Some of Dr. Nishant Sinha's Research Papers


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GIS, Insurance, Catastrophe Modeling, Remote Sensing, Climate Change

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Some of Dr. Nishant Sinha's Research Papers

  1. 1. Volume 2, Issue 3, March 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: Development of SDSS for Ensuring Insurers Nishant* Neena Priyanka P. K. Joshi Natural Resources, TERI University Natural Resources, TERI University Natural Resources, TERI University Pitney Bowes Software, NOIDA Pitney Bowes Software, NOIDA Abstract— In arena of catastrophe management in India, managing risk at varied levels along with timely and effective decision making by Insures/Reinsurers is a complex task. This unique dynamic system makes the assessment and management of enterprise-wide risk much more multidimensional and uncertain resulting in failure of connection between lines of business. Geospatial technology viz. remote sensing, GIS and SDSS has emerged as powerful aid to assist risk managers and decision makers to manage risk for several years. However, if used alone, it has limited functionality. This paper presents the conceptual design and development of remote sensing and GIS-assisted Spatial Decision Support System (SDSS) to improve property insurance underwritings that involves procedural and declarative knowledge. SDSS, coined as Insurance Profiler (InsPro), integrates geocoder, multi-criteria risk evaluation techniques and state-of-art web interface framework which is applied at three phases viz. geospatial visualization and querying of insured points, multi- criteria comprehensive evaluation of risk and report generation. It is flexible in that it can be adapted in evaluation of any property type. It is scalable because the system can be designed at local, regional, national or international level as being data driven .The system is integrative because it incorporates a number of different data types and sources (e.g., multispectral remote sensor data, numerous thematic information on hazard and vulnerability), and geo-statistical tools and techniques, and human expert knowledge of the seismic region. The system is designed to be flexible, scalable and integrative. Thus, this SDSS tends to cater the needs of users at all levels viz. risk analyst, insurer, brokers, reinsurers etc. to manage share and interact effectively and reliably. Keywords— Catastrophe, SDSS, Insurers, GIS, Real Estates, InsPro I. INTRODUCTION Risk analysis is a complex task that entails consideration of complex parameters which are difficult to interpret and quantify ([1]–[3]). In addition, risk analysis involves a comprehensive database to model uncertainty and vagueness. As a consequence, insurers/reinsurers fail to evaluate and underwrite actual risk. In addition, there are other shortcomings, such as poor visualization of insured points and risk zones ([4]–[7]) slow model based update of information that further contributes to complexity and underestimation of potential loss from natural hazards and even failure and insolvency of some insurance companies. Fig. 1 Country-wise total natural disaster events: 1976-2005 (Source: EM-DAT) The catastrophe imposed risk in India can be described as worst as being high on number of events and intensity as depicted in Figure 1, owing to an elevated probability of hazard occurrences and high exposure due to geographical, topographical and socio-economic settings [8]. This trend is expected to continue as higher concentration of populations and built-ups continue to develop in areas susceptible to natural hazards. India’s vulnerability to natural catastrophes coupled with rapid growth and transformation of the insurance market, it is crucial to address this high level vulnerability in order to avoid the present scale of losses and damage. Despite leveraging such transfer of risk through integrated product choices and schemes, there are very limited sections of population (0.5%) in India those have any kind of property insurance [9]. There are various other inadequacies such as poor location identification of insured exposures on paper maps, primitive modelling assumptions and slow update of information that add to complexity of insurers/reinsurers. Such limitations aid to underestimation of severe nature of disaster and associated potential loss resulting in unexpected significant drop in surplus and bankruptcy of some insurance companies. Beside these, there are other reasons which could be attributed for such low profiling. This include a general lack of awareness about insurance practices, two-dimensional nature of spreadsheets and reports which requires skill set for understanding, lack of spatial database that could provide easy visualization and data querying, absence of scientifically designed enterprise solutions focused for insurance underwriters to promote faster and effective decision making. Against the above-stated deficiencies of current systems,
  2. 2. Volume 2, Issue 3, March 2012 © 2012, IJARCSSE All Rights Reserved Page | 332 adoption of geospatial technology for niche areas such as actuarial underwritings, claims management, risk based pricing, could be very useful as much of the data required within these domains contain geographic component ([11]– [14]). II. GEOSPATIAL TOOL & TECHNOLOGY IN INSURANCE Remote Sensing (RS) and GIS together have emerged as useful tool for insurers/reinsurers because of spatio-temporal component involved within [15] and its ability to integrate large volume of information through repertoire of analytical tools for disaster risk management ([16], [17]).The system is further aided by development of modelling approaches such as catastrophe models with basic components including hazard, exposure, vulnerability, and loss ([18], [1]). The derived models tend to quantify the likelihood of disasters occurring and estimate the extent of incurred losses, both from single event and multiple events and eventually help in development of spatial decision support system (SDSS). In the basic framework of risk management, a combination of RS, GIS and SDSS; RS & GIS can be used in potential hazard zonation, inventory preparation, whereas SDSS for vulnerability assessment, loss estimation and in decision processes by key stakeholders [16]. Insurance companies are increasingly using SDSS as an essential business tool [15] to mitigate exposure to risk by ensuring a wide spatial distribution of policyholders. III. MULTI-CRITERIA DECISION BASED RISK ASSESSMENT Solving problems and taking rational decisions in a complex domain such as risk assessment needs integration of information, knowledge and expertise from a wide range of disciplines. It also needs some kind of support mechanism (i.e. tools) that can assist planners and decision makers in informed and rational decision making. Risk assessment being a problem of multiple dimensions; involving multiple criteria, conflicting objectives, and its planning is considered as a multi-criteria decision making (MCDM) problem that needs specialized tools and techniques that can support a systematic approach of decision analysis. MCDM is characterized by the need to evaluate a finite set of alternatives on the basis of conflicting and incommensurable criteria of quantitative, qualitative or both in nature and based on preference values of the alternatives on permissible scale measure the overall preference values ([19], [20]). For this reason, there has been a growing interest in applying GIS and spatial MCDM to risk analysis which is very much evidenced by an increasing number of published articles on this topic. Entrenched in a GIS milieu, MCDM technique provide the framework of a SDSS which improves the effectiveness of decision making process by incorporating decision maker’s judgments and computer based programs ([19]-[22]). In the domain of risk planning, MCDM approach is considered essential because of its demonstrated ability to integrate multiple criteria, preferences of different groups, expert’s knowledge, and with- standing spatial; non-spatial and inexplicit data from various sources. The most significant characteristics of this methodology are that they are transparent to the participants. Such methodologies make it possible to integrate risk assessment information in knowledge structures and networks, and opens prospects for improved risk mitigation and planning to investigate a number of multiple objectives (criteria). With this backdrop, it is obvious that the deductive, well- structured problem-solving methodologies are inadequate when it comes to the analysis of urban area risk assessment as there are multiple representations or understandings on this concept. Therefore, identifying an appropriate design structure for assessment procedure among competing options is perhaps the most important part of analysis. The design must recognize divergent perspectives of urban morphology and hence associated risk. In this paper, we deduce that one of the useful alternatives to design risk assessment procedure is to adopt an inductive approach based on spatial MCDM. We have chosen earthquakes as a subject of this research not only because of their severe impacts on urban area, but also because they have provided the basis for some of the fundamental physical, technological and social research in field of natural hazards: work that has often been a model for studies of other hazardous natural agents. The objectives formulated for current study focuses on development of a geospatial and web analytics based actuarial solution for insurers/reinsurers which would minimize uncertainty and cater to their needs for profiling overall scenario of property risk. IV.RESEARCH NEEDS A small region of capital city Delhi, India is taken up to demonstrate this concept of risk assessment using web based solutions. The study area is characterized as susceptible to earthquake and as majority of the population dwell in urban areas and even the slightest structural and physical damages will affect lives immensely. The prime objective of present research is to develop a generic methodology which is applicable to any study area. Nevertheless for initial development, a test site is required. One of the main considerations of selecting study site was availability of several experts from different discipline who are well acquainted with study area. Also, being the metropolis and capital city, spatial and non-spatial data for several themes were readily available. Most importantly, study area characterizes a typical urban landform with socio-economic activities revolving around risk planning and mitigation. Such characteristics suit selection of area for case study to demonstrate applicability of methodology. Despite of gaining importance and widespread acceptance of multi-criteria analysis based decision-making in risk assessment and regional planning; it is still in its infancy stage in India. In this respect, this study will have a significant contribution to explore potentials of this approach to address issue of risk management. V. SDSS ARCHITECTURE - INSURANCE PROFILER (INSPRO) The development of SDSS solutions for actuarial Industry, coined as InsPro – Insurance Profiler, involved four (4)
  3. 3. Volume 2, Issue 3, March 2012 © 2012, IJARCSSE All Rights Reserved Page | 333 critical areas of development (Figure 2), as illustrated stepwise in subsequent section:  Development of geocoding engine  Creation of hazard, vulnerability score maps  GIS database integration  Development of Web-based solution - InsPro Fig. 2 Schematic architecture of Insurance Profiler (InsPro) A. Development of geocoding engine Geocoding is the process of assigning geographic coordinates to data that contain addresses. The coordinates assigned to each address turn each record into a geographic object that can be potentially displayed on a map. This was designed as first ―gateway‖ into InsPro application. The development of geocoder involved: 1) Electronic Research work and use of local knowledge: The first and foremost step in this was identification of administrative hierarchy. In administrative structure of India it was seen that it is composed of states which are further divided into districts (zila) and districts are further split into into sub-districts, locally known as Tehsils/Talukas. The block is the next level of administrative division following tehsil. Villages are often the lowest level of administrative divisions in India. Thus, these datasets were captured to be used for address identification. In addition, building footprint data was created with house numbers/name for urban centres so that geocoder was capable of approximately locating houses. These datasets were used for tagging POIs, streets, localities and town datasets as these are building block for geocoder. Second step was determination of postal formats determination. In India, there are 8 PIN regions and the first digit indicates one of these regions. Postcode is however six (6) digits long where the first 2 digits together indicate sub region or one of postal circles, first 3 digits together indicate a sorting / revenue district and last 3 digits refer to delivery post office. Thus, recognition of postal hierarchy helped in creating an approximate postal reference data for India. This was another milestone in development of Geocoder. Third step was to extract address patterns/ formats. General pattern of address followed in India includes writing of recipient’s name in first line followed by house number/street name, locality name, district, postal code and state name. The address pattern identification helped in development of various permutations and combinations of address being entered by user and hence further enhancement of geocoder to fetch correct results or nearest match on hits being made by user by using these permutations and combinations of address pattern. The fourth and last step was determination of thoroughfare types: In India, different thoroughfare types identified include motorized ways, non-motorized ways and waterways. The local terms used for these thoroughfares such as highway, flyover, expressway, lane, way, avenue, gali, path, road, marg, sadak, walk, street, channel were added to the geocoder configuration files in order to determine best possible match. Also, prefix and suffixes used with road names such as NH4, directional words viz. north etc were incorporated which further assisted in enhancement of Geocoder. 2) Data build: Geospatial files viz. GeoInfo, PostInfo, POIs (Point of Interests) and StreetRef files were created to be used as input for Geocoder. GeoInfo files were point data containing information on capitals, cities, towns, villages whereas PostInfo file were polygon data with information on postal codes. StreetRef file were polyline data with details on streets names, their types, pre-post fix, house number ranges. POIRef was point file with information on landmarks viz., business hubs, commercial centres, stations, scenic places, shopping centres. All these spatial files were tagged with administrative level information. 3) Component build: Based on electronic database searches, data build, and local knowledge, configuration files, to be used by MapMarker geocoding engine, was created which contained following information besides spatial data files:  Coordinate precision information: This was to determine number of decimal places of coordinate values should be used to precise the results. This was set as 6.  Word dictionaries: Created with words generally being used by locals in writing address. Minimum quality of words used for searching areas, streets and postcodes with values assigned between 0.0 and 1.0, with a value of 1.0 indicating the words have to be perfect matches.  Pre filtering information –This allowed showing up of results with candidates having matching search area words thereby reducing false positives and speed up matching process due to reduced number of candidates.  Searches based on alternate key: This was used to determine the use of alternates keys based on transposed characters, missing characters, incorrect characters, extra characters etc.  Soundex parameterization: This involved grouping of characters or group of characters to get best possible match based on sound property. For example - soundex_replace_1=C,ts; soundex_replace_2 = A,aw.  Weights assigning: The street information and post address were assigned with scores for obtaining better results during reverse geocoding. For example: POIs data (such as landmarks) was given high scores while matching data as in Indian context these POIs are taken as identifier such as near XYZ place. Hence, better geocoding precision.
