17thEsri India User Conference 2017

“Prospect for Geospatial Techniques in Exposure Data Development using Census Housing Statistics: A Case Study of District, -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

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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 Photograph Geo-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 . It has a population of 37,926 (Census 2004) and an area of 729 km². The capital of the

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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 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

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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, separately. The building occupancy data was arranged into four distinct classes namely residential, commercial, industrial, and others. The class named as others comprises of occupancy classes such as schools and colleges, hospitals and dispensaries, government offices, religious place etc. The building structure data, consists of wall materials, roof materials, were first processed into 17 distinct wall-roof material combinations and, next, were further grouped into few structural categories based on elements that are distinctly vulnerable to the same level of hazard (Figure 3). In this process, the initially classified 17 distinct wall-roof combinations were re-grouped into six distinct structural categories based on their vulnerabilities and structural character (Table 1).

Figure 3: Screenshots of the building footprints captured in the study area

Table 1: Building categories by construction materials and structural types

S. No. Building Vulnerability Structural Types (combination of major wall and roof materials) Classes Combination of concrete, GI, asbestos and tile to build a structure. 1 Permanent Grass/ thatch/ bamboo/ wood/ plastic/ polythene etc. used in combination for wall and roof materials. Also called 'pucca' houses. Page 4 of 8

17thEsri India User Conference 2017 S. No. Building Vulnerability Structural Types (combination of major wall and roof materials) Classes 2 Semi permanent 1 Combination of wood wall and timber/asbestos/GI roof G.I./metal/asbestos sheets as wall materials and timber/asbestos/GI 3 Semi permanent 2 sheets as roof materials 4 Semi permanent 3 Bamboo as wall materials and timber/asbestos/GI roofs Grass/ thatch/ bamboo/ wood/ palm trunk/ bebak etc. used in 5 Traditional combination for wall and roof materials 6 Earthen Rock wall with timber/thatch roof

Finally, using likelihood techniques, the above building structural classes (Table 1) were distributed over the building footprints followed by distribution of census socio-economic data to develop detailed building level exposure database for damage risk and social vulnerability assessment. The economic values for building types, i.e. the exposure data, was generated for each occupancy types in the study area. To calculate the exposure values, first, the unit cost of construction was determined from the data collected from various government agency websites, published literatures. After necessary validation with respect to market rates, estimated built-up floor areas for each house/ housing types were multiplied with respective per unit construction cost (replacement cost) to get the sum of exposure values expressed in monetary units for each asset category. Data gaps were filled with information collated from available sources such as open sources data, recently published literature or, by using proxy values, with necessary data validation.

Results and Discussions The following section represents the analysis summary of the exposure data developed following the above methodology. Analysis of Exposure Elements using census housing statistics: The analysis of building exposure elements shows that about 84% of the houses in the study area are being used for residential use, about 6% for commercial use, about 2% for industrial use, while the remaining 6% houses are being used for 'other uses' (schools, colleges, hospitals, places of worship etc.) (Figure 4).

Figure 4: Distribution of Buildings by main structural classes

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17thEsri India User Conference 2017 Table 2: Building counts by structural types at suco level

Suco Concrete/brick Wall & Wood Wall & GI Wall/ Bamboo Thatch/Palm Wall & Abstos /Con/GI/Tile GI/Timber Roof GI Roof Wall/ GI Roof Trunk/Clay/Bebak Roof Roof Aisirimou 419 23 73 218 58 124 1 387 109

Fahiria 80 162 113 119

Fatubosa 43 23 515 613

Lahae 41 23 11 174 96 148 34 83

Hoholau 10 245 171

SeloiMalere 775 41 14 157 122 345 9 926 338

Saboria 73 2 219 44

SucoLiurai 230 13 37 1529 278 Acumau 404 27 41 460 111 Fahisoi 10 443 165

Cotolau 10 168 5

Talitu 80 18 746 18

Madabeno 118 3 427 10

Tohumeta 6 212 14

Fatisi 13 272 56

TOTAL 2,929 150 371 7,245 2,410

It has been observed that, on the basis of structure vulnerability categories above (Table 2), in the study area, the maximum number of houses have Bamboo wall /GI roof combinations (about 55% of the total houses), followed by Brick wall/Concrete roof (pucca) houses that account for about 23% of the total houses. Thatch/palm trunk/clay/bebak houses constitute for about 19 % of the total houses, while the remaining 4% account for wood wall/GI roof ,GI wall/GI roof houses (Table 2).

