TheSmart City Mission of India aims on people, their pressing needs and on theopportunities to improve lives with an approach mainly towards digital andinformation technologies and urban planning best practices, which will requirecomprehensive development of physical, institutional, social and economicinfrastructure. By applying smart solutions which will take into account,qualitative as well as quantitative data of the area of interest, we will beable to provide and analyze the decisions and queries of various components ofthe mission. The aim of this paper is to study various methodologies usinggeospatial tools which provides us not only with easily available data but alsoapt for freely available software like QGIS, Google maps, etc. to process thedata and also to achieve the vision and fasten the process for urban planners,administrators and stakeholders for analysis and decision making, takingChandigarh as case study. Keywords:smart city, geospatial tools, QGIS, remote sensing.1 IntroductionCitiesaccommodate 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 andcontribute 75% of India’s GDP by 2030.This requires comprehensive developmentof physical, institutional, social and economic infrastructure. All areimportant in improving the quality of life and attracting people andinvestment, setting in motion a virtuous cycle of growth and development. Smart City Mission is an urbandevelopment program by the Government of India launched in 2015 ,it is a fiveyear plan under “the Ministry of Housingand Urban Affairs”, Government of India. 1.1 Smart city mission features andstrategies Smart citymission features : Promoting mixed land use in area based developments, Housingand inclusiveness, Creating walk able localities, Preserving and developingopen spaces, Promoting a variety of transport options, Making governancecitizen-friendly and cost effective, Giving an identity to the city, applyingSmart Solutions to infrastructure and services. The strategy applied isexplained below (Fig. 1.
1):FIG 1.1: Smart CityStrategies InRetrofitting, an area consisting of more than 500 acres will be identified bythe city in consultation with citizens. Redevelopmentenvisages an area of more than 50 acres, identified by Urban Local Bodies(ULBs) in consultation with citizens. Greenfielddevelopment will introduce most of the Smart Solutions in a previously vacantarea (more than 250 acres) using innovative planning, plan financing and planimplementation tools (e.g. land pooling/ land reconstitution) with provisionfor affordable housing, especially for the poor. Pan-citydevelopment envisages application of selected Smart Solutions to theexisting city-wide infrastructure 2.
OBJECTIVE OFSTUDYThe 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 ofa city.
Using various geospatial toolslike MCDM tool in QGIS software, DEM for slope map generation, GPS, City Engineetc. for projecting, estimating and assessing various spatial decisions takenby Urban planners in the creation of smart cities future plans.3. STUDY AREAChandigarh, the dream city ofIndia’s first Prime Minister, Sh. Jawahar Lal Nehru, was planned by the famousFrench architect Le Corbusier. Picturesquely located at the foothills of Shivalikand one of the smart city project under Government of India.
It has thefollowing needs that we will be discussing further in this paper:Ø Affordable housing sitingØ Solar master plan for cityØ Storm water Harvesting 4. METHODOLOGY Thereare four steps involved namely:1. Planning requirement analysis 2. Data Generation3. Data ProcessingFor each component namely affordable housingsiting, solar master plan for city, Storm water harvesting.4.1Ranking sustainable affordable housing sites 4.1.
1 Planning requirement analysis In this stage we will study thefollowing:Ø Site study of housing sites of Chandigarh.Ø Acquiring various maps, satellite image andother demographic data from various government departments of Chandigarhrelated to affordable housing .Ø The tentative pockets which canbe considered for re-utilization in Chandigarh as listed by the architectureand 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 GenerationIn this stage after studying theliterature review and acquiring the required data, the base data for furtherprocessing is reviewed:Ø Geo-referencing of landuse map.Ø Digitization of existing landuse map ofChandigarh Figure4.1: Existing land use map of Chandigarh 3 Georeferencingthe Existing Landuse Map of Chandigarh 3 acquiring 26 feature points fromTrimble Juno Handheld Device (GPS) in Quantum GIS (QGIS 2.18.2).
Figure4.2 : Georeferenced image of existing landuse map of Chandigarh 4.1.3 Data ProcessingProcessing of data is done usingCORPAS METHOD, as established by Emma Mulliner et.al 10 that the method beingtransparent, simple and low calculation as compared to AHP and TOPSIS, couldeasily be adopted by any interested parties. It can deal with both quantitativeand qualitative criteria within one assessment. It has the ability to accountfor both positive (maximizing) and negative (minimizing) evaluation criteria.