  4. 4. Volume 2, Issue 3, March 2012 © 2012, IJARCSSE All Rights Reserved Page | 334  Assigning precision code: Coordinates to an address based on how well it matched in address dictionary was assigned a code based on precision of matched results. The code represents success or failure of geocoding operation and conveys information about quality of match. Each character of code provides information on how precisely geocoded results matched each address component. The code is an alphanumeric code of 1–10 characters and falls into the categories such as single unique match; postcode centroid match and geographic match. Each category is further subdivided into sub- categories. Table I enlist geocoded precision code results and their accuracy description. TABLE I: GEOCODING PRECISION CODE DESCRIPTION Single unique match (S category): This implies record was matched to single address candidate. First character (S) reflects that geocode component found street address that matched record. First two characters of S result code indicate type of match found. Results Accuracy Level S5 match located at street address position S4 match located on the street centroid SX match located at street intersection Street level geocode result, codes S4 and S5 are followed by additional characters, indicating details of match precision. These result characters appear in order, immediately after S4 or S5. H Exact match on house number P Street prefix direction S Street suffix direction C Exact match on town name Z Exact match on postcode name A or U A if address is returned from the address dictionary. U if address is returned from the user dictionary - If any field does not have an exact match, then its position will be replaced by a dash Example of geo-coding results explained below: S4-PSCZA Street centroid match (S4) with exact match on all other criteria except house number S5HPS—A Street centroid match (S5).Exact match on house number, but no exact match on town name or postcode SX Street intersection match Geographic centroid matches (G category): The matches under this category indicate that a match was made at geographic (town or locality) level. This may be because no street match was possible and geocoder results fell back to geographic area. Results Accuracy Level G3 geographic match with town centroid - areaname3 G4 geographic match with locality - areaname4 If Areaname3 input matches both town and locality names, then G3 candidates appear at top of candidate list followed by G4 candidates. When both town and locality is provided as input, highest scoring candidates are listed at top. Exception is when geographic input matches both town and locality. Thus, configuration and data build binary files were used in geocoder engine to create geocoding components which was capable of handling Single/Multiline input, address correction, reverse geocoding and bulk/batch geocoding. However challenges faced during the development of Delhi Geocoder were non-availability of street names, unorganized addresses, house numbers etc. and variation in address pattern. Thus, this aroused difficulty in geocoding at street level as most of addresses do not include street names and hence geocoded at geographic levels than street level. Besides, street interpolation can’t be done because of non- standard house numbers. Also, address search precision is poor due to above stated deficiencies. Thus, with these limitations, geocoder works on the hierarchy of identifying pincode and locality, identifying the street (as already segmented), identifying POI/Landmark, and identifying administrative boundaries for getting precise results. B. Creation of hazard, vulnerability score maps The integrated system designed here, is divided into two phases of risk score generation: static and dynamic phase. a) Construction of composite hazard and vulnerability layer score map which was preset in SDSS formed static component and run-time generation of risk maps formed dynamic component based on user’s permutation and combination of vulnerability classes. Hence, an aggregate risk score map was developed for a particular property under insurability consideration. For seismic hazard score map generation, Saaty’s (2000) analytical hierarchy process, a MCDM methodology, in a participatory decision-making framework was used to rank and develop seismic hazard and vulnerability layer score map of study area [23]. Nine experts (two academic researchers, three from government organizations, and four from nongovernment organizations who work closely in seismic risk assessment areas) were engaged to perform pair-wise comparison of criteria and weights were determined at two levels of hierarchy i.e attribute values of the map layers and map layers to generate hazard and vulnerability layer score maps. Pair-wise comparisons were carried out based on Saaty’s semantic nine-point scale which relates numbers to judgments (Table II). TABLE II: PAIR-WISE COMPARISON SCALE Intensity of Importance Definition Explanation 1 Equally important Two elements contributes equally to the property 3 Moderately important Experience and judgment moderately favor one element over other 5 Strongly important Experience and judgment strongly favor one element over other 7 Very Strongly important An element is strongly favored and its dominance is demonstrated in practice 9 Extremely The evidence favoring one
  5. 5. Volume 2, Issue 3, March 2012 © 2012, IJARCSSE All Rights Reserved Page | 335 important element over another is of the extremely highest order of affirmation 2, 4, 6, 8 Intermediate values Compromise is needed between two judgments Reciprocal of above numbers If an activity has one of the above numbers assigned to it when compared with a second activity has the reciprocal value when compared to the first. In this way different criterion were weighted with homogenous measurement scale. Through this method, the weight assigned to each single criterion reflected the importance which every expert involved in the project attached to objectives. Once experts were through with comparative analysis, weights and consistency ratios besides calculating eigenvalue, Consistency Index (CI) and Random Index (RI) were calculated (Refer Saaty and Vargas, 1993 for calculation steps). The pair-wise comparison matrices for each expert that met the consistency ratio (CR) i.e CR < 0.1 were then aggregated using geometric mean (Saaty, 2000). A geometric mean was used instead of arithmetic mean when comparing different criteria and finding a single "figure of merit" for these criteria as geometric mean "normalizes" the ranges being averaged and hence no range dominates the weighting, and a given percentage change in any of the properties has the same effect on the geometric mean. Thus, through MCDM approach of pair-wise comparisons weights of criterions were determined for below enlisted criteria and final hazards score maps was generated. 1) Seismic zones: A seismic zone is a region in which the rate of seismic activity remains fairly consistent. There are five seismic zones named as I to V as details given below:  Zone V - Covers the areas liable to seismic intensity IX and above on Modified Mercalli Intensity Scale. This is the most severe seismic zone and is referred here as Very High Damage Risk Zone.  Zone IV - Gives the area liable to MM VIII. This, zone is second in severity to zone V. This is referred here as High Damage Risk Zone.  Zone III - The associated intensity is MM VII. This is termed here as Moderate Damage Risk Zone.  Zone II - The probable intensity is MM VI. This zone is referred to as Low Damage Risk Zone.  Zone I - Here the maximum intensity is estimated as MM V or less. This zone is termed here as Very Low Damage Risk Zone. 2) Peak Ground Acceleration (PGA): Peak ground acceleration is the maximum value observed from an accelerograph recording in an earthquake. Because it is a value derived readily from ground motion records, there is a much larger global dataset of PGA available. 3) Soil characteristics: The soil parameter controls relative amplification of ground motion. The soil value is actually an index related to the shear-wave velocity (Vs) of the top 30 meters at a site. This material property has been shown to correlate well with shaking amplification; lower Vs generally result in a larger ground motion than hard materials with a high velocity. 4) Liquefaction: Liquefaction is form of ground failure that can be triggered by strong shaking. It is the temporary transformation of a solid soil into a liquid state. It can occur when certain types of saturated, unconsolidated soils are subjected to repeated, cyclical vibration and therefore most commonly occurs during earthquakes. 5) Geology: Geology is the study of the Earth, the materials of which it is made, the structure of those materials, and the processes acting upon them. Geology plays an important role in determining seismic hazard as regional geology enables in assessment of sources and patterns of earthquake occurrence, both in depth and at the at the surface. 6) Land use: Most of the Delhi area has changed land use from the forest to agricultural areas to urban centres to business hubs especially in the central portion. This has actually led to increase in the urban population, decrease in open spaces and forested areas. Delhi has also experienced a large population in growth in the last decades and this combined with rapid infrastructure development has intensified the seismic vulnerability in the area. 7) Proximity to the fault: A fault is a break in the earth's crust along which movement can take place causing an earthquake. When an earthquake occurs on one of these faults, the rock on one side of the fault slips with respect to the other. Faults can be centimeters to thousands of kilometers (fractions of an inch to thousands of miles) long. The fault surface can be vertical, horizontal, or at some angle to the surface of the earth. Faults can extend deep into the earth and may or may not extend up to the earth's surface. Faults with evidence of Holocene (about 10,000 years ago to present) movement are the main concern because they are most likely to generate future earthquakes. If the earthquake is large enough, surface fault rupture can occur. 8) Proximity to the epicenters: The epicenter is the point on the Earth's surface that is directly above the hypocenter or focus, the point where an earthquake or underground explosion originates. In the case of earthquakes, the epicenter is directly above the point where the fault begins to rupture, and in most cases, it is the area of greatest damage. However, in larger events, the length of the fault rupture is much longer, and damage can be spread across the rupture zone The weight maps were standardized by applying a linear function. Linearity was chosen to limit discussion with stakeholders for selecting other membership functions. The composite hazard score map generated herein formed the static framework of risk analysis in InsPro. Multi-criteria evaluation (MCE) technique was adopted for creation of vulnerability score map. MCE was applied with following factor maps:  Building height  Year built  Construction type  Building area (square footage)