At suco level, suco has the highest number of pucca (concrete roof) houses (775 nos.), whereas, Tohumeta suco has the lowest number of concrete houses (6 nos.). This suggests Seloi Malere suco accounts 70% of pucca houses followed by Lausi suco (55%) and Aisirimou suco (53%). Conversely, Fahisoi suco has only 2% pucca houses. Among bamboo wall/GI roof houses at suco level, Cotolou suco has about 92% of bamboo wall /GI roof houses followed by the Tohumeta suco (91%) and the Talitu suco (86%) .

For exposure values, it has been observed that the Seloi Malere suco has the highest residential exposure values of about US$ 13.37 million, followed by Aisirimou (US$ 8.3 million) and Seloi Craic (US$ 8.06 million). For commercial exposure values, Seloi Malere has the highest exposure value of US$ 5.28 million followed by Seloi Craic (US$ 1.09 million), while Suco Liurai shows the highest industrial exposure value of US$ 0.65 million (Figure 5).

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17thEsri India User Conference 2017

Figure 5: Distribution of (a) Occupancy Types; (b) Residential Exposure values; (c) Commercial Exposure values and (d) Industrial Exposure values at suco level

Conclusion: On the basis of the above analysis, it can be concluded that, Census housing information combined with spatial distribution of exposure (building footprints/ clusters) using geospatial techniques is an useful, fast, effective and most importantly quite accurate mean of exposure data development. The result obtained in this Page 7 of 8

17thEsri India User Conference 2017 study following the above methodology was compared and validated against the published DRM reports of the World Bank (2015) and, observed a close resemblance of the exposure values at common sucos estimated in both the studies. Hence, this methodology is being recommended for developing higher resolution easy to update contemporary exposure database that can assist the concerned national and international agencies in planning, development of urban infrastructure and, preparedness, reduction and mitigation of disaster risks.

References: Analytical Report on Labor Force, 2010, Timor Leste Timor Leste Population housing Abella, E.A.C. and Van Western, C.J. (2007).”Generation of a landslide risk index map for Cuba using geospatial Multi-criteria evaluation, “landslides, in the landslide online publication ADPC 2013, A Country Situation report on disaster risk assessment related initiatives, A Comprehensive National risk assessment and mapping-Timor- Leste Analytical Report On Labour Force, 2010, Timor-Leste Population And Housing Census, 2010 AusAID 2007, Community Based Development and infrastructure in Timor Leste: Past Experiences & Future opportunities Aleotti, P., Chowdhury, R., 1999.Landslide Hazard Assessment: summary review and new perspectives. Bulletin of engineering geology and the environment, 58:21-44 Barry E. Flanagan et. al. (2011) A Social Vulnerability Index for Disaster Management Journal of Homeland Security and Emergency Management Volume 8, Issue 1 2011 Article 3. Cutter et al., Social vulnerability to environmental hazards, Social science quarterly, vol. 84, No. 2 June 2003. General directorate of statistics: Statistics Timor-Leste Geological Report (Rehabilitation Works for the Dili-Ainaro Road - Stage 1), Ministry of Public Works Timor Leste and World Bank GoTL 2010, Government of Timor-Leste, Census, 20 October 2010. Preparedness Timor–Leste, Disaster Needs Analysis – 07 Sept 2012, Emergency Capacity Building Project, ACAP. PCRAFI, September, 2011, Pacific Catastrophe Risk Assessment and Financing Initiative (PCRAFI) Component 2: Exposure Data Collection and Management; Technical Report Submitted to the World Bank by AIR Worldwide; Public Works Department, Timor Leste (Final Report On Resettlement Plan Lots-1-2-3) UNDP Report (2013), Comprehensive National Risk Assessment and Mapping in Timor Leste, 222 pp World Bank 2013, Resettlement Plan, Road Rehabilitation Works for the Dili-Ainaro Road - Stage 1, Ministry of Public Works, Timor Leste

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