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Sample Format 17thEsri India User Conference 2017 “Prospect for Geospatial Techniques in Exposure Data Development using Census Housing Statistics: A Case Study of Aileu District, Timor-Leste” 1 Mrs. Ruchika Yadav, 2 Mr. Ujjwal Sur, 3Dr.Prafull Singh 1 Land Referencer, WSP Consultants India Pvt. Ltd. (WSP Global) 2 Senior Manager, Nippon Koei India Pvt. Ltd, Delhi 3 Assistant Professor, Amity Institute of Geo-informatics and Remote Sensing, Amity University, Noida Word Limit of the Paper should not be more than 3000 Words = 7/8 Abstract: Exposure is an integral element to perform About the Author: pre and post catastrophe risk assessment. A detailed knowledge of the structural and occupancy characteristics of buildings at risk assists disaster risk management authorities and concerned administration to rapidly determine the extent and severity of damages and, thus assist to facilitate fast relief and rescue. Unfortunately, in most of the Recent Mrs. Ruchika Yadav received her M.Tech in Geo- countries, only little information is readily available Photograph informatics and Remote Sensing from Amity about building assets, their structural types and conditions, monetary values and spatial distribution. University, Noida (2015) and M.A. in Geography from In order to conduit the gap, this paper introduces an Aligarh Muslim University (2013). Presently, she is approach to develop detailed building exposure data working as Land Referencer in WSP Consultants India by distributing census housing statistics. The Pvt. Ltd. (WSP Global).Her key specialty includes uniqueness of this approach is amalgamating the wall Geospatial modeling in DRM, NRM, Social studies. and roof materials combinations grouped into E mail ID: [email protected] analogous structural vulnerability classes through Contact No: +91 – 8860110293 geospatial technology that produces low cost exposure data at finer resolution. The present case study was carried out across 18 Sucos (villages) of Aileu district in Timor-Leste by disaggregating the coarser resolution housing cum socio-economic data (census) over finer resolution building footprints to build a comprehensive exposure database. We Recent Mr. Ujjwal Sur received his M.Tech. in Remote adopted a bottom up approach delineating the Photograph Sensing and GIS (Spl. Human Settlement Analysis) building footprints from higher resolution satellite images using suitable building morphometric from IIRS, Dehradun (Andhra University-2005) and technique in spatial analyst extension of Arc GIS 10x M.Sc. in Geography, Calcutta University (2002). software in conjunction with prerequisite building Presently, he is working as Senior Manager in Nippon Attributes such as roof type, number of floors. Next, Koei India Pvt. Ltd, Delhi and engaged in the Western the wall and roof combinations of census housing Dedicated Freight Corridor project as Chief GIS data was further categorized into distinct building Expert. He has vast experience in working with vulnerability classes based on extensive literature multilateral projects worldwide funded by World survey for the area of interest. Using likelihood Bank, UN, ADB, JICA. technique, the building structural classes were distributed over the building footprints and exposure E mail ID: [email protected] values were estimated. This was followed by Contact No: +91 – 9711225642 distribution of census socio-economic data over the Page 1 of 8 17thEsri India User Conference 2017 building level exposure database for casualty and social vulnerability analysis. Data gaps were filled with information collated from available sources such as open sources data, recently published literature or, using proxy values, with necessary data validation. The results obtained in this method is found to be very useful and can help in disaster preparedness, planning and mitigation by concerned national and Dr. PrafullRecent Singh, Assistant Professor, Amity Institute international agencies. of PhotographGeo-informatics and Remote Sensing, Amity University, Noida. He has more than 10 years Keywords: GIS, housing census, building footprints, experience on various aspect related to earth exposure, data disaggregation, disaster risk resource evaluation , mapping and monitoring .His assessment major research contributions are in the field of water resource management, Remote Sensing application, natural disaster and environmental management. E mail ID: [email protected] Contact No: +91 – 9958196406 Introduction: Developing exposure data is a critical component of any risk assessment study. Exposure data constitutes population, the built environment, systems that support infrastructure and livelihood functions, or other elements present in the hazard zones, which are subjected to potential losses. Therefore, modeling vulnerability of a system to natural hazards involves establishing a relationship between the potential damageability of critical exposure elements and different levels of local hazard intensity for the hazard of interest. A detailed knowledge of the structural and occupancy characteristics of buildings at risk assists disaster risk management authorities and concerned administration to rapidly determine the extent and severity of damages and, thus assist to facilitate fast relief and rescue. Unfortunately, in most of the countries, only little information is readily available about building assets, their structural types and conditions, monetary values and spatial distribution. To conduit the gap, this study focuses on identifying an useful technique for development of the exposure database by disaggregating the coarser resolution housing cum socio-economic data (census) over finer resolution building footprints captured from higher resolution satellite images for the 18 Sucos (villages) in the study area distributed over Aileu districts of Timor-Leste. Objective of the Study The key objective of this study is to develop detailed building exposure data by distributing census housing statistics. The wall and roof material (housing census) combinations in the Census tables are to be distributed over the building footprints/ clusters to generate a pragmatic easy to update contemporary exposure database. Study Area Aileu is an administrative district of East Timor. It has a population of 37,926 (Census 2004) and an area of 729 km². The capital of the Page 2 of 8 17thEsri India User Conference 2017 district is also named Aileu. The sub districts are Aileu, Laulara, Lequidoe and Remexio. It is in the northwestern part of East Timor and is one of only two landlocked districts, the other being Ermera. It borders Dili to the north, Manatuto to the east, Manufahi to the southeast, Ainaro to the south, Ermera to the west, and Liquiçá to the northwest. It was formerly part of the district of Dili but was split in the final years of Portuguese administration. As the weather and climate is concerned, average temperature of Alieu is 36 degree c and it has hot & humid climate (tropical). The study area covers 18 Sucos (village) located in the Aileu district of Timor-Leste for the detailed exposure data development (Figure 1). Methodology The methodology adopted for this study is based on a bottom up approach that involves generation of detailed building exposure data by distributing census housing statistics on the building footprints. The approach focuses on amalgamating the wall and roof material combinations grouped into analogous structural vulnerability classes through geospatial technology that produces cost effective exposure data at a finer resolution (Figure 2). The digitization, geocoding, statistical analysis and mapping was carried out using Arc GIS 10.2 software and MS Access database and advanced analysis was made using the spatial analyst extension of the Arc GIS software. Figure 2: Methodology for exposure data development First, the building footprints were captured from higher resolution latest satellite images downloaded from Google Earth and other open sources available for the study area. The essential attributes captured during this process include roof materials, number of stories and building use (residential, commercial, industrial etc.) on the basis of visual image interpretation techniques and automated tool for building height determination. To develop a test datasets for this purpose, the PCRAFI data (Pacific Catastrophe Risk Assessment and Financing Page 3 of 8 17thEsri India User Conference 2017 Initiative, 2011), UNDP report and data (2013), published reports, geocoded building photographs and Census data (2010) were used and, a relationship was established between combinations of building roof type and probable wall materials. This test dataset was validated for each of the Sucos of Aileu district and captured building footprint counts were matched against the Census building counts projected to the year 2016 based on yearly growth rates at suco level. From the vulnerability and risk to hazard perspective, structural classification of buildings is a critical component of exposure data development which determines the social vulnerability of an area. Now, vulnerability of houses to various hazards depends largely on the construction materials used, structural types, and heights, which are categorized into different structural–types based on their characteristics. In this study, the Census housing data collected from the General Directorate of Statistics, Timor Leste was processed to generate combination of types of houses primarily based on occupancy types and structural types,
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