  6. 6. Volume 2, Issue 3, March 2012 © 2012, IJARCSSE All Rights Reserved Page | 336 Each vulnerability score map generated herein also formed the static component ofInsPro. C. GIS database integration Workspace was created which is a simple text based scripting resource containing commands to open tables, create and position the necessary map, browser and other windows, define layer style and thematic settings. The layers included in workspace were: administrative, gazetteer, point of interests (POIs), streets layers. This workspace was used as base map and formed the front end visualization component of the InsPro on which analysed results were to be depicted. Microsoft Access® 2007 was used for data storage and queries and contained non-spatial data assembly including hazard and vulnerability score tables. Hazard score table contained pre generated composite hazard score at pincode level derived using MCDM techniques. MCDM was too applied to obtain vulnerability score tables with individual score tables of building height, year built, construction type, and building area (square footage) of case study region which. The advantages of storing it in access lies in the fact that these, tables are not static and can be updated, revised at any given point of time by the administrator of Ins Pro. Ahlers and Boll (2008) introduced five classes in terms of spatial granularity (country, region, city, street, and building) for geocoder development. For the current study street level geocoder engine was developed and integrated into InsPro using C# language. The geocoder was incorporated enabling geocoding functionality to fetch and show search results to the user on web interface. D. Development of Web-based solution - InsPro Cognizant of the need for a risk assessment tool for better underwriting and actuarial engineering, and to provide a system that can generate and manage risk information for acquisition of insurance/reinsurance facilities and catastrophe cover, GIS assisted SDSS wascalled as Insurance Profiler (InsPro) was developed. The codes were developed for integrating geocoding components and multi-criteria score mapsand layers to create sync between them. Besides for visualization of results of search, query, geocoder, statistical analysis web interface was created using MapXtreme framework. The SDSS were built with basic functionalities such as zoom, pan, search and locate, address validate, bulk geocode, on the fly risk score computations, report generation, print and save. Computation of risk score involved using multiplicative function of hazard potential and vulnerability i.e. Risk = Hazard potential x Vulnerabilitywhich is also the definition of risk. To be able to portray the risk of region, the risk scores/map is based on an aggregated hazard map and an integrated vulnerability map, and it enables us to see the level of risk related to a region. This concept was applied in InsPro where hazard score map was pre-computed and stored in database, vulnerability criteria classes were selected by user and dynamic risk score was computed as output using multiplicative function. However, such simplification doesn’t devalue flexibility and usefulness of the SDSS tools in disaster insurance underwriting. In support of the robust expert-system shell, more use can further populate the knowledge bases of hazard, vulnerability and risk assessment making them more complete, more sophisticated and easily adjustable by satisfying demands for decision-making. Besides these, in InsPro, the flexibility for calibrating data, parameters and even risk computation logic and limits, as per user’s requirement were provided. The better visuals and array of the applications has capability to draw more acute fascination of customer toward insurance underwritings/pricing. The application will tend to bring in uninsured segment of population into insured segment by giving a logical view of where and why asset should be insured. VI.RESULTS AND DISCUSSION A. Multi-criteria evaluation based Hazard Score Map for SDSS In the present study the Spatial-MCDM method was used in which different hazard criteria were appraised in order to establish their validity and usefulness, and eventually amalgamation of the different factors were provided in form of a composite hazard score map for study region. Following a multi-criteria decision making - analytical hierarchical process (AHP) (Saaty, 1980), each theme and features were assigned weights and rankings respectively according to their perceived relative significances to seismic hazard (Refer Tables III -IX). TABLE III: SCORES OF SEISMIC ZONES Seismic Zone Risk Zone Weight Seismic Zone-1 Very Low Damage Risk Zone 0.009793 Seismic Zone-2 Low Damage Risk Zone 0.009838 Seismic Zone-3 Moderate Damage Risk Zone 0.424964 Seismic Zone-4 High Damage Risk Zone 0.546386 Seismic Zone-5 Very High Damage Risk Zone 0.009019 TABLE IV: SCORES OF PEAK GROUND ACCELERATION Peak Ground acceleration (PGA, in g) Susceptibility Weight 0 – 0.12 Very Low 0.091047 0.12– 0.14 Low 0.128602 0.14 – 0.16 Moderate 0.189169 0.16 – 0.18 High 0.266218 0.18 – 0.20 Very High 0.324964 TABLE V: SCORES OF SOIL CHARACTERISTICS Soil characteristics Soil Susceptibility Weight Very Hard to Hard Rock Very Low 0.091047
  7. 7. Volume 2, Issue 3, March 2012 © 2012, IJARCSSE All Rights Reserved Page | 337 Loamy Sand Low 0.128602 Soft Rock to Older Alluvium Moderate 0.189169 Younger Alluvium High 0.266218 Fill to Shallow Bay Mud Very High 0.324964 TABLE VI: SCORES OF LIQUEFACTION CHARACTERISTICS Liquefaction characteristics Liquefaction Susceptibility Weight Rock very stiff or cohesive clays, sediments older than Pleistocene (>1.6 Ma); sites with deep water table Low 0.101103 Holocene to Pleistocene (11Ka to 1.6Ma) alluvial fan deposits Very Low 0.127001 Modern alluvial fan deposits Moderate 0.2673707 Modern floodplain or beach ridge deposits High 0.504526 TABLE VII: SCORES OF GEOLOGICAL CHARACTERISTICS Geological characteristics Susceptibility Weight Polycyclic sequence of brown silt- clay with kankar and brown to grey fine to medium grained sand 1 0.0421679 Yellowish fine to medium grained sand with minor silt and siliceous kankar 2 0.109563 Quartzite with interbanded schit and phyllite 3 0.113868 Multiple fill alternate sequence of grey micaeous fine to medium grained sand 4 0.212850 Grey micaeous fine to coarse grained sand and overbank silt 5 0.521552 TABLE VIII: SCORES OF LAND USE Land use Risk Zone Weight Group 1 High Density Vegetation, Waterbodies 0.068837 Group 2 Low Density Vegetation, Open, Quasi open area 0.112326 Group 3 Industrial area, Residential/village, Agriculture 0.225349 Group 4 Skyscrapers, Urban low density , Urban High Density, Airport 0.593488 TABLE IX: SCORES OF PROXIMITY TO FAULTS Proximity to Faults (Neotectonic, Subsurface) No. of faults Weight 0-20 km 1 0.006543 21-40 km 1 0.005479 41-60 km 4 0.168103 61-80 km 7 0.286638 81-100 km 12 0.545258 TABLE X: PAIR-WISE COMPARISON SCALE Proximity to Epicenter No. of epicenter Weight 0-25 km 0 0.021693 26-50 km 4 0.255663 51-75 km 6 0.4091855 76-100 km 2 0.191668 101-125 km 1 0.121790 The composite seismic hazard score map of Delhi region involved evaluation of different seismic hazard components namely seismic zones, peak ground acceleration at seismic bedrock, soil characteristics, liquefaction potential, land use, geological characteristics, proximity to faults and epicenter. These layers were, thereafter, integrated through MCDM techniques to obtain composite seismic score map addressing site specific hazard scores for seismic micro-zonation. A composite hazard score map was generated with indices value from 0.17 to 0.89 (Figure 3). Fig. 3 Seismic hazard score map of Delhi The hazard scores were set into five categories, negligible (0), low (0.01 - 0.25), moderate (0.26 – 0.50), high (0.51 – 0.75) and very high (0.76 – 1.00). The map depicted that seismic susceptibility of Delhi region follows the order: east > north > west > south areas. East regions of Delhi are considerably high vulnerable area because it is positioned in high seismic zone and greater liquefaction potential. Overall, parts of central Delhi are also subjected to greater seismic scores due to social-economic assets accumulation. Accumulation of people and their assets seemingly become major cause of the hazard risk. The generated composite hazard score map was integrated in InsPro.
