Abstract. 75% of India’s GDP by 2030.This requires comprehensive

Abstract. The
Smart City Mission of India aims on people, their pressing needs and on the
opportunities to improve lives with an approach mainly towards digital and
information technologies and urban planning best practices, which will require
comprehensive development of physical, institutional, social and economic
infrastructure. By applying smart solutions which will take into account,
qualitative as well as quantitative data of the area of interest, we will be
able to provide and analyze the decisions and queries of various components of
the mission. The aim of this paper is to study various methodologies using
geospatial tools which provides us not only with easily available data but also
apt for freely available software like QGIS, Google maps, etc. to process the
data and also to achieve the vision and fasten the process for urban planners,
administrators and stakeholders for analysis and decision making, taking
Chandigarh as case study.

smart city, geospatial tools, QGIS, remote sensing.

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1   Introduction

accommodate nearly 31% of India’s current population and contribute 63% of GDP
(Census 2011). Urban areas are expected to house 40% of India’s population and
contribute 75% of India’s GDP by 2030.This requires comprehensive development
of physical, institutional, social and economic infrastructure. All are
important in improving the quality of life and attracting people and
investment, setting in motion a virtuous cycle of growth and development. Smart City Mission is an urban
development program by the Government of India launched in 2015 ,it is a five
year plan under  “the Ministry of Housing
and Urban Affairs”, Government of India.

1.1   Smart city mission features and

Smart city
mission features : Promoting mixed land use in area based developments, Housing
and inclusiveness, Creating walk able localities, Preserving and developing
open spaces, Promoting a variety of transport options, Making governance
citizen-friendly and cost effective, Giving an identity to the city, applying
Smart Solutions to infrastructure and services. The strategy applied is
explained below (Fig. 1.1):

FIG 1.1: Smart City


Retrofitting, an area consisting of more than 500 acres will be identified by
the city in consultation with citizens.

envisages an area of more than 50 acres, identified by Urban Local Bodies
(ULBs) in consultation with citizens.

development will introduce most of the Smart Solutions in a previously vacant
area (more than 250 acres) using innovative planning, plan financing and plan
implementation tools (e.g. land pooling/ land reconstitution) with provision
for affordable housing, especially for the poor.

development envisages application of selected Smart Solutions to the
existing city-wide infrastructure



The objective of our study would be :

Ranking sustainable affordable housing sites.

Ranking and siting storm water harvesting sites.

Estimation of rooftop solar photovoltaic potential of
a city.

Using various geospatial tools
like MCDM tool in QGIS software, DEM for slope map generation, GPS, City Engine
etc. for projecting, estimating and assessing various spatial decisions taken
by Urban planners in the creation of smart cities future plans.


Chandigarh, the dream city of
India’s first Prime Minister, Sh. Jawahar Lal Nehru, was planned by the famous
French architect Le Corbusier. Picturesquely located at the foothills of Shivalik
and one of the smart city project under Government of India. It has the
following needs that we will be discussing further in this paper:

Affordable housing siting

Solar master plan for city

Ø  Storm water Harvesting



are four steps involved namely:

1. Planning requirement analysis

2. Data Generation

3. Data Processing

For each component namely affordable housing
siting, solar master plan for city, Storm water harvesting.

Ranking sustainable affordable housing sites


4.1.1 Planning requirement analysis

In this stage we will study the

Site study of housing sites of Chandigarh.

Acquiring various maps, satellite image and
other demographic data from various government departments of Chandigarh
related to affordable  housing .

Ø  The tentative pockets which can
be considered for re-utilization in Chandigarh as listed by the architecture
and planning department are : Industrial houses in sectors 29 and 30, Sector 31, Sector 35b, Sector 35,
Sector 37, Sector 47, Sector 40, Sector 41, Sector 43-a, Sector 44, Sector 50
& 51, Sector 61.


4.1.2 Data Generation

In this stage after studying the
literature review and acquiring the required data, the base data for further
processing is reviewed:

Geo-referencing of landuse map.

Digitization of existing landuse map of


4.1: Existing land use map of Chandigarh 3


the Existing Landuse Map of Chandigarh 3 acquiring 26 feature points from
Trimble Juno Handheld Device (GPS) in Quantum GIS (QGIS 2.18.2).


4.2 : Georeferenced image of existing landuse map of Chandigarh


4.1.3 Data Processing

Processing of data is done using
CORPAS METHOD, as established by Emma Mulliner et.al 10 that the method being
transparent, simple and low calculation as compared to AHP and TOPSIS, could
easily be adopted by any interested parties. It can deal with both quantitative
and qualitative criteria within one assessment. It has the ability to account
for both positive (maximizing) and negative (minimizing) evaluation criteria.


