1. The document discusses the application of modern geographical tools and techniques such as GIS, remote sensing, and geostatistics in planning and development projects to ensure they are economically feasible, socially acceptable, and environmentally sustainable. 2. Key aspects discussed include creating robust spatial and non-spatial databases, classifying and regionalizing areas using composite indices to identify problem areas, and applying tools of geoinformatics like GPS, remote sensing, and GIS for data acquisition, analysis, and geovisualization to extract geographical information. 3. Modern geographical techniques are crucial for resource appraisal, management, and development by identifying issues, deficit areas, and informing the strategies and inputs of planners and managers.

1. Application
of
Modern Geographical Tools & Techniques
in
Planning and Development

2. Development
Suitable / Effective Planning:
1.economically feasible,
2.socially acceptable, and
3.environmentally sustainable
Robust Databases or GDMs:
1.Spatial / Geographical
2.Non-spatial Attribute

3. “All such data layers must be -
a) current / updated, and
b) reliable (accurate and precise).
These should be meticulously acquired and
scientifically structured
for
direct input in the inter-operable Software
for
analysis and geovisualization
in order to
extract the required “Geographical Information”

4. 1.Acquisition, Storage, Management, and Manipulation
of Data
2.Measurement, Mapping, Monitoring and Modelling
based on the GDMs are done using modern tools:
a) geoinformatics (rs, gis & gps), and
b) geostatistics
Therefore, ‘geography’ or a ‘geographer’ or a
‘geographical scientist’ has a role to play in the
‘national development’
Because only they can provide the raw material
for the formulation of basic strategies for
‘regional planning’

5. Development
Economic, Social, and Environmental
Development needs ‘timely and adequate inputs’
in the ‘problem areas’ identified in terms of certain
‘economic’, ‘social’, and ‘environmental’ attributes.
Input and Execution are the components of
Management Strategy:
these belong to the domain of the Planners.
Geographers: help identification of problem
areas, deficit areas, backward areas using ‘recent
and updated information base’ and ‘modern
tools’. Hence, the Relation between the two.

6. ‘planning / development’ for ‘smart spaces’
management
with
efficient real time organizational structure
in
processes and spaces
smart resource managers
for appraisal and development
maintaining ‘environmental quality control’
resource analysts : geographers

7. …. management is executed
by the officials in the public or
private sector.
…. managers frequently seek
guidance from the ‘resource
analysts’ or ‘geographers’.
…. Developers are concerned
with the actual exploitation or
use of a resource.
…. Manager  Analyst
Developer
Resource DeveloperResource Manager
Resource Analyst
Planning needs efficient –
managers, analysts and developers

8. resource appraisal, management, development ….
building a local / global RIS
relational database
robust in dimension
numerous attributes
multiple variables
multidimensional
multivariate
geospatial database
Planning needs precise -

9. since 1970s … a sharp rise in GRIS facilitated by
satellites / satellite-aided
a) geodetic,
b) cartographic and
c) geostatistical methods
this enormous database needs entirely new
1) methods of analysis and
2) interpretation
Hence, emerged an entirely new branch of learning and
methodology,
“geoinformatics”

10. The use of RS/SA data ─
a) enhances the level of research,
b) covers knowledge domains with scarce reliable materials,
c) enables monitoring of those phenomena which couldn’t
be investigated otherwise,
d) enables to pursue research in inaccessible terrains,
e) enables to explore information /database with tools
formerly not available,
f) enables data mining and exploration, and
g) enables to visualize the data products in amazing ways
previously beyond imagination.
Since 1996, the popularity of geoinformatics has been
reaching a new height, with ─
1) the RS-GPS-GIS integration,
2) their interoperability,
3) more advanced computer technology, and
4) web technology

11. geoinformatics creates ─
(1) an opportunity
for presenting spatial events in a new way;
(2) a situation in which
quantity translates into quality and also,
spatial data of a new quality are created;
(3) development of S&T (especially in areas of
satellite remote sensing, informatics and
other allied fields);
(4) development of gis; and
(5) the origin of global information system

12. gives geography new visions /opportunities in ─
quality data acquisition,
precise spatial analysis,
dynamic analyses, and
identifying the relationships between and among its
various components (habitat, economy & society).
now, the data can be disseminated in
various traditional and modern cartographic forms.
multi-dimensional forms,
dynamic animated images and
various sorts of databases (that combine spatial
information about various aspects of the
environment).