It’s a five stage process :Stage 1The first step is normalization ofthe decision-making matrix . (1) where xij is the value ofthe ith criterion of the jth alternative, and qiis the weight of the ith criterion. Stage 2The sums of weighted normalizedcriteria describing the jth alternative are calculated. (2) Stage 3The priority of the alternatives isdetermined on the basis of describing positive(+) and negative(-) qualitiesthat characterize the alternative residential areas. The relative significanceQj of each alternative Aj is determined according to: (3)where Smin – the minimum value of Sj – cancels. The first term of Qjincreases for higher positive criteria S-j, whilst the secondterm of Qj increases with lower negative criteria Sj.Therefore a higher value of corresponds to a more sustainable housingaffordability.The prioritization of the alternative residential areasunder consideration is determined in this stage.
The greater the value Qj, the higher the priority of thealternative. (4) Stage 5The final stage is the determination of the alternativethat best satisfies sustainable housing affordability. The residential areathat best satisfies the sustainable housing affordability criteria is expressedby the highest degree of utility Nj equaling 100%. The degreeof utility Nj of the alternative Aj is determined according to the followingformul 100% (5) Sustainable housing affordability criteria:1. House prices in relation to incomes2. Safety(Crime level)3. Access to employment opportunities4. Access to public transport services5.
Access to good quality schools6. Access to shops7. Access to health services8. Access to child care9. Access to leisure facilities10. Access to open green spaces The above data and criteria’s can be calculated andprocessed using QGIS and Python after digitization of the landuse map as shownin FIG. 4.3 Figure 4.
3: Digitizedmap of Chandigarh in QGIS 4.2Ranking 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 forwater harvesting were identified by adopting International Mission forSustainability Development (IMSD) and Indian National Committee on Hydrology(INCOH) guidelines in GIS environment.4.
2.2 Data GenerationØ Geomorphology map,Ø Land Use Land Cover (LULC)Ø Road MapsØ Drainage Maps are prepared and the knowledge basedweights will be assigned to all the parameters to compute the ranking of thesites in the GIS environment. Figure 4.4: Stacked image ofChandigarh region, Satellite image acquired from Sentinel -2Figure4.5: Landuse and Landcover map of Chandigarh Region Slope Map of Chandigarh using DEM (ASTER) 4.
2.3 Data Processing22.214.171.124Evaluation of suitability criteria Criteria identification for stormwaterharvesting suitability According to P.
M. Inamdar et al.11, Suitability at the screeningstage of planning process needs to consider first if there is a reasonablematch between supply and demand before proceeding to more detailed assessment.
The runoff criterion considered runoff generated from impervious and perviousareas within the study region. The water demand is calculated from potentialresidential and non-residential water uses, such as irrigation of parks.Data acquisition and processing to createspatial maps for identified criteria Spatial maps are generated for runoff, demand and accumulatedcatchments, which requires the collection of data such as rainfall, waterdemands, impervious-pervious area, digital elevation model (DEM), and digitalcadastre. For the GIS based screening tool, an annual time scale for estimatingrunoff was chosen for both stormwater runoff and demand, as the tool only dealtwith preliminary evaluation and ranking of potential stormwater harvestingsites.Estimation of suitability indicesSpatial maps of runoff and demands are overlaid on the accumulatedcatchments. The accumulated catchments can be derived from individual catchmentlayer obtained from delineation of DEM.
Each drainage outlet of theseaccumulated catchments represents a potential site for storm water harvestinghaving attributes of runoff and demand.11 Figure 4.6: Methodology Flowchart for rankingand siting of Storm Water Harvesting 4.2.
3.2 Evaluation of screening parameters Normalization to a Common ScaleDemand: (6)where D1is lower value of range, D2 is upper value of range, DLis lowest demand of the area, DU is highest demand of the area, and ? and ? are constants.Ratio ofRunoff to Demand (RTD): (7) where RTD1is lower value of the range, RTD2 is upper value of the range, RTDLis lowest value of ratio of runoff to demand of the area, RTDU isthe highest value of ratio of runoff to demand of the area, and ? and ? areconstants.WeightedDemand Distance: (8)where WD1is lower value of the range, WD2 is upper value of the range, WDLis lowest value of inverse weighted demand distance of the area, and WDUis the highest value of inverse weighted demand distance of the area, ? and ? areconstants.Thus, bysolving the above equations, constants can be computed. After computing theconstants, all the values of parameters of different sites are transformed to anew scale that ranges from D1 to D2 for demand, RTD1to RTD2 for ratio of runoff to demand and WD1 to WD2for inverse weighted demand distance by applying the following equations:For, demand; Ratio ofrunoff to demand; Weighteddemand distance; (9)where DSis scaled demand, DC is computed demand for each site, RTDSis scaled ratio of runoff to demand, RTDC is computed ratio of runoffto demand for each site, WDS is scaled inverse weighted distance andWDC is computed inverse weighted distance for each site.