It’s a five stage process :

Stage 1

The first step is normalization of
the decision-making matrix .



where xij is the value of
the ith criterion of the jth alternative, and qi
is the weight of the ith criterion.


Stage 2

The sums of weighted normalized
criteria describing the jth alternative are calculated.





Stage 3

The priority of the alternatives is
determined on the basis of describing positive(+) and negative(-) qualities
that characterize the alternative residential areas. The relative significance
Qj of each alternative Aj is determined according to:



where Smin – the minimum value of  Sj – cancels. The first term of Qj
increases for higher positive criteria S-j, whilst the second
term of Qj increases with lower negative criteria Sj.
Therefore a higher value of corresponds to a more sustainable housing

The prioritization of the alternative residential areas
under consideration is determined in this stage. The greater the value Qj, the higher the priority of the



Stage 5

The final stage is the determination of the alternative
that best satisfies sustainable housing affordability. The residential area
that best satisfies the sustainable housing affordability criteria is expressed
by the highest degree of utility Nj equaling 100%. The degree
of utility Nj of the alternative Aj is determined according to the following

 100%                                                                                  (5)      


Sustainable housing affordability criteria:

House prices in relation to incomes

Safety(Crime level)

Access to employment opportunities

Access to public transport services

Access to good quality schools

Access to shops

Access to health services

Access to child care

Access to leisure facilities

Access to open green spaces 


The above data and criteria’s can be calculated and
processed using QGIS and Python after digitization of the landuse map as shown
in FIG. 4.3


  Figure 4.3: Digitized
map of Chandigarh in QGIS



Ranking and siting storm water harvesting sites


4.2.1 Planning requirement analysis

Geographic Information System (GIS)
facilitates the screening of potentially suitable SWH sites in the urban areas
(Pathak et al. 8). In India, the methodology to select potential sites for
water harvesting were identified by adopting International Mission for
Sustainability Development (IMSD) and Indian National Committee on Hydrology
(INCOH) guidelines in GIS environment.

4.2.2 Data Generation

Geomorphology map,

Land Use Land Cover (LULC)

Road Maps

Drainage Maps


are prepared and the knowledge based
weights will be assigned to all the parameters to compute the ranking of the
sites in the GIS environment.

Figure 4.4: Stacked image of
Chandigarh region, Satellite image acquired from Sentinel -2

4.5: Landuse and Landcover map of Chandigarh Region




Slope Map of Chandigarh using DEM (ASTER)


4.2.3 Data Processing
Evaluation of suitability criteria


Criteria identification for stormwater
harvesting suitability

According to P.M. Inamdar et al.11, Suitability at the screening
stage of planning process needs to consider first if there is a reasonable
match between supply and demand before proceeding to more detailed assessment.
The runoff criterion considered runoff generated from impervious and pervious
areas within the study region. The water demand is calculated from potential
residential and non-residential water uses, such as irrigation of parks.

Data acquisition and processing to create
spatial maps for identified criteria

Spatial maps are generated for runoff, demand and accumulated
catchments, which requires the collection of data such as rainfall, water
demands, impervious-pervious area, digital elevation model (DEM), and digital
cadastre. For the GIS based screening tool, an annual time scale for estimating
runoff was chosen for both stormwater runoff and demand, as the tool only dealt
with preliminary evaluation and ranking of potential stormwater harvesting

Estimation of suitability indices

Spatial maps of runoff and demands are overlaid on the accumulated
catchments. The accumulated catchments can be derived from individual catchment
layer obtained from delineation of DEM. Each drainage outlet of these
accumulated catchments represents a potential site for storm water harvesting
having attributes of runoff and demand.11


Figure 4.6: Methodology Flowchart for ranking
and siting of Storm Water Harvesting Evaluation of screening parameters


Normalization to a Common Scale



where D1
is lower value of range, D2 is upper value of range, DL
is lowest demand of the area, DU is highest demand of the area, and ? and ?  are constants.

Ratio of
Runoff to Demand (RTD):


where RTD1
is lower value of the range, RTD2 is upper value of the range, RTDL
is lowest value of ratio of runoff to demand of the area, RTDU is
the highest value of ratio of runoff to demand of the area, and ? and ? are

Demand Distance:  


where WD1
is lower value of the range, WD2 is upper value of the range, WDL
is lowest value of inverse weighted demand distance of the area, and WDU
is the highest value of inverse weighted demand distance of the area, ? and ? are

Thus, by
solving the above equations, constants can be computed. After computing the
constants, all the values of parameters of different sites are transformed to a
new scale that ranges from D1 to D2 for demand, RTD1
to RTD2 for ratio of runoff to demand and WD1 to WD2
for inverse weighted demand distance by applying the following equations:

For, demand;


Ratio of
runoff to demand;


demand distance;


where DS
is scaled demand, DC is computed demand for each site, RTDS
is scaled ratio of runoff to demand, RTDC is computed ratio of runoff
to demand for each site, WDS is scaled inverse weighted distance and
WDC is computed inverse weighted distance for each site.

of Weights

Principal Component Analysis (PCA) Method

PCA is defined as a linear combination of optimally-weighted
observed variables. In PCA, the most common used criterion for solving the
number of components is to compute eigenvectors and eigenvalues. To solve the
eigenvalue problem, the following steps are followed:-


Let A be a n x n matrix and consider the vector equation:




where    represents
a scalar value.