13. Today,
measurement, mapping, monitoring and
modeling are 4 M’s of GIS
and also the key words of the evolving domain of
geography.
The S of GIS stands for ‘software’. Since the late
1990s,
1) GIS has been used for Geographic(al)
Information Science, and
2) more recently, Geographic(al)
Intelligence Services.

14. current geographical research …..
primarily relates to the ─
1. creation and dissemination of geospatial databases,
2. management of natural resources (i.e., soil, water, forests,
animals, minerals, etc),
3. mitigation of natural hazards,
4. disaster management,
5. spatial organization of human activities,
6. regional development and national planning,
7. landuse planning, urbanization and smart growth,
8. detection, measurement, mapping, monitoring and
modeling changes in LULC, EQ quality, etc.
9. spatial management of protected regions / bio-reserves,
10.administration, transport, navigation, trade, election, etc.

19. GENERATION OF DISTRICT INFORMATION MAPS

20. DISTRICT
INFORMATION
MAPS
GENERATED USING
IRS-1D LISS – III
IMAGES

21. Order out of Chaos
Spatial Order / Regularity → The Spatial Pattern of Elements over
the Earth Surface:
This can be defined, identified and analysed with a scientific
understanding of geographical knowledge.
In space – time frame, it can be measured, monitored,
mapped and modelled.
It is this that forms the philosophical foundation of the discipline
of ‘Geography’.
Naturally, it is the Geographer who discovers this Spatial Order.
Spatial Order → Order-forming Processes → Order-forming
Factors for scientific geographical explanation.
Areal Differentiation

22. Identification
of
Problem Areas/Deficit Areas/Negative Areas
by means of
Classification/Regionalisation/Spatial Mapping
using
‘composite indices’
derived from Geo-statistical Analysis
based on
Robust GDMs
prepared
using Tools of Geoinformatics
(i.e., GPS, RS and GIS)

23. Classification
1) Attribute Classification
(i.e., grouping of attributes only)
2) Spatial Classification
(i.e., grouping of spaces on earth, commonly
known as ‘regionalisation’)
regionalisation
to
identify
Backward Areas/Deficit Areas/Negative Areas
for
selecting and prioritising the ‘inputs’ of ‘development’
in order to eliminate ‘regional disparity’

24. Classification/ Regionalisation
Natural or ‘general’ Classification —
based on ‘apparent’
1) similarity
2) common origin
3) common evolution
Artificial or ‘statistical’ Classification —
a) Univariate (1 variable case)
class interval = arbitrary, or statistical
b) Bivariate (2 variable case)
four classes (groupings using either mean or
median)
c) Multivariate (more than 2 variables case)
using PC Scores, Factor Scores, Similarity
Coefficients, Discriminant Functions

25. Selecting the Significant Variable (s)
Univariate Situation: depends on User’s Choice and Needs
Bivariate Situation: depends on the User’s Choice and
Needs, but also may be based on
data exploration
Multivariate Situation: depends on data exploration
Basic Tasks are —
1) to compute and analyze the ‘descriptive statistics’
2) to build and analyze the ‘correlation matrix’
3) to compute and analyze the ‘multiple regression
parameters’
4) to compute and analyze the ‘factor loading matrix’

