Office Automation with GIS in
Urban Planning and Management
Anthony Yeh
Centre of Urban Planning andEnvironmental Management
GIS Research Centre
University of Hong Kong

University of Hong Kong

GIS and Urban Planning
Spatial Query &Mapping

GIS

Geo-Processing
Functionsdata

Non-GIS
Database/Datadata

SpatialAnalysis &Modelling

Urban
Planning

Integration of Models with GIS-GIS
commands-macros

GIS

GIS

GIS
importDataexport-GIS
commands-compiledmodelling
program-macros-GIS commands
with modelling
functions-macors-modelling
programs
GIS
database

GIS
database

(a)

(b)

(c)

Loosely-Coupled Architecture

Tightly-Coupled Architecture

Fully Integrated Architecture

for Integrating Models with GIS

for Integrating Models with GIS

for Integrating Models with GISGISdatabase-statisticalanalysis
package

Use of GIS in Urban Planning
General Administration
 Development Control
 Plan Making


CUPEM, The University of Hong Kong

Use of GIS in Urban Planning
General Administration
Office Automation – mapping, data retrieval Public Enquiry – internet map, 3-D map


CUPEM, The University of Hong Kong

Use of GIS in Urban Planning
Development Control
Office Automation
 Land Suitability Analysis
 3-D Analysis


CUPEM, The University of Hong Kong

Use of GIS in Urban Planning
Plan Making
Office Automation - mapping Land Suitability Analysis
 Location of Facilities and Activities Plan Generation
 Plan Evaluation


CUPEM, The University of Hong Kong

Office Automation
Office Work

Computer Network

Human Interactions：
Not working at thesame time
Not working in thesame place
Multimedia
information

System Design

Integration of Texts, Images and Maps
MapObjects

TextualDatabases

Graphics
Databases

001036589
052-01
ARC/INFOSQL Server052-01SQL Server

001036589

ARC/INFO

RelatedInformation

Dynamic Updating
Construction DeptOfficeAutomationSystemConstruction
Database
Updating
GZ
Urban Planning
Database

QueryPlanningLand Use

Updating
Query

Updating

Application

Query

Transport DBSurvey

Inspection

Public Enquiry

Work Flow Managementin Development Control
Ap p li can t

Ur ba n Pla nn in g a nd La nd Man ag em en t Org an iza ti on

Age nts

Even ts

Sta tes

Priva teg Cit
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tiiCvznale cyAPPub lic
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LeL galWork Flow Management

Public Enquiry - InternetOffice Automation withCase-Based Reasoning and GIS

CUPEM, The University of Hong Kong

Introduction (1)Assumptions of KBS (rule-based or model-based)(Han and Kim, 1990):• the problem is clearly specifiable and wellbounded;• the ‘deep knowledge’, i.e. the relations between
the factors or elements of the problem, are
known and can be expressed explicitly;• problem solving methods can be articulated; and• experts agree on solutions, i.e. the cause-effectrelationships are definitely defined.

Introduction (2)• Unfortunately, these requirements are seldom
met in planning.
“It is very difficult to find an urban planning problem
which is well-bound, well understood, and takes a few
minutes to a few hours to solve” (Han and Kim, 1990).

CBR and Its Advantages toUrban Planning Case-Based Reasoning (CBR) is a method ortechnique of KBS which uses the previous
similar case(s) to help to solve a current newproblem (Kolodner, 1993, pp.4).
 CBR can be typically represented by a schematic

cycle comprising four REs : RETRIEVE the most similar case(s); REUSE the case(s) to attempt to solve the problem; REVISE the proposed solution if necessary, and RETAIN the new solution as a part of a new case.

CUPEM, The University of Hong Kong

The CBR Cycle
Problem

RETRIEVE

RETAIN

Case-Base
Library

REU
S
E

REVISE

Confirmed
Solution

Proposed
Solution

Advantages of Case-Based
Systems CBS (1) store knowledge in the form of concrete stories(cases) rather than abstract rules or models.
 CBR provides a good method to involve‘informal’ information’, e.g. personal judgements,
hunches, intuition, hearsay and personal
experiences (Han and Kim, 1990).
 CBR can provide raw but comprehensive andoriginal information to the planner, and leave
space for the intelligence of planners torecognize and understand the problem.