  8. 8. Volume 2, Issue 3, March 2012 © 2012, IJARCSSE All Rights Reserved Page | 338 Vulnerability score maps were too integrated into the InsPro application so that in the fly final risk computation can be made using hazard and vulnerability layers. B. SDSS – Insurance Profiler Insurance underwritings, risk pricing and claims management are a number of objectives by which an insurer can reduce the volatility and liquidity in characteristics of risk to 'homogenize' it, and make it fall in to the basket of 'risk pools'. The booming geospatial technological have made possible to build geo-analytical custom insurance solutions that leapfrog capabilities of traditional offerings. InsPro – Insurance Profiler is an upshot of such offerings, coming out with hitherto hard-to-obtain location data with integrated risk scores. Delhi region is used as case study site to showcase few of the functionality of InsPro. The details of further offerings of InsPro are explained in Table 3. 1) Mapping: Insurers/reinsurers/Risk managers can locate address and visualize spatially (Refer Figure 4). The mapping solutions incorporated in InsPro enables to depict myriad of themes. All visualizations that user can see is rendered from workspace. The series of standard procedures were involved from conversion of data from OSL (Oracle) format to MapInfo *.TAB format and forms the background for geospatial results visualization based on functionality executed in InsPro. For example, locating address Central Cottage Industrial Corporation, Delhi. InsPro was able fetch this result by making use of geocoding engine. Reverse was also possible i.e. on entering Latitude/Longitude values, address could be returned. Fig. 4 Insurance Profiler (InsPro) - Web based mapping solution for Insurers/Re-insurers 2) Risk Assessment: InsPro generates comprehensive assessment of location under consideration by insurer/reinsurers to produce more objective patterns of risk assessment in lieu support of the expert knowledge base (Figure 5). Besides these, InsPro has inherent functionality of data analytics. For example, if a zone presents an unacceptable risk for insuring new property then such risk can be pre-screened by underwriters by varying the vulnerability parameters. If the new risk falls in the alarming range of score, it means there is already a concentration of risks, and they should be careful while writing risk based on the actuarial guidelines. Thus, its very well evident from the above case study that close association of geospatial technology with insurance decision making processes, InsPro application is perfectly suited for insurance domain to address its deficiencies. Fig. 5 Insurance Profiler (InsPro) - Web based mapping solution for Insurers/Re-insurers TABLE XI: INSPRO FUNCTIONALITY AND USAGE Functionality Usage Assessment of spatio-temporal hazard risk patterns The spatial decision support system can provide comprehensive analysis of hazard based risk score in addition to building parameters to produce more objective patterns of risk assessment in support of the expert knowledge base Evaluation of spatio-temporal variation of exposure Discrepant insured buildings have differential spatial variation of loss risk and thereby have their respective vulnerability and loss curve. Further, it is necessary to correctly estimate the regional total loss at risk from all kinds of properties so as to classify the insurance portfolios The past claims and their correlation by different policies Although the past claims data alone can’t provide enough accurate information concerning the occurrence patterns of natural hazards, they are an available indicator for the vulnerability and loss curve of exposures and contain important information for pricing and for determining insurance rates. Mapping Underwriters can examine specific regions on a digital map to see, how much of the current book of business is concentrated within a
  9. 9. Volume 2, Issue 3, March 2012 © 2012, IJARCSSE All Rights Reserved Page | 339 given radius or is proximal to historical claim records? This would give them a clear picture of the potential risk of the specific building/ pincode/ regions. Analytics Trend analysis with historical data can be performed to determine if a zone presents an unacceptable risk for insuring new industry in the area. Or by varying parameters of building contents, a new risk can be pre- screened by the underwriter. If the new risk falls in the alarming range of score, it means there is already a concentration of risks, and they should be careful while writing risk based on the current guidelines Risk Search A simple query interface for Risk Portfolio Manager to ease out the process of extracting information from the database using pre- stored policy and claims database. Running an event footprint on the policy database A geospatial footprint of any disaster viz. earthquake, flood or cyclone can be overlaid on insurance company’s current portfolio using and this would help in estimating extent of losses due to event Thematic Risk Reports Map a portfolio and then determine its exposure to various risks or intensity of risk. These maps can be exported from Risk Portfolio Manager in an image format. Accumulate the insured value, premium, PML, net retention, treaty limits and limit either by a geographical point of reference like building, pincode, or various admin boundaries, risk zones, proximity to a selected location. Even the accumulation by risk parameters of a peril can be carried out. For example, in case of the earthquake peril, risk accumulation can be monitored by building characteristics like occupancy, construction type, construction quality, number of inhabitants by day / night Tabular Accumulation Reports Tabular reports generated on the fly incorporating some of the following key aspects of the portfolio such as Premium Distribution, Claims Distribution, Loss Cost etc. Insurance Profiler (InsPro) developed herein in this paper, overcome some of the deficiencies of traditional actuarial assessment in India such as inadequate understanding of the geographical settings and its relationship to historical events [24], analysis based on anticipation and correlation of evidences ([25], [26]), small coverage and non-homogeneous information ([11], [27]), fixed scale analysis. In the current knowledge-based system shell, the geocoding engine, supporting multi-criteria evaluations and visual display facilitate insurers with the spatial visualizations, database management, data analysis, querying, trend analysis, estimating loss cost, avenue for new business expansion and underwriting risks. This system will even allow, risk managers to assess hazard concentration, determine degree of vulnerability and anticipate damage, in case of occurrence of catastrophe. VII. CONCLUSION Natural disaster and its vagaries contribute to complexity of the risk analysis. Insurance pricing of these involves manifold factors and interdisciplinary cooperation between disaster experts, meteorologists and actuaries ([29]–[31]). From the initial phase of hazard simulation, vulnerability and risk analysis to rate-making and premium-making, there is no clear-cut method or model that can give a comprehensive answer ([29], [32], [33]). The location based knowledge system designed specifically to deal with situation involving procedural and declarative knowledge is thus an appropriate choice of technology ([34],[35]). The SDSS - InsPro developed in this study incorporates the advanced expert- system shell, sophisticated visual GIS and robust spatial multi-criteria statistics components into a coherent and integral system using the industry standard interface protocol. Such a system is flexible, portable, extendable, low-cost and effective to provide a solid base for more accurate risk analysis and pricing of insurance policies. The application based on insurance guiding principles and scientific risk assessment considerations, has the potential to basically transform the lifecycle of most of the insurance business processes as known in present day. Because of its flexibility, scalability, user-friendliness GUI, despite some shortcomings (hazard assessment, unsystematic uncertainty analysis), InsPro can be easily enhanced and become more powerful with continuous update of knowledge bases, enhancement of data to support geocoding and incorporation of developed risk models. This suite of product developed as prototype for insurance sector, can also replicated for various domains. REFERENCES [1] V. D. H. Voet, and W. Slob, ―Integration of probabilistic exposure assessment and probabilistic hazard characterization‖, Risk Analysis, vol.27, pp. 351–371, 2007. [2] T. Q. Zeng, and Q. Zhou, ―Optimal spatial decision making using GIS: a prototype of a real estate geographic information system (REGIS)‖, International Journal of Geographical Information Science, vol. 15, pp. 307–321, 2001. [3] M. R. Zolfaghari, and K. W. Campbell, ―A new insurance loss model to promote catastrophe insurance market in India and Pakistan‖, Earthquake Engineering, vol. 2, pp. 1-8, 2008. [4] R. T. Kozlowski and S. B. Mathewson, ―Measuring and managing catastrophe risk‖, Journal of Actuarial Practice, vol. 3, pp. 211–232, 1995. [5] R. T. Musulin, ―Issues in the regulatory acceptance of computer modeling for property insurance ratemaking‖, Journal of Insurance regulation, vol.15, pp. 342–359, 1997. [6] L. Lianfa, J. Wang, and C. Wang, ―Typhoon insurance pricing with spatial decision support tools‖, International Journal of Geographical Information Science, vol.19 (3), pp. 363–384, 2005. [7] B. Rabkin, and D. Sonnen, ―Frameworks to Develop Spatial Perspectives of the Insurance Value Chain‖, IDC Technology Spotlight, IDC 1043, pp. 1-8, 2010. [8] (2011) The EM-DAT website. [Online]. Available: http:// / [9] K. Nagesh, ―GIS as Decision Making Tool for Insurer‖, Bimaquest, vol. 4(1), pp. 48-59, 2004. [10] R. I. 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  10. 10. Volume 2, Issue 3, March 2012 © 2012, IJARCSSE All Rights Reserved Page | 340 [12] A. Amendola, Y. Ermoliev, T. Y. Ermolieva, V. Gitis, G. Koff, and J. Linneroothbayer, ―A systems approach to modeling catastrophic risk and insurability‖, Natural Hazards, vol. 21, pp. 381–393, 2000. [13] Z. Zhang, and D. A. Griffith, ―Integrating GIS components and spatial statistics analysis‖, International Journal of Geographical Information Science, vol. 14, pp. 543–566, 2000. [14] Y. Ding, and P. Shi, ―Fuzzy risk assessment model of typhoon hazard‖, Journal of Natural Disasters, vol. 11, pp. 34–43, 2002. [15] R. Thomas, ―Insurance pricing wit GIS: It’s all about business‖, Geospatial Solutions, vol. 20, 30-45, 2000. [16] P. Grossi, H. Kunreuther, C. C. Patel, ―Catastrophe modeling: a new approach to managing risk‖. Huebner International Series on Risk. Insurance and Economic Security, vol. 25, pp. 252-270, 2005. [17] G. Carpenter, ―The Catastrophe Bond Market at Year-End 2007: The Market Goes Mainstream‖. GC Securities, vol. 1, 2008. [18] P. Peduzzi, ―The Disaster Risk Index: Overview of a quantitative approach‖. In: Birkmann, J. ed. Measuring Vulnerability to Natural hazards – Towards Disaster Resilient Societies, New York: United Nations University: pp. 502-524, 2006. [19] I. Vertinsky, S. Brown, H. Schreier, W. A. Thompson, and G. C. Vankooten, ―A hierarchical-GIS-based decision model for forest management: the systems approach‖, Interfaces, vol. 24, pp. 38–53, 1994. [20] H. Jiang, and J. R. Easterman, ―Application of fuzzy measures in multi-criteria evaluation in GIS‖, International Journal of Geographical Information Science, vol. 14(2), pp. 173–184, 2000. [21] Q. Wu, S. Ye., X. Wu, and P. Chen, ―Risk assessment of earth fractures by constructing an intrinsic vulnerability map, a specific vulnerability map, and a hazard map using Yuci City, Shanxi, China as an example‖, Environmental Geology, vol. 46, pp. 104–112, 2004. [22] A. Sakamoto, and H. Fukui, ―Development and application of a livable environment evaluation support system using Web GIS‖, Journal of Geographical Systems, vol. 6, pp. 175–195, 2004. [23] T. L. Saaty, ―A scaling method for priorities in hierarchical structures‖, Journal of Mathematical Psychology, vol. 15, pp. 234–281, 1977. [24] R. Klostermann, ―Planning Support Systems: A New Perspective on Computer-Aided Planning‖, In: R. Klostermann, ed. Planning Support Systems, Integrating GIS, Models, and Visualizations Tools, Redlands: ESRI Press, pp. 1-24, 2001. [25] Y. Leung, and K.S. Leung, ―An intelligent expert system shell for knowledge-based Geographical Information Systems: the tools‖ International Journal of Geographical Information Systems, vol. 7, pp. 201–214, 1993. [26] K. A. Knut, ―A Markov model for the pricing of catastrophe insurance future and spreads‖, Journal of Risk and Insurance, vol. 68, pp. 25–50, 2001. [27] J. R. Easterman, ―Uncertainty management in GIS: decision support tools for effective use of spatial data‖ In: C. Hunsaker, and M. Goodchild, eds. Spatial Uncertainty in Ecology: Implications for Remote Sensing and GIS Applications, New York: Springer- Verlag, 2001, vol. 14. [28] R. Leigh, and I. Kuhnel, ―Hailstorm loss modeling and risk assessment in the Sydney region, Australia‖, Natural Hazards, pp. 171–185, 2001. [29] J. F. Wang, and L. F. Li, ―Improving Tsunami Warning Systems with Remote Sensing and Geographical Information System Input‖, Society for Risk Analysis, 2008. doi: 10.1111/j.1539-6924.2008.01112.x. [30] S. T. Algermissen, and K. V. Steinbrugge, ―Seismic hazard and risk assessment. Some case Studies‖, The Geneva Papers on Risk and Insurance, vol. 9 (30), pp. 84-123, 1984. [31] S. Mansor, M. A. Shariah, L. Billa, I. Setiawan, and F. Jabar, ―Spatial technology for natural risk management‖, Disaster Prevention and Management, vol. 13(5), pp. 364-373, 2004. [32] M. T. Pareschi, L. Cavarrs, M. Favalli, F. Giannini, and A. Meriggi, ―GIS and volcanic risk management‖, Natural Hazards, vol. 24, pp. 187–196, 2000. [33] D. Sommer, ―The Impact of Firm Risk on Property-Liability Insurance Prices‖, Journal of Risk and Insurance, vol. 29, pp. 501-514, 1996. [34] D. C. M. Dickson, ―Insurance Risk and Ruins‖ International Series on Actuarial Science. Cambridge: University Press, 2005. [35] Y. Ermolieva, and V. V Norkin, ―Spatial Stochastic Model for Optimization Capability of Insurance Networks under Dependent Catastrophic Risks: Numerical Experiments‖. IIASA Interim Report, IR-97-028, 1997.