26. Parameter Mini
mum
Maxi
mum
Mean Standard
Deviation
Vari-
ance
Skew
ness
Kurtosis
HI: Hypsometric integral 0.154 0.630 0.370 0.130 0.017 0.132 -1.058
L / W ratio 1.207 3.260 2.057 0.534 0.285 0.553 -0.164
CR: Circularity ratio 0.364 0.847 0.549 0.104 0.011 0.385 0.082
ER: Elongation ratio 0.473 0.793 0.624 0.064 0.004 0.024 0.740
CC: Compactness coefficient 1.087 1.659 1.368 0.131 0.017 0.270 -0.525
FF : Form factor 0.176 0.494 0.309 0.063 0.004 0.418 0.947
BR : Basin relief (m) 7.000 343.00 105.802 86.620 7503.0 1.105 0.177
θ : Basin slope (degree) 0.009 0.190 0.038 0.037 0.001 2.153 5.588
DI : Dissection Index 0.163 0.940 0.498 0.176 0.031 0.228 -0.269
RI : Ruggedness index 0.012 0.635 0.161 0.167 0.028 1.265 0.848
SF : Stream frequency (No./ sq km) 0.139 5.893 1.563 1.301 1.693 1.136 1.241
Dd : Drainage density (km / sq km) 0.416 2.677 1.369 0.640 0.410 0.379 -1.053
DT : Drainage texture 0.058 13.521 2.878 3.219 10.361 1.382 1.616
Descriptive Measures: 43 Sub-basins of Dulung basin
Example - 1

27. HI L/W CR ER CC FF BR θ DI RI SF Dd DT
HI 1
L/W -0.23 1
CR 0.55 -0.39 1
ER 0.36 -0.81 0.65 1
CC -0.56 0.37 -0.99 -0.63 1
FF 0.36 -0.80 0.65 0.99 -0.62 1
BR -0.78 0.06 -0.70 -0.23 0.73 -0.23 1
θ -0.57 0.03 -0.53 -0.21 0.55 -0.22 0.78 1
DI -0.63 0.31 -0.73 -0.42 0.72 -0.43 0.84 0.56 1
RI -0.72 -0.02 -0.60 -0.21 0.61 -0.21 0.86 0.70 0.63 1
SF -0.08 -0.29 0.00 0.11 -0.01 0.11 0.12 0.28 -0.21 0.44 1
Dd -0.32 -0.21 -0.16 -0.02 0.15 -0.02 0.30 0.41 -0.02 0.65 0.91 1
DT -0.12 -0.24 -0.03 0.06 0.02 0.06 0.15 0.30 -0.15 0.49 0.98 0.91 1
Correlation Matrix: 13 Morphometric Parameters
Example - 2

28. Model Summary
Correlation Coefficient, r = 0.84
Goodness of Fit, R2 = 0.71
Standard Error of Estimate, SE = 0.076
Durbin – Watson Coefficient = 1.275
Sum of
Squares
df Mean
Square
F Sig.
Regression 0.509757 7 0.072822 12.5394 6.45E-08
Residual 0.203262 35 0.005807
Total 0.713019 42
ANOVA
Example - 3

29. Unstandardized Coefficients Standardized
Coefficients
t Significance
β Std. Error βs
-0.13975 1.16389 -0.12007 0.90511
-0.16784 0.71854 -0.13445 -0.23359 0.81666
0.26056 0.56572 0.26146 0.46057 0.64795
1.00431 0.29835 0.48252 3.36618 0.00186
-0.00205 0.00055 -1.35962 -3.70638 0.00072
0.82220 0.53227 0.23536 1.54469 0.13141
0.25929 0.16109 0.34968 1.60959 0.11647
-0.05855 0.15242 -0.07488 -0.38416 0.70318
The multivariate linear regression model is represented by the equation —
HI = — 0.13975 — 0.13445 CR + 0.26146 CC + 0.48252FF —
1.35962 BR + 0.23536 θ +0.34968 DI — 0.07488 RI
Regression ParametersExample - 4

