contiguity and compactness

through incorporating appropriate constraints into the LP model. A hypothetical case study to

illustrate the NOA methodology is given in Figure 1. Nevertheless, it is noted that the

computation effort required to solve the problem mathematically is quite significant and the

shape problem could hardly be handled by this approach.

Figure 1: Hypothetical Case Study to Demonstrate the

Neighborhood Operator Approach (NOA)

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3. Framework and Guiding Principles for Designing the Site Search Tool

In the planning field, there are a number of studies related to the framework of site search.

To begin with, for the decision-making tools to be effective, the tool designers must be able

to understand the human choice process as well as the needs of the users for information

(Sauter, 1997). In line with this thinking, it is highly desirable that the operating framework

be designed by making reference to the existing practice and preferences of the users

concerned.

3.1 Site Search Framework

To simulate the current practice, the site search exercise will comprise two stages: (i) The

first stage is to determine the suitability of spatial units (in the format of cells of regular size)

based on a set of evaluation criteria to be followed by determining the appropriate cluster of

cells for formation of potential sites, taking into account the spatial relationship including

contiguity and shape of those clusters of cells; (ii) The second stage is to prioritise the

possible sites based on the relative importance (weights) or preference of different factors

involved (Siddiqui, et al., 1996; Manoliadis, et al., 2008; Sener, et al., 2011).

Under this framework, the first stage is to be carried out by the site search tool based on

objective criteria while the second stage is taken up by local planners taking into account

qualitative criteria, local planning environment and views of stakeholders which may

inevitably involve value judgment.

3.2 Design Guiding Principles

To achieve the objectives of developing a tool that can meet the short and long-term business

and operational needs of PlanD, the following guiding principles have been adopted in

designing the tool:

Scientific – In order to produce objective results, the methodology and algorithm involved

should have a strong theoretical base. Being scientific, it is also essential that the results are

replicable, i.e. the same set of results could be produced irrespective of users, time and

environment. To achieve this objective, there should be a standardised workflow built into the

tool, and the set of parameters and criteria adopted for the selection process could be linked

up with the outputs generated and recorded in the geo-database.

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Transparent – Traditional site search tools are often criticised as a “black box” because it is

difficult for users or decision-makers to view the intermediate steps and results. As a result, it

is hard to comprehend or even provide explanation on how the final results are arrived at.

Such tools are not able to cope with the changing planning environment which has become

more transparent and open to allow more public involvement. To overcome these issues, the

tool should be designed in such a way that all the intermediate results, including textual and

spatial information could be viewed and produced throughout the whole site search process.

Flexible and scalable – To respond to the dynamic planning environment, it is important that

the site search tool is able to be adjusted or modified to meet new requirements. To address

this issue, a modular approach should be adopted in designing the new site search tool. Under

this design, adding or modifying the dataset and the computer programme to cater for new

requirements can be carried out as and when needs arise.

4. PlanD’s New Site Search Tool

The operation of PlanD’s new site search tool is based on self-developed methodology and

algorithm which attempts to overcome a number of known limitations of this kind of tool.

The key features of this tool are described below.

4.1 Operation environment and dataset

The model is operated on a raster-based GIS environment, i.e. the whole Hong Kong territory

is sub-divided into small spatial units, e.g. 4m x 4m. In this model, there are 13 categories of

criteria comprising over 45 data layers, and some of which are considered favourable for a

particular type of land use while others are unfavourable.

Data and information (including textual and spatial) that would need to be taken into account

are related to the physical features (e.g. gradient), land status, land use zonings,

environmental factors such as proximity to Potentially Hazardous Installations (PHI) and

environmentally sensitive areas, and other related information. This information is obtained

from government departments concerned and is updated on a regular or need basis.

4.2 Programming

The GIS model is prepared by “Dot Net Programs” and operated according to a specially

designed algorithm to meet the local situation as well as the operational needs of PlanD.

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During the model operation process, special GIS function objects stored in the library of the

Spatial Analyst module of the software ArcGIS would be activated for specific operation,

including overlaying, buffering, focal and zonal statistics.

4.3 Methodologies

To enhance the efficiency and effectiveness of site search, a workflow system has been

incorporated. As a result, users are guided throughout the whole exercise in a user-friendly

graphical environment. This can help standardise the workflow procedures to ensure quality

output in the format of a summary of potential sites. Most important of all, the set of criteria

and parameters adopted in the model for producing the potential sites would also be listed out

for reference and further scenario testing.

4.4 Workflow Procedures

Step 1

The workflow begins with the comparison of information stored in the workstation with that

kept by the central server. In case the information is not up-to-date, the updating process

would be carried out.

Step 2

The second step of the process is to select spatial units in accordance with the specified

selection criteria. In the process, functions operated in a user-friendly environment are

provided for users to view layers of geo-referenced planning-related information and to select

preferred criteria (e.g. zoning, land status, gradient, environmental factors, etc) (Figure 2).