  11. 11.  2012 AARS, All rights reserved. * Corresponding author Landscape Characterization to Assess Impact and Magnitude of Roads on the Urban Spaces of Delhi, India Nishant1, 2* , Neena Priyanak1, 2 and P K Joshi1 1 TERI University, New Delhi 110070, India 2 Pitney Bowes Software, Noida 201301, India Abstract Quantification of landscape pattern and its transformation is crucial for assessment and monitoring of environmental consequences of urban infrastructure development. In the present study, geospatial tools and landscape metrics have been coalesced to quantify impacts of roads on spatial pattern of urbanization in Delhi using Quickbird (0.6m) dataset by varying grain size and across the transects 1 (Roads and urban class as individual entity) and 2 (roads and urban class treated as aggregate entity). Landscape metrics were computed along a 31 km long and 6 km wide transect (West to East direction) using standard and moving window analysis. The results of transect analysis showed that urbanization together with infrastructure development have resulted in increased patch density (PD), patch and landscape shape complexity (LSI), while a spectacular decrease in the largest and mean patch size (LPI) and landscape connectivity or increased fragmentation have been observed. The changes in landscape pattern along the transect have important ecological implications, and quantifying it at varied grain size, as illustrated in this paper, is an important first step to link patterns with processes in urban environs. Key words: Urbanization, landscape metrics, patch, remote sensing, roads 1. Introduction Urbanization and rapid infrastructure developments are considered key factors of land transformation profoundly influencing microclimatic conditions, green spaces and human life. Ecological consequences of urbanization and developmental plans are interesting and important to monitor and assess. Landscape analysis is one such attempt that can be used to quantify these. It further assists to understand concept of urban-rural gradient (McDonnell et al. 1997, Miller and Pillsbury 2008), which enhance variety of ecological issues in urban areas (Harshberger 1923). Still there exists a great gap in understanding these ecosystems (Collins et al. 2000, Wu 2000). Various methods such as gradient analysis (Godron and Forman 1983, McDonnell and Pickett, 1990) and landscape pattern metrics have been employed to understand the spatial pattern of urbanization with its ecological processes and thereby providing means to relate urban environment and people spatially and location of urbanization center with multiple indices (Alberti and Botsford 2000, Alberti 2001). Geographers and social scientist have carried out spatial pattern and urban dynamics of urban-rural areas with little or only superficial consideration of ecology in and around cities (Forrester 1969, Berry and Kasarda 1977, Batty and Longley 1994, Schneider and Woodcock 2008). By uncovering such characteristics of urban fragmentation along the gradient of land use zones, spatial distribution of urban fragmentation can be understood. Rapid developmental activities in transportation sector due to increasing urban demand have resulted in alteration of
  12. 12. Landscape Characterization to Assess Impact and Magnitude of Roads on the Urban Spaces of Delhi, India land use land cover (LULC) pattern. Construction of roads is one such activity that has brought important effects to landscape. Depending on the adjacency to nearby LULC the impacts of roads vary such as some are easily identifiable and some show effect with time. For example, impacts of forest roads such as dissecting the land, leading to habitat fragmentation, shrinkage, and attrition have been spatially viewed and quantified at several times (Reed et al. 1996), however, ecological impacts of roads in urban landscape have rarely been reported. The integrated urban ecosystems need new and integrative perspectives (Pickett et al. 1997, Grimm et al. 2000, Zipperer et al. 2000). Over the year, the urban development of Delhi have been on the fringes and in radial pattern with reliance on road infrastructure. The development envisaged by previous plans were polynodal with hierarchy of Commercial Centers located on either ring or radial roads. The MRTS network, underpass, overpass, metro networking have brought connectivity thereby having impact on the existing structure of city and consequently its development. This changed scenario has provided opportunities for city restructuring and alterations in LULC pattern. The present study was taken up with multifold objectives. We aim to assess changes of road patches to urban landscape pattern and relationship between the two. It has been accomplished while analyzing landscape in transect where road patches merge with urban areas. In this paper, the theoretical basis and general structure of landscape pattern metrics and effect of grain size have been used to address impact of road development on urban landscape. 2. Study Area Delhi lies between latitudinal parallels of 28°40' N and 28°67' N and longitudinal parallels of 77°14' E and 77°22' E and occupies northern region of India (216 meters above sea level). With an area of 1483 it corresponds to a typical patch of tropical region, completely engrossed with residential, commercial and urban centers. The region is undergoing rapid urban sprawl because of unprecedented developmental activities and population growth. A 31 km × 6 km study area located in central region traversing from West to East of Delhi is chosen in this case study (Figure. 1). 3. Material and Methods The Quickbird images acquired on 2008-03-15 (pan sharpened-0.6m) were georeferenced using a polynomial approach.FiveLULCclasseswereextractedusinginteractive approach of both visual and digital interpretation with the aid of ancillary data (e.g., pre-classified maps, topographic maps). Two transects were subset from imagery. Transect 1 comprises five LULC classes viz. open area, green spaces, urban area, roads and water body and Transect 2 contains four classes in which roads were merged with the urban area. The LULC raster was resampled to varying pixel size (1.2m, 3m, 5m, 15m, 30m and 60m) from the original data of 0.6m Figure 1. Location of study area grain size using nearest neighborhood technique. The derived resampled files were exported to GRID file format. Class properties file was prepared to set the run parameterization using Fragstats v.3.3. A series of landscape metrics at class and landscape level were calculated using 8-neighbors patch delineation rule. Standard and moving window analysis were performed each for Transect 1 and Transect 2. Landscape metrics at class and landscape level with variable pixel size were analyzed with regard to dynamic information of landscape and to determine the optimal grain size for impact analysis study of roads and characteristics of landscape dynamics. 4. Results and Discussion The major LULC classes are open area, green spaces, urban area, roads and water body. The open area refers to agricultural fields, scrub, riverbed and vacant lands in and around the city.The green spaces are ridge forest, biodiversity part of city and all urban green spaces along roadsides and settlements. Because of high resolution data linear green spaces could be mapped very conveniently. Urban area refers to all settlements in and around city. No attempt has been taken to classify the type of settlement and define any part of the settlement as rural, which is very difficult in Delhi. Roads are linear feature in and around settlements. Roads were also mapped in open areas and green spaces. Visual interpretation
  13. 13. Asian Journal of Geoinformatics, Vol.12,No.1 (2012) technique was used to delineate road network. River, canal network and small water bodies are classified as water. The overall accuracy of LULC interpretations exceeds 85% for all classes based on validation using the random points selected from original images. Class area (CA) is a measure of landscape composition i.e. how much of landscape is composed of particular patch type. CA of transect 1 (Figure. 2a) shows that open areas composition is highest and rest follows the sequence as urban> vegetation> roads> water body. However in Transect 2 (Figure. 2b), when urban and roads are merged, CA is still higher for open areas but value of urban and road class area has increased which is not additive in nature. Rest classes 0 20 40 60 80 100 Open Urban Vegeation Road Waterbody LULC ClassArea(sqkm) Figure 2a: LULC and class area for Transect 1 0 10 20 30 40 50 60 70 80 90 Open Urban Vegeation Waterbody LULC ClassArea(sqkm) Figure 2a: LULC and class area for Transect 2 Figure 2a. LULC and class area for Transect 1 Figure 2b. LULC and class area for Transect 2 does follows similar trend as in transect 1 such as urban + roads> vegetation> water body. Thus this shows that road and urban area when combined together exert a greater influence on landscape pattern and alters the landscape composition. PLAND reveals the most important information about landscape composition because quantitatively different LULC types generally would have different landscape pattern attributes. The PLAND of open area is considerably higher followed by urban structures. Vegetation class is slightly lower in occupancy. Road though being a linear feature does show greater occupancy thus showing its impact in landscape composition. Percentage occupancy of land shows similar trend as class area. PLAND of all class in Transect 1 decreased at varying grain size thus suggesting that grain size play a key role in determining composition of landscape classes (Figure. 3a). Up to 6m grain size, change in PLAND is quite significant but as the grain size increased from 6m to 15m, 30m and 60m, grain size does tend to show saturation and hence change in PLAND is not quite significant. This suggests that up to 6m resolution urban landscape composition at local scale can be evaluated for studying identified classes as at coarser resolution, PLAND value gradually saturates and hence is degree of differentiation reduces at coarser resolution datasets. The similar trend was observed in transect 2 (Figure. 3b). This is in concordance because width of roads in Delhi does not exceed more than 15m and streets are much 0.00 10.00 20.00 30.00 40.00 50.00 60.00 1.2 3 4 15 30 60 Pixel Size (m) PLAND Open Road Vegetation Urban Waterbody Figure 3a: PLAND for Transect 1 0.00 10.00 20.00 30.00 40.00 50.00 60.00 1.2 3 4 15 30 60 Pixel Size (m) PLAND Open Urban + Road Vegetation Waterbody Figure 3b: PLAND for Transect 2 Figure 3a. PLAND for Transect 1 0.00 10.00 20.00 30.00 40.00 50.00 60.00 1.2 3 4 15 30 60 Pixel Size (m) PLAND Open Road Vegetation Urban Waterbody Figure 3a: PLAND for Transect 1 0.00 10.00 20.00 30.00 40.00 50.00 60.00 1.2 3 4 15 30 60 Pixel Size (m) PLAND Open Urban + Road Vegetation Waterbody Figure 3b: PLAND for Transect 2 Figure 3b. PLAND for Transect 2