Determinationof WeightsPrincipal Component Analysis (PCA) MethodPCA is defined as a linear combination of optimally-weightedobserved variables. In PCA, the most common used criterion for solving thenumber of components is to compute eigenvectors and eigenvalues. To solve theeigenvalue problem, the following steps are followed:- Let A be a n x n matrix and consider the vector equation: (10) where representsa scalar value.Thus, if , it represents a solution forany value of ?. Eigenvalue or characteristics value of matrix A is thatvalue of ? for which the equation has a solution with .
The corresponding solutions are called eigenvectors orcharacteristic vectors of A.(i) Compute the determinant of With ? subtracted along the diagonal, this determinant startswith .It is a polynomial in ? of degreen. (ii) Find the roots of this polynomial .By solving det () = 0, the n roots are the neigenvalues of A. It makes singular.(iii) For each eigenvalue , solve ()x = 0 to find an eigenvectorx. Eigenvalues are used to decide weights in proportions to total ofeigenvalues.
4.3 Estimation of rooftop solar photovoltaic potentialof a city4.3.1 Planning requirement analysisIn this stage we will study the following:Ø ChandigarhGoogle map.Ø Acquiringvarious architectural drawings from government department of Chandigarh. 4.3.2 Data Generation Digitization of roofs by overlapping presentGoogle map and architectural drawings, to get the exact footprint of the built– up area.
4.3.3 Data Processing Themethodology adopted by Rhythm Singh and Rangan Banerjee estimates values of theBuilding Footprint Area (BFA) Ratio. Photovoltaic-Available Roof Area (PVA)Ratio has been estimated by simulations in PVSyst and has to be comparedwith relevant values from the literature. Solar irradiance (DNI and DHI) datawill be taken from Climate Design Data 2009 ASHRAE Handbook. Liu Jordantransposition model has been used for estimating the plane-of-arrayinsolation. Effect of tilt angle on the plane-of-array insolation received hasbeen studied to make an optimum choice for the tilt angle.
Micro-levelsimulations in PVSyst have been used to estimate effective sunshine hours forthe region of interest, to calculate the expected output from the rooftop PVsystem. 12 Figure 4.3: Methodology flowchart for Expected output ofrooftop PV System1) Estimation of Building Footprint Area (BFA) Ratio :BFA Ratio = where Abuiltis the actual area covered by a built-up structure, and Aplot is theplot area of the building.2) Estimation of Total Building Footprint Area (BFA)The BuildingFootprint Ratio for each of these Land Use Categories has been estimated. BuildingFootprint Ratio of the ith Land Use Category is denoted by bi.Also, the area used for the ith Land Use Category in the jthsector will be denoted as Aij. Thus the total Building FootprintArea is given by: (12)3) Estimation of PVA from BFAPVA Ratio isdefined as the ratio of the effective area of solar photovoltaic panelsinstalled on the rooftop of a building(s) to the total Building Footprint Areaof the building(s).
(13) To ascertainthe PVA ratio for our analysis, we will have to do simulations for some samplebuildings of residential, commercial, office and educational Land Use Types inPVSyst.4) Transposition ModelPhotovoltaicSystems make use of not only the Direct Normal Irradiance (DNI) but also theDiffuse Horizontal Irradiance (DHI). The transposition model should be able toaccount not only for both the DNI and the DHI, but also for theground-reflected irradiance, along with the system-design parameters, such as arrayorientation, tilt, and tracking, if applicable. Liu-Jordan model (Liuand Jordan, 1960) has been chosen for estimating all the POA irradiances in ourcalculations 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 energyoutput of the solar PV panel in an hour and is the incident solar energy, in an hour, on aunit area; A is the area of the panel; is the rated efficiency of the PV panel; is the efficiency of the power conditioningunit including the inverter. 5.
ConclusionRanking sustainable affordable housing sites 1) Provide and monitor affordable housing development2) Aid in identifying areas which would be suitable fordevelopment of affordable housing and areas which may not be suitable.3) Assist in identifying areas which may require alternativeforms of investment to enhance affordability and create sustainablecommunities.Ranking and siting storm water harvesting sites Thismethodology should reduce time and subjectivity in creating a set of fewsuitable storm water harvesting sites uses from which planners can take a quickand efficient decision in finalizing suitable sites for Storm Water Harvestingat a specific location.Estimation of rooftop solar photovoltaic potential of acityThe resultswill help in forecasting the rooftop solar photovoltaic potential in MW for thecity.