Advantages of Case-Based
Systems CBS (2) solution to a problem can be conveniently savedin the case-base and the system’s knowledge
increases.
 Learning method is very simple and can be

performed by experts themselves during working
process without knowledge engineers’ help.
 CBR will not just tell the planner what he or sheshould do but present previously handled similar
problems to the plannerApplying CBR in Processing
Planning Applications• Processing planning applications is a daily workin the Planning Department of the Hong KongGovernment.
• Make recommendations to the Town Planning
Board

The Integration of CBS(ESTEEM) and GIS (ArcView)• CBS software - ESTEEM• GIS software - ArcView

integrated through DDE of WindowsRepresentation of aPlanning Application Case
A planning application case contains three parts :graphics (map, picture, charts) (handled byArcView)

feature values (dbf file, handled by ESTEEM)

comments (txt file)– valuable tips about this case– list of cases which are relevant to this case

CUPEM, The University of Hong Kong

Integration of CBS and GIS
User Interface
User Inputs

System Outputs
CBR Result(Information about the Retrieved Cases)

GIS(ArcView)
Identifying
Codes

Descriptions ofthe New Application

Contents ofthe Specified Cases

Shape Files

Graphics

CBS(ESTEEM)
Matching
Directories

Text Files

Comments
Case-Base LibraryFeature
ValuesDescribingFeatures

Information about
the Retrieved Cases

Main Retrieval Functions ofthe System (Office Automation)• Spatial Retrieval• Nonspatial Retrieval

CUPEM, The University of Hong KongAn Example of a Planning Application CaseAn Example of Index Map for Spatial Retrieval

Case Retrieval : Similarity Index
Nearest Neighbour Algorithm• assess similarity between stored cases and the newproblem based on matching a weighted sum offeatures (Kolodner, 1993, pp. 354; Watson, 1995).

Nearest Neighbour Algorithm
assess similarity between stored cases and the newproblem based on matching a weighted sum of features(Kolodner, 1993, pp. 354; Watson, 1995). The algorithm
is represented as nIR
 w i  sim (f i , f i )i 1nwii 1wisim
fiR and fiI= importance of dimension i= similarity function for comparing
feature fiR and fiI= values for feature fi in the input (I) andretrieved cases (R), respectively(Kolodner, 1993, pp. 355).

Non-Spatial Retrieval

Similarity-Based Retrieval
If more than one similar case are found, the result
will be listed in a table of ArcView:•••

Identification Code (Appnum)
Approval Probability (Approval)
Similarity Score (Similarity)
Approval Probability = 0 to 1
0 = not approved
1 = approved

List of Similar Cases

Evaluation of a New Case• It will calculate the approval probability of the newapplication based on previous similar cases.
• The New Approval (NA) probability is calculated with thefollowing formula:NA 

 (((ai -0.5)si +0.5) s 2i )
 si2

NA = proposed probability of approving the new applicationai = approval probability of each retrieved casesi = similarity of each retrieved case

Evaluation of a Planning Application

Application
Documents

Features of thenew application

Operations ofCBR Engine
Information for matching

RETAIN

Data of the new case

RETRIEVE

Case LibraryNew Case

REVISE

Similar Case1(Approval Score1)

Similar Case2(Approval Score2)... ...

Similar Casen(Approval Scoren)

Modified Approval Scoreof the new application

Adjustment bythe Experts

Proposed Approval Scoreof the new application

Result of theapplication

Proposed Approval Scoreof the new application

Evaluation Documents

Deliberation ofTown Planning Board

REUSECase-Based Reasoning and GIS
An Office Automation System(text-graphics integration)

An Expert System for Experts

Corporate Memory SystemEnsure Consistent Decision

CUPEM, The University of Hong Kong

Discussion• The integration of CBR with other reasoning
engine to deal with situation without cases.

CBR can be integrated with other reasoning
engines, e.g. rule-based reasoning (RBR) engine
and model-based reasoning (MBR) engine, to build
a more powerful KBS (Bartsch-Sporl, 1995;
Burstein, etc., 1996; Kolodner, 1993, pp.92-96).
• Maintenance of cases - time decay function