  14. 14. Landscape Characterization to Assess Impact and Magnitude of Roads on the Urban Spaces of Delhi, India narrower than this and thus coarser resolution datasets may fail to capture landscape pattern beyond 15m grain size. The results from class level metric moving window analysis along transect are shown in Figure 4a and 4b. The diagram shows spatial changes of landscape pattern. On each diagram the horizontal axis represents rural-urban-rural gradient from West to East and vertical axis represents the metric value. The major reason behind the following interpretation is how the changes in landscape pattern are related to process of urbanization. This also identifies the impact of grain size and visualizing impact of road network on the adjacent features and landscape patterns (patch density primarily). The V shape curve indicates that metric having an inverted V-shape distribution is positively correlated to the degree of urbanization and others (representing V-shape distribution) are negatively correlated to degree of urbanization. Patch Density (PD) is highest value in the urban core indicating a highly fragmented landscape and decreasing on both sides of the urban axis consisting of regions of sub- urban and rural areas. The central region being the city zone 0 20 40 60 80 100 120 140 Rural Sub-Urban Urban Sub-Urban Rural PatchDensity(no.ofpatches/100ha) Urban Open Roads Vegetation Waterbody Figure 4a: Patch density for Transect 1 0 20 40 60 80 100 120 140 Rural Sub-Urban Urban Sub-Urban Rural PatchDensity(no.ofpatches/100ha) Urban+Roads Open Vegetation Waterbody Figure 4a: Patch Density for Transect 2 0 20 40 60 80 100 120 140 Rural Sub-Urban Urban Sub-Urban Rural PatchDensity(no.ofpatches/100ha) Urban Open Roads Vegetation Waterbody Figure 4a: Patch density for Transect 1 0 20 40 60 80 100 120 140 Rural Sub-Urban Urban Sub-Urban Rural PatchDensity(no.ofpatches/100ha) Urban+Roads Open Vegetation Waterbody Figure 4a: Patch Density for Transect 2 Figure 4a. Patch density for Transect 1 Figure 4b. Patch Density for Transect 2 area, higher PD and hence higher fragmentation is obvious across all classes. Figure 4a and 4b show the trend of PD in both transects. The fragmentation in urban class is higher in transect 2 than that of 1 as road patches have been merged. This suggests that road developmental activities together with urbanization tend to have influence on landscape composition and structure and the developmental activities inappropriately planned would influence it to greater extent. Grain size also plays important role in determining the PD as it determines the maximum number of patches per unit area. An inverse relationship is observed between PD and grain size (Figure. 5a). The graph shows that PD tend to decrease across all classes as the grain size decreases from 1.2m to 3m, 6m, 15m, 30m respectively. However at coarser resolution PD of all classes are very low and differentiation of classes is not much significant. However in transect 2 the PD is comparatively higher for urban region thus exhibiting fragmentation characteristics even at coarser resolution (Figure. 5b). The graph also depicts sensitivity of roads to varying grain size and saturation being achieved beyond 15m. This is also attributed to small width of roads which could be picked up only because of unique spatial signature and only using spatial resolution less than 15 m. Mean patch size (MPS) is lowest at urban core region indicating a fragmented landscape that is composed of many small patches. For the Western and Eastern half of the transect MPS increases gradually with distance from city 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 1.2 3 6 15 30 60 Grain Size (m) PatchDensity(patches/100ha) Open Road Vegetation Urban Waterbody Figure 5a: Patch Density for Transect 1 in different grain size 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 1.2 3 6 15 30 60 Grain Size (m) PatchDensity(patches/100ha) Open Urban+Roads Vegetation Waterbody Figure 5b: Patch Density for Transect 2 in different grain size 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 1.2 3 6 15 30 60 Grain Size (m) PatchDensity(patches/100ha) Open Road Vegetation Urban Waterbody Figure 5a: Patch Density for Transect 1 in different grain size 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 90.00 1.2 3 6 15 30 60 Grain Size (m) PatchDensity(patches/100ha) Open Urban+Roads Vegetation Waterbody Figure 5b: Patch Density for Transect 2 in different grain size Figure 5a. Patch Density for Transect 1 in different grain size Figure 5b. Patch Density for Transect 2 in different grain size center, indicating an increase in land parcel size. However MPS of urban class is comparatively higher in Transect 2 than 1 which illustrates road patches when merged with urban class tends to exert greater influence than urban and road class individually. Landscape Patches Index (LPI) showed monotonically increasing trend with increasing pixel/grain size indicating dominance value of class increases with increasing resolution. Moreover, LPI saturated beyond a resolution value of 30m and does not have appreciable
  15. 15. Asian Journal of Geoinformatics, Vol.12,No.1 (2012) effect on landscape class. Thus LPI measures should be used carefully when comparing landscapes at varied grain size. Landscape Shape Index (LSI) showed a gradual declining trend. There are apparent effects to respond to variable grain sizes in class-level and landscape-level. Perimeter-Area Fractal Dimension (PAFRAC) approaches 1 for shape with very simple perimeters such as squares and approaches 2 as the patch complexity increases. It’s an indicative of shape complexity across a range of spatial scales. PFRAC of all classes is considerably higher at resolution of 1.2m and it tends to decrease with increasing grain size (Figure. 6a).Thus it suggests that at high-resolution shape complexity is much greater and this trend gradually diminishes as spatial scale varies from finer to coarser resolution. The increased complexity of merged urban and roads landscape in transect 2 suggests that it tends to have greater influence on urban landscape structure than roads and urban classes alone (Figure. 6b). Thus, road indeed tend to increase complexity of landscape which is identifiable at finer scale thus its developmental planning should be taken with greater concern and contemplation. ClumpinessIndex(CLUMPY)wascalculatedfordetermining the focal patch type disaggregation/aggregation and degree of disaggregation/ aggregation. CLUMPY equals -1 when maximally disaggregated, 0 distributed randomly, and approaches 1 as maximally aggregated. Among all classes in Transect 1 road patches showed maximum disaggregation and degree of disaggregation increased as resolution of grain size increased. This is quite evident with transect 2 study too. As the transect grain size increased road patches appear maximally disaggregated and the degree of disaggregation was highest among all class patch type. Water body showed highest aggregation at all grain sizes however the degree of 1.00 1.20 1.40 1.60 1.80 2.00 1.2 3 6 15 30 60 Grain Size (m) PAFRAC Open Road Vegetation Urban Waterbody Figure 6a: PAFRAC for Transect 1 in different grain size 1.00 1.20 1.40 1.60 1.80 2.00 1.2 3 6 15 30 60 Grain Size (m) PAFRAC Open Vegetation Urban+Roads Waterbody Figure 6b: PAFRAC for Transect 1 in different grain size Figure 6a. PAFRAC for Transect 1 in different grain size 1.00 1.20 1.40 1.60 1.80 2.00 1.2 3 6 15 30 60 Grain Size (m) PAFRAC Open Road Vegetation Urban Waterbody Figure 6a: PAFRAC for Transect 1 in different grain size 1.00 1.20 1.40 1.60 1.80 2.00 1.2 3 6 15 30 60 Grain Size (m) PAFRAC Open Vegetation Urban+Roads Waterbody Figure 6b: PAFRAC for Transect 1 in different grain size Figure 6b. PAFRAC for Transect 2 in different grain size Figure 7. Effect of grain size on road (red colored) patches association weakened at resolution greater than 30m. The aggregation index of class patch type followed the sequence as water body followed by vegetation, open, urban and road class patches (Figure.7 and 8). However scale of disaggregation follows the reverse sequence. In the Transect 2 CLUMPY of merged Urban and road class is higher than the vegetation patch showing greater
  16. 16. Landscape Characterization to Assess Impact and Magnitude of Roads on the Urban Spaces of Delhi, India Figure 8. Effect of grain size on water body (cyan colored) patches aggregation measure than Transect 1 (Figure. 9a and 9b). Road tends to increase aggregation measure of urban area and hence tend to transform landscape pattern and processes. Patch cohesion index (PCI) measures the physical connectedness of the corresponding patch type. In present analysis, PCI is highest for open areas and least for road thus typifying that open areas is maximum aggregated and road is least aggregated in its distribution and hence more physical connectivity among open areas than roads. The degree of 0 0.2 0.4 0.6 0.8 1 1.2 3 6 15 30 60 Grain Size (m) CLUMPY Open Road Vegetation Urban Waterbody Figure 9a: Clumpiness index for Transect 1 in different grain size 0 0.2 0.4 0.6 0.8 1 1.2 3 6 15 30 60 Grain Size (m) CLUMPY Open Vegetation Urban+Roads Waterbody Figure 9b: Clumpiness index for Transect 2 in different grain size 0 0.2 0.4 0.6 0.8 1 1.2 3 6 15 30 60 Grain Size (m) CLUMPY Open Road Vegetation Urban Waterbody Figure 9a: Clumpiness index for Transect 1 in different grain size 0 0.2 0.4 0.6 0.8 1 1.2 3 6 15 30 60 Grain Size (m) CLUMPY Open Vegetation Urban+Roads Waterbody Figure 9b: Clumpiness index for Transect 2 in different grain size Figure 9a. Clumpiness index for Transect 1 in different grain size Figure 9b. Clumpiness index for Transect 2 in different grain size PCI decreased with increasing grain size but changes were more evident in road class only. In Transect 2, degree of physical connectedness is still higher than Transect 1 (Figure. 10a and 10b). Merged urban and road classes show comparatively higher PCI than urban and road structures alone indicating more clumped or aggregation in its distribution, hence more physically connected. The above interpretations conclude that selection of appropriate grain size is the first parameter to be established. 20 30 40 50 60 70 80 90 100 1.2 3 6 15 30 60 Grain Size (m) PCI Open Road Vegetation Urban Waterbody Figure 10a: Patch Cohesion Index for Transect 1 in different grain size 20 40 60 80 100 1.2 3 6 15 30 60 Grain Size (m) PCI Open Vegetation Urban+Roads Waterbody Figure 10b: Patch Cohesion Index for Transect 2 in different grain size 20 30 40 50 60 70 80 90 100 1.2 3 6 15 30 60 Grain Size (m) PCI Open Road Vegetation Urban Waterbody Figure 10a: Patch Cohesion Index for Transect 1 in different grain size 20 40 60 80 100 1.2 3 6 15 30 60 Grain Size (m) PCI Open Vegetation Urban+Roads Waterbody Figure 10b: Patch Cohesion Index for Transect 2 in different grain size Figure 10a. Patch Cohesion Index for Transect 1 in different grain size Figure 10b. Patch Cohesion Index for Transect 2 in different grain size
