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.

Keywords:

smart city, geospatial tools, QGIS, remote sensing.

1 Introduction

Cities

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

strategies

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

Strategies

In

Retrofitting, an area consisting of more than 500 acres will be identified by

the city in consultation with citizens.

Redevelopment

envisages an area of more than 50 acres, identified by Urban Local Bodies

(ULBs) in consultation with citizens.

Greenfield

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.

Pan-city

development envisages application of selected Smart Solutions to the

existing city-wide infrastructure

2. OBJECTIVE OF

STUDY

The objective of our study would be :

ü

Ranking sustainable affordable housing sites.

ü

Ranking and siting storm water harvesting sites.

ü

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.

3. STUDY AREA

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:

Ø

Affordable housing siting

Ø

Solar master plan for city

Ø Storm water Harvesting

4. METHODOLOGY

There

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.

4.1

Ranking sustainable affordable housing sites

4.1.1 Planning requirement analysis

In this stage we will study the

following:

Ø

Site study of housing sites of Chandigarh.

Ø

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:

Ø

Geo-referencing of landuse map.

Ø

Digitization of existing landuse map of

Chandigarh

Figure

4.1: Existing land use map of Chandigarh 3

Georeferencing

the Existing Landuse Map of Chandigarh 3 acquiring 26 feature points from

Trimble Juno Handheld Device (GPS) in Quantum GIS (QGIS 2.18.2).

Figure

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 .

(1)

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.

(2)

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:

(3)

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

affordability.

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

alternative.

(4)

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

formul

100% (5)

Sustainable housing affordability criteria:

1.

House prices in relation to incomes

2.

Safety(Crime level)

3.

Access to employment opportunities

4.

Access to public transport services

5.

Access to good quality schools

6.

Access to shops

7.

Access to health services

8.

Access to child care

9.

Access to leisure facilities

10.

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

4.2

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

Ø

Geomorphology map,

Ø

Land Use Land Cover (LULC)

Ø

Road Maps

Ø

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

Figure

4.5: Landuse and Landcover map of Chandigarh Region

Slope Map of Chandigarh using DEM (ASTER)

4.2.3 Data Processing

4.2.3.1

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

sites.

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

4.2.3.2 Evaluation of screening parameters

Normalization to a Common Scale

Demand:

(6)

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):

(7)

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

constants.

Weighted

Demand Distance:

(8)

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

constants.

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;

Weighted

demand distance;

(9)

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.

Determination

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:

(10)

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

n.

(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

eigenvalues.

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:

Ø

Chandigarh

Google map.

Ø

Acquiring

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

The

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

1)

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.

2)

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:

(12)

3)

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).

(13)

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

PVSyst.

4)

Transposition Model

Photovoltaic

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:

(14)

Figure 3.6: Parametric inputs to the transposition model,

12

5)

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

1)

Provide and monitor affordable housing development

2)

Aid in identifying areas which would be suitable for

development of affordable housing and areas which may not be suitable.

3)

Assist in identifying areas which may require alternative

forms of investment to enhance affordability and create sustainable

communities.

Ranking and siting storm water harvesting sites

This

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

city

The results

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

city.