30. UNROTATED ROTATED
1 2 3 4 5 6 7 1 2 3 4 5 6 7
X2 .509 .535 -.391 .177 -.228 -.232 .083 .191 .744 -.463 .129 -.034 -.076 .116
X3 .333 .405 .053 .543 .062 -.046 -.214 -.123 .680 .118 .221 -.075 .280 .017
X4 .736 -.020 .043 .154 -.254 .234 .038 .588 .422 .139 -.007 .049 .203 .319
X5 .747 -.079 -.267 .475 -.119 -.094 .006 .417 .791 .195 -.092 .159 -.082 .073
X6 .201 -.740 .354 .431 .041 .062 -.050 .233 .011 .909 -.132 .049 -.081 -.027
X7 -.187 -.311 .516 .655 .164 -.131 -.039 -.307 .073 .831 .220 -.145 -.085 -.071
X8 -.044 .398 .120 .120 -.406 .535 .466 -.069 -.011 -.155 .180 -.069 .093 .885
X9 .643 -.120 .110 -.544 -.233 -.177 .095 .870 -.061 -.209 .115 -.070 -.025 -.091
X10 .560 .219 -.411 .523 .167 -.102 -.142 .047 .877 .003 -.036 .275 .080 -.092
X11 -.854 .002 .099 .239 .130 .210 -.017 -.810 -.370 .188 -.087 -.071 -.016 .127
X12 .774 .078 .314 -.292 -.052 -.009 -.164 .748 .155 -.024 .249 -.070 .394 -.110
X13 -.441 .541 .118 .000 .153 .181 .091 -.545 -.156 -.266 .302 -.028 .211 .192
X14 .271 .163 .112 -.503 .607 -.247 -.050 .232 -.163 -.266 .419 .331 .218 -.560
X15 .438 .399 .275 -.013 .240 .275 -.190 .164 .203 -.032 .331 .110 .636 .033
X16 .251 -.127 -.367 .038 .554 .212 .291 .042 .106 -.002 -.007 .799 -.031 -.015
X17 .424 .478 .283 .075 .115 -.028 .342 .188 .269 -.108 .659 .098 .131 .212
X18 .186 .345 .225 -.134 .095 .510 -.550 .024 .011 -.075 -.037 -.074 .883 .031
X19 -.045 .390 .750 .140 .122 -.321 .178 -.127 -.032 .182 .849 -.329 .058 -.041
X20 -.550 .289 -.573 .005 -.218 -.074 -.245 -.556 .085 -.504 -.443 -.208 -.127 -.037
X21
.219 -.167 -.438 .030 .633 .225 .189 -.007 .102
5.99E-
005
-.099 .851 .016 -.110
X22 .536 -.619 .051 -.154 -.146 .300 .008 .714 -.100 .354 -.321 .196 .070 .123
Component Matrix: Loadings
Example - 5

31. Factor Score Matrix
GP F1 F2 F3 GP F1 F2 F3
1 -1.410 -0.328 -0.100 32 0.538 -0.519 0.289
2 0.914 0.241 -0.389 33 -0.380 2.456 0.496
3 -0.888 3.086 -0.203 34 0.876 -0.564 -0.552
4 0.003 0.200 -0.512 35 0.415 0.675 -0.321
5 0.074 0.050 -0.506 36 0.029 2.387 0.575
6 -0.092 0.833 -0.157 37 0.364 1.597 0.266
7 1.421 1.657 0.400 38 0.456 -0.492 -1.052
8 -0.984 -0.957 0.657 39 -1.297 -1.151 -0.753
9 -0.086 1.744 -0.101 40 -0.573 -1.146 0.676
10 0.566 1.215 -0.091 41 -1.089 -0.875 -0.315
11 0.597 0.525 -0.091 42 -0.101 0.077 -0.488
12 0.509 0.185 0.270 43 -2.006 -0.57 0.127
13 0.478 1.162 0.481 44 -0.133 -2.007 -1.132
14 0.001 0.312 -0.228 45 1.256 -0.339 0.932
15 0.177 0.213 -0.087 46 -1.231 -0.447 -0.600
16 0.198 0.569 -0.146 47 -0.186 0.557 -0.900
17 1.310 -0.362 -0.323 48 0.383 0.158 -0.120
18 0.435 0.002 -0.294 49 -1.333 -0.709 0.266
19 0.369 0.455 0.150 50 -1.54 -1.015 0.086
20 0.185 0.282 -0.653 51 -0.299 0.505 -0.521
21 -0.514 -0.361 -1.055 52 0.129 -0.59 -0.573
22 1.163 0.337 0.667 53 0.472 -0.21 -0.429
Example - 6

