This document discusses various methods for delineating and classifying regions, including scientific methods. It describes single variable, fixed index, and variable index methods for delineating regions based on analyzing one or more socioeconomic variables and assigning weights. The document also covers cluster methods, which group regions based on similarities without comparing variables. Specific cluster analysis tasks discussed are choosing clustering methods/algorithms, deciding the number of clusters, naming and interpreting results, and validating the analysis. Examples provided include using language, temperature, income, and other variables to delineate regions.

1. Regional & Metropolitan Planning
Dr. Mohammed Firoz . C
Architect and Urbanist
Department of Architecture and Planning
National Institute of Technology Calicut, India
Lecture 8

2. Lecture : Delineation of regions

3. Classification of Regions
No fixed
boundary
-social
acceptan
ce
District or
state
regionalizat
ion
technique,
Summary of Regions classifications

4. Methods of Delineation of Formal Region
1. Factor analysis methods ( Complex
methods)
2 Weighted index number methods (
Relatively simple methods)
i. Single Index method
Ii Fixed Index Method
iii Variable Index Method
3 Cluster Method
.
Methods of Delineation of
Functional Region
1. Flow Analysis
2. Gravitational Analysis
Methods
Methods of Delineation of Planning
Regions •
1. Theissen Polygon Method
2. Distance Minimization Method
3. Discrimint Analysis Method
4. Graph Theory Method
Methods of Delineation of Regions
Scientific Methods of Delineation of Regions

5. Delineation of regions
Single variable method:
• Regions delineated based on a
single variable. Accordingly,
similarities, regions are
classified.
• Language as a variable- then
compare the language in
geographical areas. Based on
the similarities in language,
the region can be delimitated.
• Temperature as a single
variable –Using temperate as a
variable, regions can be
classified.
•Per-capita income as a variable- Demarcation of backward regions ( If not available
use proxy variables – House hold amenities, assets, access to infrastructure and
vehicles etc)

6. Fixed index method:
• In this method, many variables are selected and weight is assigned in
each variable based on the importance. The index is made from these
variables based on the weight.
• In this case the weight is fixed. 1/3, 1/3 and 1/3 for 3 variables or 50 %,
20 % or 30 % etc as the case may be.
• For example- HDI- in the HDI, there are three main variables (
education, health, income) assigned by the same weight. There are
many sub-variables in each main variable.
• Other examples- Consumer price index , Wholesale price Index
Delineation of regions

7. Delineation of regions

8. Variable index method:
• Similar to fixed index method, but each variable and the range of the
values may have differential weights.
• Generally used for complex analysis.
• Crude ways and statistically arrive at weights
S.NO X1 X2 X3 Nor X1 Nor X2 Nor X3 Weight
(W1)
Weight
(W2)
Weight
(W3)
R1
R2
R3
…
Delineation of regions

9. Cluster method:
The cluster method is used where the comparison of the variable is
not possible or not done.
• Cluster analysis is a convenient method for identifying
homogenous groups of objects called clusters by grouping those
settlements which has high degrees of within- segment
homogeneity and between-segment heterogeneity
2 Cluster
7 Cluster
Delineation of regions

10. • Cluster analysis or clustering is the task of grouping a set of
objects in such a way that objects in the same group (called
a cluster) are more similar (in some sense) to each other than
to those in other groups (clusters).
• Helps in data reduction ( grouping into manageable
interpretations) say if u have 1 Lakhs data points, how do you
interpret
• Grouping main groups to sub groups
Applications:
o Grouping settlements based on homogeneity ( City planning)
o Buying a product ( market research)
o Biology ( grouping a species)
Delineation of regions

12. Cluster analysis Case study

13. There are 4 tasks involved
in cluster analysis
Task 1: Choosing the
appropriate clustering
methods and algorithms
Task 2: Deciding on
number of clusters
Task 3: Naming of the
clusters and
interpretation of the
results.
Task 4: Validation of the
results of Cluster
analysis
13
Decide on the clustering variables
Decide on the clustering procedure
Hierarchical clustering K Mean Clustering Two Step Clustering
Select a measure of
similarity or dissimilarity
Select a measure of
similarity or dissimilarity
Choosing a clustering
Algorithm
Decide on Number of clusters
Validate and Interpret the cluster solution
Schematic representation
Delineation of regions- Cluster analysis

14. Case Study 1
Fathim R Kallingal M.Plan Thesis
Case Study 2
Research paper
Creating a small scale area classification for understanding the
economic, social and housing characteristics of small
geographical areas in the Philippinesrsp3
Delineation of regions- Cluster analysis

15. Thank You