  17. 17. Asian Journal of Geoinformatics, Vol.12,No.1 (2012) The appropriate range of grain for landscape indices of Delhi transect was 1.2m to 15m. The above results show that urbanization has resulted in dramatic structural changes of metropolitan landscape. For example, as urbanization progressed large and contiguous patches were broken up with an increasing number of patch types (LULC types) occurring in landscape. The density of patches of various types and thus PLAND composition increased exponentially. The overall LPTincreased steadily mainly due to increasingly even proportions of dominant LULC types whereas geometric shapes of patches in landscape as a whole became more and more irregular. As a result, urbanization has brought about increased structural fragmentation and complexity of landscape in Delhi region. In present analysis most critical points occur within 1.2-15m which is width range of some landscape elements, such as roads, branches of rivers. When grain size increases over this range, these elements shrink to small patches or are masked by other dominating elements, thus inflexions occur. Satellite imageries such as IKONOS, Quickbird, Worldview-1/2, SPOT PAN, XS, ASTER with 1.2-15m resolution are adequate for assessing impact of road on urban landscape research. Roads were thus sensitive to grain size of 15×15m2 because most of the roads in the study area were 10-20m wide. High percent coverage of roads indicated high patch density of landscape. A major ecological impact of roads in process of urban land transformation was leading to habitat fragmentation. 5. Conclusion The present research work adopted a combined method of landscape metrics analysis and sensitivity of metrics to varying grain size to analyze impact of road dynamics on landscapepatternDelhi,India.Forthis,degreeofurbanisation and infrastructure developmental (roads) were considered focal factors. The research design helped to answer research objectives such as how changes of road patches alters urban landscape pattern and what is the degree of changes at varying grain size. The major findings include (i) landscape compositional diversity and degree of fragmentation is positively correlated to degree of urbanization both along rural-urban –rural gradient, (ii) road patch type has unique spatial signature as compared with other LULC types, which differ with varying grain size, (iii) different patch type have differential and distinguishable landscape pattern attributes along transect and across various grain resolution, and (iv) changes in pattern of road structures shows positive correlation to degree of urbanization and developmental activities. This study is a step in direction of better understanding of impacts of road on landscape pattern and processes both of which would tend to have severe ecological consequences. The study also substantiated that urban landscape is more heterogeneous in composition and are mostly fragmented. Landscape metrics quantify pattern of landscape within designated landscape boundary and facilitates differential scenario based planning. Grain size is one important parameter in such analysis and provides insights to regional planning scales. Consequently, through the interpretation of these metrics and ecological significance of grain size an acute awareness of the landscape context and openness of landscape relative to phenomenon under consideration can be determined. The concept can be applied to identify indicators to mitigate negative effect of urbanization and sustainable LULC planning in urban landscapes. References Alberti, M. (2001). Quantifying the Urban Gradient: Linking Urban Planning and Ecology. In: Avian Ecology in an Urbanizing World. J. M. Marzluff, R. Bowman, R. McGowan and R. Donnelly. New York, Kluwer. Alberti, M. and E. Botsford (2000). Behavior of land use and land cover metrics along an urban-rural gradient. Working Paper, Urban Ecology Research Laboratory, Department of Urban Design and Planning, University of Washington. Seattle. Batty, M., and P. Longley (1994). Fractal cities: A geometry of form and function. San Diego: Academic Press. Berry, B. J. L., and J. D. Kasarda (1977). Contemporary urban ecology. New York: Macmillan. Collins, J. P., A. Kinzig, N. B. Grimm, W. F. Fagan, D. Hope, J. Wu, and E. T. Borer (2000). A new urban ecology. American Scientist 88: 416–425. Forrester, J. W. (1969). Urban dynamics. Cambridge: The M.I.T. Press. Godron, M., and R.T.T. Forman (1983). Landscape modification and changing ecological characteristics. In: H.A. Mooney and M. Godron (eds.). In: Disturbance and ecosystems: components of response. Springer-Verlag, N.Y. P. 12-18. Grimm, N.B., J.M. Grove, C.L. Redman, and S.T.A. Pickett (2000). Integrated approaches to long-term studies of urban ecological systems. BioScience 50:571–584. Harshberger, J.W. (1923). Hemerecology: The ecology of cultivated fields, parks, and gardens. Ecology 4:297–306. Luck, M., and J. Wu (2002). A gradient analysis of urban landscape pattern: a case study from the Phoenix metropolitan region, Arizona, USA. Landscape Ecology 17:327-339.
  18. 18. Landscape Characterization to Assess Impact and Magnitude of Roads on the Urban Spaces of Delhi, India McDonnell M. J., S. T. A. Pickett, P. Groffman, P. Bohlen, R.V. Pouyat, W. C. Zipperer, R. W. Parmelee, M. M. Carreiro, and Medley K. (1997). Ecosystem processes along an urban-to-rural gradient. Urban Ecosystems 1: 21–36. McDonnell, M. J. and S.T.A Pickett (1990). The study of ecosystem structure and function along urban-rural gradients: an unexploited opportunity for ecology. Ecology 71: 1231–1237. Pickett, S., W. R. Burch, S. E. Dalton, T. W. Foresman, J. M. Grove, and R. Rowntree (1997). A conceptual framework for the study of human ecosystems in urban areas. Urban Ecosystems 1: 185–199. Pillsbury, F.C. and J. R. Miller (2008). Habitat and landscape characteristics underlying anuran community structure along an urban-rural gradient. Ecol Appl. 18(5): 1107-18. Reed, R.A., J. Johnson-Barnard, and W.A Baker (1996). Contribution of Roads to Forest Fragmentation in the Rocky Mountains. Conservation Biology 10: 1098-1106. Schneider,A. and C.Woodcock (2008). Compact, Dispersed, Fragmented, Extensive? A Comparison of Urban Growth in Twenty-five Global Cities using Remotely Sensed Data, Pattern Metrics and Census Information. Urban Studies 45(3) 659–692. Wu, J. (2000). Landscape ecology: Concepts and theory. Chinese Journal of Ecology 19(1):42-52. Zipperer, W. C., J. Wu, R. V. Pouyat, and S. T. A. Pickett (2000). The application of ecological principles to urban and urbanizing landscapes. Ecological Applications 10: 685–688.
  19. 19. Exploring non-conventional options of rain water harvesting – responding to climate change impacts using geospatial tools Nishant * 1, 2 , N. Priyanka 1, 2 and P. K. Joshi 2 1 Pitney Bowes Business Insight (MapInfo), Logix Cyber Park, 5 th Floor, Tower - B, C-28&29, Sector-62, Noida- 201301 U.P. India,, 2 TERI University, Vasant Kunj, New Delhi, * Corresponding Author: Change Workshop Abstract Global climate change analysis has indicated variations in the temperature and precipitation regimes and as a result water resources are likely to come under increasing pressure. This coupled with anthropogenic activities is increasing the ecological footprint and thereby trampling the fragile hydrological systems. For supplementing the ever-increasing needs, techniques such as rainwater harvesting schemes are one of the adaptation options in the climate change scenario. These are employed to intercept additional water to minimize run-off loss (which is ~ 45% of average annual rainfall). However, the conventional rainwater harvesting methods are inadequate to address water demands as vertical expansion is far exceeding horizontal expansion in populated cities. In the wide expanding cities, road network is the most planned and developed infrastructures that could be explored for the water harvesting, if surveyed, planned and executed appropriately. In view of this, we present a conceptual framework for supplementing water supply through a prototype study in Delhi region. It has been the endeavor of this study to identify the options to harvest the rainwater using geospatial tools. It recognizes roadside in the sprawling cities providing an additional source of water to harvest. The collected water can be put to use for groundwater recharge or made potable or variously exploited employing bioremediation techniques. The prudent and integrated water resources development for sustainable water utilization is important even in absence of climate change impact. Such conceptual studies could gauge the extent of problems that the cities are likely to envisage. Introduction Climate change refers to variations in the mean state of climate or variability of its properties in its rate, range and magnitude that extends for a long period usually decades or longer (IPCC, 2007). Theses long- term changes are in unequivocal agreement between climate models which points towards increasing warming trends globally (IPCC, 2007). The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (IPCC, 2007; Baines et al., 2007), reports that extending from 1956 to 2005 the global surface warming increased at a rate of 0.13°C per decade which was nearly double that experienced in 100 years from 1906 to 2005 and is further likely to increase by 1.1-6.4°C towards the end of the 21st century without showing any sign of ceasing (Figure1). The relationship between hydrological system and climate change is even more complex, and the scientific consensus has broadened that climate impacts on water resources are already appearing worldwide.