32. Univariate Classification (1 variable situation)
arbitrary classes
based on range and number of class
statistical
mean/standard deviation (mean ± n.σ)
standard scores (0 ± n.1z)
etc.
spatial index
linkage (groupings based on CM or CGA)
location quotient (class interval = 1)
inequality (Gini coefficient with class interval =
0.20/ 0.25 /0.30)

33. Bivariate Classification (2 variable situation)
Identification of Groups from—
Scatter Plots of x1 – x2 with lines of means of x1 and x2
Scatter Plots of x1 – x2 with lines of medians of x1 and x2
Scatter Plots
100
140
180
220
100 140 180 220 260 300 340
x1
x2
Group – 1 : x1 high, x2 low
Group – 2 : x1 high, x2 high
Group – 3 : x1 low, x2 low
Group – 4 : x1 low, x2 high
No. of Groups = 4
Colour Patch Mapping

34. Combinatorial Method: Map Algebra (Nominal Data)
Let there be two sets defining the attributes of two variables, e.g.,
lithology (L) and geomorphology (G) as :
Li = {L1, L2, L3, ... Ln), and
Gj = {G1, G2, G3, ... Gk}.
Hence, the terrain classes are defined by the elements derived from the
union of Li and Gj as ― Ti j = {Li.Gj }
where i = 1, 2, 3, ...... n and j = 1, 2, 3, ...... k

35. Classification
(Raster Data)
using
Statistical Classifier

36. Multivariate Classification (Multi-variable Situation)
Virtually geographical events / objects are inherently multivariate, and
hence suited to multivariate techniques. These allow the researcher to
consider changes in several properties simultaneously in order to explore
the properties of dependence, interdependence and classification.
Softwares are now easily available: SPSS, Statistica, etc
PC Scores / Factor Scores:
to find the directions of maximum variance in the data, to use these to
ordinate data in 1, 2, 3 or 4 dimensions and to interpret them as factors
influencing the data.
Discriminant Functions:
to find the equation of a line that best separates two or more user-defined
(a priori) sub-groups within the dataset and to allocate new data to one
or other of the a priori groups on this basis.
Similarity Coefficients (CA):
to find the magnitude of similarity between pairs of objects or observations
and to use this to produce an empirical classification.

37. Scatter Plots of Factor Score – 1 and 2
Linear Clusters can be identified, which are regarded as Groups in the
Classification Scheme.

38. Multivariate
Classification

39. Factor Score – 1 may also form the basis of
Numerical Classification of the GPs in terms of the
selected 21 variables.
Range of
Factor Score
– 1
No. of Gram
Panchayats
Gram Panchayat
ID
Remarks
>2 1 61 Highly Developed
1 to 2 8 7, 55, 17, 45, 22, 27, 31, 26 Fairly Developed
0 to 1 26 2, 34, 23, 25, 11, 10, 32, 12, 13, 53,
38, 18, 35, 48, 19, 37, 16, 20, 15,
52, 28, 5, 58, 36, 4, 14
Developed
-1 to 0 17 6, 42, 44, 47, 29, 51, 59, 60, 33, 56,
24, 21, 40, 3, 54, 8
Backward
-2 to -1 6 41, 46, 39, 49, 1, 50 Fairly Backward
< -2 3 43, 57, 30 Very Backward

40. Clusters /
Classes
derived from
Scatter Plots

41. Dendrogram Data
from Cluster Analysis
may be used to prepare
Thematic Maps
showing
Spatial Classes / Regions

42. Classification (Spatial Data)
Trend Surface Analysis: z = f (x, y)
(Polynomial Surface of order 2 – 6 can be fitted)
Residual Maps of (z – zc) are used to identify the ‘regions’
Backward Regions :: negative residuals
Developed Regions :: positive residuals

43. Fix the Parameters of Development
Identify the Input
Formulate the Management Strategy
Execute the Plan
Development

44. Thank You
Prof Ashis Sarkar
Presidency University, Kolkata
profdrashis@gmail.com

45. For Publication of Your Valuable Research Article
in
The Indian Journal of Spatial Science
On-line Version: ISSN 2249 – 4316
Print Version: ISSN 2249 – 3921
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