Some of the criteria are mandatory e.g. excluding Sites of Special Scientific Interest which

should be applied to all types of land uses while others are optional and to be determined by

users. Besides, there are built-in functions for users to flexibly adjust some parameters of

special criteria (e.g. gradient, distance to railway station and marine frontage). When the set

of criteria and parameters are finalised, overlay analysis could then be performed for the

purpose of identifying spatial units that could meet the selection criteria.

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Step 3

After identifying spatial units that could meet all the selected criteria, clusters of spatial units

would form a set of “preliminary potential sites”. Proximity of those potential sites to special

features and facilities are analysed by the zonal statistics2 function provided by the software

making use of a self-developed methodology (Figure 3). The potential sites would be further

processed to overcome the problem of contiguity and odd shapes by making use of a

specially designed algorithm operated under the built-in focal statistics3 function (Figures 4

and 5).

2 Zonal Statistics function is an object provided by the software Spatial Analyst of ArcGIS which calculates

statistics on values of a raster within the zones of another dataset. 3 Focal Statistics function is an object provided by the software Spatial Analyst of ArcGIS which gives control

over the neighborhood type (e.g. annulus, circle rectangle, etc) and statistics (e.g. maximum, sum, etc) to be calculated.

Figure 2: Graphical Interface for Viewing and Criteria Selection

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Step a

On the buffer layer, assign “1” to all

spatial units.

On the preliminary potential site layer,

assign a unique value to spatial units of

each preliminary potential site.

Step b

Perform “zonal statistics” function on the buffer layer

and the preliminary potential site layer.

Change the value of spatial units of the preliminary

potential site within the buffer to value of the buffer

layer, i.e. “1”.

Step c

Preliminary potential sites with certain degree of

overlapping with the buffer area are identified.

(Buffer Layer) (Preliminary Potential Site

Figure 3: Diagrammatic Illustration of the Zonal Statistics Function Operation

in Step 3 of the Workflow

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Figure 4: Diagrammatic Illustration of the Focal Statistics Function Operation

in Step 3 of the Workflow

Step a

Perform “focal statistics” function on each continuous spatial unit.

For details of the calculation, please refer to Figure 5.

Step b

Select the spatial units with the “focal statistics” value larger

than or equal to a pre-configured value. (e.g. 0.7)

Step d

Clip the preliminary potential sites

of Step a by the buffered spatial

units generated from Step c.

Step c

Buffer the spatial units selected in Step b.

Preliminary Potential Site 1

Preliminary Potential Site 2

Buffered Spatial Units

Final Potential Site 1

Final Potential Site 2

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Step 4

The potential sites in the format of a cluster of continuous cells would then be converted into

polygons through the vectorisation process followed by smoothening of the polygon

boundaries.

Step 5

After identifying the potential sites, the next step would involve the classification of the

resulting sites into two categories: one category includes those that could meet the minimum

site requirement while the other category is consisted of sites with areas between the

tolerance level and the minimum site requirement.

Figure 5: Diagrammatic Illustration of how the Value of Focal Statistics at a Focal

Point is Derived

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Step 6

The final step is generation of reports on the potential sites including the basic information of

those sites, e.g. site area and plot ratio as well as site plan and location plan of those sites

(Figure 6).

Figure 6: Sample Summary Reports Generated in Step 6 of the Workflow

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4.5 Innovation & Cost Effectiveness

The tool possesses the special features of being scientific, transparent, flexible and scalable. It

is considered the first of its kind as it has made use of self-developed algorithms to overcome

some known problems of site search tools, such as the issues related to contiguity and shape

of site polygons.

The tool is developed by the internal professional and technical staff of PlanD. It is operated

on the existing GIS platform making use of current software licence. As no additional

software and hardware is required, the development cost is considered minimal.

In terms of analyses and production of results, the tool is extremely easy to operate and the

results are derived from an objective and rational algorithm based on sound theoretical basis.

This has greatly enhanced the effectiveness of the existing workflow. Most important of all,

the tool can greatly expedite the processes involved.

5. Milestones

Due to its innovative design and cost-effectiveness, this tool has won two awards for the

PlanD in the Hong Kong ICT Awards 2012, namely Best Public Service Application Grand

Award and Best Public Service Application (Small Scale Project) Gold Award.

Being the first of its kind, this tool will serve as a useful reference model for both local and

international planning organisations. Looking ahead, this model will also open up more

research on similar types of applications in both the academic and professional fields.

Under the current design, the tool is adopted for the first part of the site search exercise. Due

to the design scalability, feasible means could be explored to refine the tool so that it could

assist the local planners and decision-makers at the second stage of the site search exercise

the purpose of which is to provide continued high quality services to the public.

The views expressed in this article are the authors’ own and do not represent the views of the

organisation in which the authors are employed.

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