  20. 20. Figure 1: Temperature projections to the year 2100, based on a range of emission scenarios and global climate models. Scenarios that assume the highest growth in greenhouse gas emissions provide the estimates in the top end of the temperature range. The orange line (“constant CO2”) projects global temperatures with greenhouse gas concentrations stabilized at year 2000 levels. Source: NASA Earth Observatory, based on IPCC Fourth Assessment Report (2007) In India, studies by various authors illustrates that there is escalating trend in surface temperature (Hingane et al., 1985; Pant et al., 1999, Goswami et al., 1992; Chylek et al., 2007) no significant drift in rainfall pattern (Pant et al., 1999) on all-India basis, but decreasing/increasing trends in rainfall pattern (Mall et al., 2006; Dhar and Majumdar, 2009) at regional levels. However, little work has been done on hydrological impacts of possible climate change for Indian regions/basins. Average annual rainfall over India is about 117 cm which is highly variable in spatio-temporal scales and is mostly concentrated in four months viz. June, July, August and September (southwest monsoon season) (IMD, 2009). Therefore, the variation in seasonal monsoon rainfall may be considered a measure that the climate change is exacerbating the spatial and temporal variations in water availability over the Indian domain in the context of global warming. The demand for water has already increased manifold over the years due to urbanization (Rodell et al., 2009), agriculture expansion (Umapathi and Ramashesha, 2001), increasing population (Census of India, 2006), rapid industrialization and economic development (Alam et al., 2007) and is projected that towards 2025, the freshwater demand globally will rise by 25% or more (Kundzewicz et al., 2007). The situation is getting worse in the cities where mounting demographics along with space crunch is regular phenomenon. In a study by Rodell, it is illustrated that more than 26 cubic miles of groundwater vanished from aquifers in the states of Haryana, Punjab, Rajasthan and the National Capital Territory of Delhi since 2002. The capital city of India, Delhi is an exemplary region where as per the projections of Census of India, the population is expected to be over 24 million by 2021 and touch 28 million by 2026 (Department of Urban Development, 2010) and demand for water will increase manifold. Thus, catering to such populations, an assessment of the availability of water resources in the context of requirements and expected impacts of climate change and its variability is critical for long-term development strategies and sustainable development of the city (Lean et al., 2008). In purview of the above, we present a conceptual framework for supplementing water supply through a prototype study in Delhi region to identify the options to harvest the rainwater along roadside using geospatial tools. It recognizes that, the similar experimental design, if successful, can be replicated for
  21. 21. other populated for sustainable development of surface water and groundwater resources within the constraints imposed by climate change. It is therefore essential that individuals, societies and institutions are made aware of the likely changes and have strategies in place to mitigate or adapt to a changing climate for sustainable water resource management. Study area New Delhi is located in the centre of Northern India within co-ordinates of latitudes 28°27’15” to 28°34’44” N and longitudes 77°10’9” to 77°18’41”E (Figure 2). It is positioned with the Great Indian Desert of Rajasthan to the west and south west, central hot plains to the south and Gangetic plains of Uttar Pradesh to the east while cooler hilly regions of Uttarakhand to the north. It covers an area of 1,483 sq kmwith a population estimate of 16 million (Census, 2006). Figure 2: Location of the Study Area - Delhi, India Experimental framework The overall experimental design for current study was divided into two components as illustrated below: Vulnerability Assessment Adaptation Assessment Analysis of current rainfall variability Projected future rainfall variability Anthropogenic Impacts Evaluation of Rain water harvesting method Proposition of road area as alternative for harvesting Proposition of cleansing harvested water
  22. 22. Vulnerability Assessment The IPCC Special Report on Emission Scenarios (SRES) suggests that climate changes is mainly driven by increased atmospheric concentration of Greenhouse Gases (GHG’s) and are intricate processes that are highly uncertain to manifest (IPCC, 2007). Thus, climate scenarios viz. A1, A2, B1 and B2, exploring alternative development pathways covering a wide range of demographic, economic, technological, and environmental and policies driving forces, have been developed (Refer Figure 3 and 4) as tool of assessing plausible alternatives of how the future emission may unfurl. Figure 3: Illustration of the four SRES scenario families Figure 4: Projected global average temperature increases for different SRES scenarios (IPCC, 2007) Source: IPCC Fourth Assessment Report (2007) Two regional scenarios (Refer Figure 3) were evaluated for current study viz. A2 sceanrio (describes a very heterogeneous world with high population growth, slow economic development and slow technological change) B2 scenario (which stipulates a world with intermediate population and economic growth, emphasizing on local solutions to economic, social, and environmental sustainability). The Hadley Centre Coupled Model version-3 (HADCM3) data at 30 arc-seconds resolution developed by Hadley Centre were processed and used, as it is one of the AOGCM’s participating in IPCC’s Fourth Assessment Report (AR4) (IPCC, 2007) and does not need flux adjustment (additional artificial heat and freshwater fluxes at the ocean surface) to produce good simulations (Gordon et al., 2000 and Pope et al., 2000). These data were tailored for Delhi region and statistical and graphical methods were applied to detect changes in the rainfall regime over Delhi. Also, projected rainfall trend for year 2020, 2050 and 2080 were analyzed by employing visual and statistical techniques. Rainfall pattern in current and future scenarios were analyzed only for four months viz. June, July, August and September as almost 80% (88 cm ± 10 SD) of the long term average annual rainfall comes down in these months (Refer Figure 5) through southwest monsoon (Mall et al., 2006; IMD, 2008). Figure 5: 25 year (1981 - 2006) average rainfall of Delhi Source: Adapted from National Data Centre, India Meteorological Department, Pune (2008)
  23. 23. Moreover, for analyzing anthropogenic impacts on water resources two proxy measures such as landuse change pattern and demographic trends were analyzed. For landuse change pattern analysis, Landsat satellite images available with GLCF were processed to find out the indicators of urban sprawl and development of infrastructures such as roads resulting in loss of open areas. The datasets of October (1977, 1989, 1999 & 2006) were taken for sprawl assessment which were available with the required preprocessing (radiometric and geometric corrections). The band information was used to compute Normalized Difference Vegetation Index [NDVI = (NIR - RED) / (NIR + RED)] for detecting greenness. The raw bands were put to digital classification with classification scheme viz. (i) Settlements (ii) Vegetation (iii) Open area and (iv) Water aimed to assess the changes in land use pattern. The local areas were visited with paper print of satellite data, topographic sheets and GPS to collect the ground truth and field verification. The demographic trends were statistically analyzed using Census data and its projections for future. Adaptation Assessment Literature review was carried to unveil available adaptation measures and coping strategies for water resources especially in context of rain water harvesting methods. Geospatial analysis such as spatial correlation, statistics and mathematical calculations were formulated to propose road as an alternate method of rain water harvesting and its feasibility for making additional water available for groundwater recharge or making it potable for drinking. Techniques to purge off the oil pollutants from harvested water collected from roadside were reviewed herein. Results and Discussions Vulnerability Assessment: Analysis of current and projected rainfall variability A number of studies have been carried out to assess the impact of climate-change scenarios on hydrology of various basins and regions in India (Gosain and Rao, 2003, Mall et al., 2006) and it is projected that increasing temperature and decline in rainfall has been observed which tends to reduce net recharge and affect groundwater levels (Gosain and Rao, 2003, Mall et al., 2006). However, little work has been done on hydrological impacts of possible climate change for Delhi regions/basins. In purview of the above, the rainfall regime shift analysis was performed using current and future climate change under two scenarios. It was observed that under A2 sceanrio the shift in the rainfall was compartively more than in B2 scenario which stipulates a world with relatively better economic, social, and environmental sustainability (Refer Table 1). Also, the shift in the mean value of rainfall was observed higher as one graduated from current to future climate years viz. 2020, 2050 and 2080 in descending fashion (Refer Figure 6 -Shift anlysed for the month of June). Figure 6: Climate change and induced variability in rainfall (Source: Prepared from Worldclim data, 2011)
  24. 24. Table 1: Precipitation trends under current and future climate years for Delhi region (Source: Prepared from Worldclim data, 2011) Climate Scenarios Month Current Climate 2020 A2a 2050 A2a 2080A2a June Current Climate 2020 B2a 2050 B2a 2080 B2a
  25. 25. Climate Scenarios Month Current Climate 2020 A2a 2050 A2a 2080A2a July Current Climate 2020 B2a 2050 B2a 2080 B2a
  26. 26. Climate Scenarios Month Current Climate 2020 A2a 2050 A2a 2080A2a August Current Climate 2020 B2a 2050 B2a 2080 B2a
  27. 27. Climate Scenarios Month Current Climate 2020 A2a 2050 A2a 2080A2a September Current Climate 2020 B2a 2050 B2a 2080 B2a It is therfore observed spatially and statitically that the rainfall regime contraction is bound to happen in the years to come. This is very much evident with the shift in the mean average value of rainfall from current to future years (Figure 6). Thus, its imperative to explore new avenues for capturing maximum amount of rainfall to sustain the climate change imapct on water resources in urban centres such as Delhi.
  28. 28. Anthropogenic Impacts Beside observed climate shifts, anthropogenic activities are exerting great pressure on water resources (Mckenzie and Ray, 2009) from rising human population and sprawling concrete structures, particularly growing concentrations in urban areas (Mookherjee and Hoerauf, 2004). Urban agglomerations magnetize various sectors such as manufacturing, construction, trade and service of all kinds (Lall and Mengistae, 2005) thereby opening avenues of employment and have become the pull factor for the ever-increasing migration (Iyer and Kulkarni, 2007), employment opportunities (Lopez et al., 2003) and population growth (Mookherjee and Larvey, 2000). Figure 6 shows the impact of expected population growth on water usage by 2025, based on the UN mid-range population projection and the current rate of per capita water use (UNEP and Earthscan, 1999; Min. of water resources, 2003, Alexander et al., 2006). This clearly indicates the ‘two-sided’ effect on water resources – the rise in population will increase the demand for water leading to faster withdrawal of water and this in turn would reduce the recharging time of the water-tables (Mall et al., 2006; IWMI, 2008). As a result, availability of water is bound to reach critical levels sooner or later. Secondly, the sprawling concrete structures will disrupt the permeability of water in the ground by forming impermeable surface thus making scenario even worse. Figure 7: Observed and projected decline in per capita average annual freshwater availability and growth of population from 1951 to 2050 (Source: Mall et al., 2006) Landuse Pattern - Land use pattern change analysis of Delhi very well highlights the urban sprawl and increase in the concrete forest in a very random pattern. The classified output reflects a rapid increase in the Settlements and observed decrease in green cover and open area over the period, as most of the areas have been converted to Settlement (Refer Table2). The new Settlements have been seen in the fringes and outskirts of the city. Green cover though increased in selected areas, but the net assessment shows a decreasing trend. While assessing the direction of urban sprawl, it’s generally from core to peripheral and extending outwards leading to dramatic changes in the eastern, south western and western fringes of the Delhi resulting in prime sub-cities of the National Capital Reserve (NCR) namely (a) Gurgaon township (as one of the IT and communication hub); (b) Dwarka sub-city (primarily designed for the human settlements and amenities). Some of the changes have also been observed in the western part named as Noida Township (a complicated mosaic of small scale industries, infrastructure and human settlements).
  29. 29. Table 2: Distribution of land use classes depicting change over the year (Area in sq. km.) Year/ Class 1977 1989 1999 2006 NetAreaChange Year / Class 1977 to 1989 1989 To 1999 1999 to 2006 Urban 419.67 444.83 478.55 545.37 Urban 25.16 (+ve) 33.72(+ve) 66.82 (+ve) Vegetation 223.65 204.91 187.97 155.65 Vegetation -18.74(-ve) -16.94(-ve) -32.32(-ve) Open Area 752.19 738.58 720.23 688.19 Open Area -13.61(-ve) -18.35 (-ve) -32.04(-ve) Waterbody 78.45 85.64 87.21 84.75 Waterbody 7.19 (+ve) 1.57 (+ve) -2.46 (-ve) Year/ Class 1977 1989 1999 2006 Percentchange Year/ Class 1977 to 1989 1989 To 1999 1999 to 2006 Urban 419.67 444.83 478.55 545.37 Urban 1.71 2.29 4.53 Vegetation 223.65 204.91 187.97 155.65 Vegetation -1.27 -1.15 -2.19 Open Area 752.19 738.58 720.23 688.19 Open Area -0.92 -1.24 -2.17 Waterbody 78.45 85.64 87.21 84.75 Waterbody 0.49 0.11 -0.17 Besides rapid gain in urban agglomerations, the associated development of road infrastructures is huge and expanding at an alarming rate. This was analyzed using time series road vector data extracted from Quickbird imagery for 2006 and 2010 vintage. Figure 7 shows the observed increase in road from 2006 to 2010. This clearly indicates that as the urban agglomerations will increase leading to more avenues of employment and migration, the demand for connectivity will increase proportionately. Figure 8: Pictorial representation of road infrastructure sprawl in Delhi [Color scheme: Red – Delhi roads in year 2006; Grey – Delhi roads in year 2010] Demographic trends - Delhi has witnessed a phenomenal population growth during past few decades. A population of 405,809 in 1901 has grown to 13,782,976 in 2001. Between 1999 and 2001, population in the region grew by about 54% (Census of India, 2006) while the amount of developed land increased by about 146%, or nearly three times the rate of population growth. There is tremendous increase in the urban population as compared to rural population of the state. This conforms to the land use pattern