Thus, if , it represents a solution for
any value of ?. Eigenvalue or characteristics value of matrix A is that
value of ? for which the equation has a solution with . The corresponding solutions are called eigenvectors or
characteristic vectors of A.

(i) Compute the determinant of  With ? subtracted along the diagonal, this determinant starts
with .It is a polynomial in ? of degree

(ii) Find the roots of this polynomial .By solving det () = 0, the n roots are the n
eigenvalues of A. It makes  singular.

(iii) For each eigenvalue , solve ()x = 0 to find an eigenvector
x. Eigenvalues are used to decide weights in proportions to total of

4.3 Estimation of rooftop solar photovoltaic potential
of a city

4.3.1 Planning requirement analysis

In this stage we will study the following:

Google map.

various architectural drawings from government department of Chandigarh.


4.3.2 Data Generation


Digitization of roofs by overlapping present
Google map and architectural drawings, to get the exact footprint of the built
– up area.  


4.3.3 Data Processing


methodology adopted by Rhythm Singh and Rangan Banerjee estimates values of the
Building Footprint Area (BFA) Ratio. Photovoltaic-Available Roof Area (PVA)
Ratio has been estimated by simulations in PVSyst and has to be compared
with relevant values from the literature. Solar irradiance (DNI and DHI) data
will be taken from Climate Design Data 2009 ASHRAE Handbook. Liu Jordan
transposition model has been used for estimating the plane-of-array
insolation. Effect of tilt angle on the plane-of-array insolation received has
been studied to make an optimum choice for the tilt angle. Micro-level
simulations in PVSyst have been used to estimate effective sunshine hours for
the region of interest, to calculate the expected output from the rooftop PV
system. 12

Figure 4.3: Methodology flowchart for Expected output of
rooftop PV System

Estimation of Building Footprint Area (BFA) Ratio :

BFA Ratio =

where Abuilt
is the actual area covered by a built-up structure, and Aplot is the
plot area of the building.

Estimation of Total Building Footprint Area (BFA)

The Building
Footprint Ratio for each of these Land Use Categories has been estimated. Building
Footprint Ratio of the ith Land Use Category is denoted by bi.
Also, the area used for the ith Land Use Category in the jth
sector will be denoted as Aij. Thus the total Building Footprint
Area is given by:


Estimation of PVA from BFA

PVA Ratio is
defined as the ratio of the effective area of solar photovoltaic panels
installed on the rooftop of a building(s) to the total Building Footprint Area
of the building(s).


To ascertain
the PVA ratio for our analysis, we will have to do simulations for some sample
buildings of residential, commercial, office and educational Land Use Types in

Transposition Model

Systems make use of not only the Direct Normal Irradiance (DNI) but also the
Diffuse Horizontal Irradiance (DHI). The transposition model should be able to
account not only for both the DNI and the DHI, but also for the
ground-reflected irradiance, along with the system-design parameters, such as array
orientation, tilt, and tracking, if applicable. Liu-Jordan model (Liu
and Jordan, 1960) has been chosen for estimating all the POA irradiances in our
calculations given as:


Figure 3.6: Parametric inputs to the transposition model,


Expected output from the Rooftop PV Systems :

                                         (15) where EPV is the energy
output of the solar PV panel in an hour and  is the incident solar energy, in an hour, on a
unit area; A is the area of the panel; is  the rated efficiency of the PV panel;  is the efficiency of the power conditioning
unit including the inverter.


5. Conclusion

Ranking sustainable affordable housing sites

Provide and monitor affordable housing development

Aid in identifying areas which would be suitable for
development of affordable housing and areas which may not be suitable.

Assist in identifying areas which may require alternative
forms of investment to enhance affordability and create sustainable

Ranking and siting storm water harvesting sites

methodology should reduce time and subjectivity in creating a set of few
suitable storm water harvesting sites uses from which planners can take a quick
and efficient decision in finalizing suitable sites for Storm Water Harvesting
at a specific location.

Estimation of rooftop solar photovoltaic potential of a

The results
will help in forecasting the rooftop solar photovoltaic potential in MW for the