Heegu Park
Business Development / Marketing Director
Gaia3D
South Korea


Bio
Heegu began his career in an IT company as a software engineer and developed some web programs for famous Korean fashion company. After short period of time as a software engineer, he moved to gaming industry, which was booming at that time in South Korea , where he experienced technical production and coordination of several online games at leading online game companies. 5 years working experience drove him to get higher degree for business and management, in KAIST(Korean Advanced Institute of Science and Technology) for MBAdegree and USC(University of Southern California) for MSBA(Master of Science in Business Administration). During his time at two graduate schools, KAIST and USC, he mainly focused on IT and the Creative industry. His studies have given him great opportunities to enhance his cooperation and management skills of various teams and people and his knowledge from two schools and working experience have driven him to pursuit successful IT business and efficient marketing strategies. Now, he works at Gaia3D, ageospatial company based in South Korea, and take charge of marketing and business development. His goal at Gaia3D is to make Gaia3D become a global GIS company.

Abstract
Efficient Ways of Generation, Drawing and Editing of Digital Weather Chart
Weather chart is very important data to view weather information as a snapshot at a certain time and heavily depends on manual works for the improvement of data credibility because data generated automatically by numerical model can't be perfect. Digital version of weather chart editor has been used for several years by KMA (Korea Meteorological Administration) for the work efficiency of generating weather charts. The previous digital weather chart editor used in KMA used to produce PostScript typed weather charts of fixed projection in limited area. This kind of weather charts could be only used for viewing weather charts. Neither displaying weather charts in other applications nor extracting weather information from weather charts for research or study was possible. Our team tried to overcome the drawbacks of the previous system and work on the improvement of the previous digital weather chart editor and made it possible not only to support multiple projections and interactive area but also to use KML (Keyhole Markup Language) as a saving format of weather charts for the scalability. The system uses GRIB2 (Gridded Binary 2) data type - one of the most popular weather grid data types - and weather observation data as initial data. Throughout automatic analysis of grid data, weather information such as contour, optima of high/low pressure or warm/cold and so on can be automatically generated and generated weather data can be displayed and modified (creation, modification, deletion) based on weather chart notations and regulations.

KyawSoe Linn
Principal Geospatial Consultant
Singapore Land Authority
Singapore


Bio
Principal Geospatial Consultant in the Singapore Land Authority.Project Manager for the development of PopuplationQuery service on OneMap.

Abstract
PopulationQuery@OneMap
PopulationQuery is a free service which allows user to view Singapore's demographic data in geospatial form. Before, the data was published in textural form and quite difficult to visualize the information. Under the service, user can query the demographic distribution of Age/Sex, Ethnic Group, Economic Status, Education attending, Income and Housing Status of Singapore Then, User can compare the information from area to areas. Or, user is able to compare the data between two census series and able to see the population changes easily.One of the advantages of developing this application on OneMap (www.onemap.sg) is user can overlay with the result of other services or data which are already available on the portal and that will help the user to get more meaningful visual estimation in decision making.All these data and functions are available as API for the people who want to overlay with their own data or application. So, they can call the API and use it in their own website.By using this service, City and town planner, housing agency, business community and local can use the information to help them to make better decision.

Mohammed Ariff Abdullah
Researcher / PhD Candidate
National University of Malaysia
Malaysia


Bio
Mohammed Ariff Abdullah, Ph.D. student in Pattern Recognition, Center of Artificial Intelligent Technology, Faculty of Information Science & Technology, National University of Malaysia. Qualification: Master of Information Technology (System Science & Management) (National University of Malaysia). B.Sc. Computer Science (University of Missouri-Kansas City, USA).

Abstract
Smart City Security: Predicting the Next Location of Crime Using Geographical Information System with Machine Learning Delivering experience, Smart City development is an orchestration of a system scaling from home to community, precinct, city and nation. Smart City method of Crime Busting has been introduced in the Malaysian context timely when the attention given towards crime has been taking center stage. Through National Key Result Area Safe City Program, crimes are mapped into a centralized Geographical Information System database hence enabling the visualization of crime hot spots. The concept from this study presents possibly a major capability upgrade to the existing Safe City Program by introducing the ability to predict the next location crimes will be committed. Crime profiling is done based on geographical attributes with machine learning to produce reliable prediction of the next crime location. It is hoped the ability to predict the next crime location can lead to another form of proactive crime busting.

Sung Hyejung
Assistant Research Fellow
Korea Research Institute for Human Settlement
South Korea


Bio
Sung is an Assistant Research Fellow in KRIHS. She holds a Master in Landscape Architecture from Seoul National University, Korea. Her major research fields include Environmental Spatial Analysis, Spatial Decision Support System and Smart City.

Abstract
Korea Planning Support System(KOPSS)
KOPSS (KOrea Planning Support System) is a decision support system for territorial planning, regional planning, urban planning, public facility planning and landscape planning based on geospatial open source technologies and sophisticated analytical methodologies. KOPSS is a computer system to support spatial planning and policy making in a scientific way. Spatial data such as land, building, cadastre, topography have been created since the launching of national geographic information system development project in 1995 in Korea. KOPSS currently has five models for regional planning, land use planning, urban regeneration planning, public facility planning and landscape planning. Each model has adopted its own analytical methodologies. Regional planning support model (REPSUM) diagnoses and monitors balanced development over time across the country by computing spatial patterns of various indicators. REPSUM also locates clusters or hot spots for those indicators. In addition, REPSUM analyzes location quotient based on extended distance and visualizes traffic volumes among regions. Land use planning support model is a tool for reviewing locational conditions, analyzing development potential and supporting population projection, demand calculation for land use types, land suitability analysis and allocation. Urban regeneration planning support model delineates areas to regenerate based on outworn buildings, household density and the rate of very small parcels etc. Public facility planning support model evaluates supply sufficiency of public facilities compared to demand using parcel-based population data, and simulates change between demand and supply for a new facility based on Huff model, and recommends optimize d locations to minimize total travel distance. Finally, landscape planning support model analyzes visibility, skyline, view-shaft, sunshine light, blockage ratio etc. for a new land/building development in three dimensional space.

Dinesh Sathyamoorthy
Research Officer
Science and Technology Research Institute for Defence
Malaysia


Bio
Dinesh Sathyamoorthy obtained the B.Eng and M.Eng.Sc degrees in computer engineering from Multimedia University, Malaysia, in 2003 and 2006 respectively. He is currently working as a research officer in the Science and Technology Research Institute of Defence (STRIDE), Ministry of Defence, Malaysia, while pursuing the PhD degree in electrical and electronics engineering in UniversitiTeknologiPetronas, Malaysia. His research interests include digital terrain modeling and digital image processing.

Abstract
Analysis of Surface Textures of Physiographic Features Extracted from Digital Elevation Models via Grey Level Co-Occurrence Matrix: A Multiscale Approach
While a number of studies have been conducted on the classification of various landforms extracted from multiscale digital elevation models (DEMs), not much attention has been provided on the effect of multiscaling on surface textures. To this end, this paper is aimed at employing grey level co-occurrence matrix (GLCM) to analyse the surface textures of physiographic features extracted from multiscale DEMs. Four GLCM parameters, energy, contrast, correlation and entropy, are computed for horizontal (0°), vertical (90°) and diagonal (45 and 135°) pixel pair orientations. For the respective DEMs and physiographic features, varying patterns are observed in the plots of the GLCM parameters due to varying surface profiles and the changes that occur over the scales. Due to the smoothing of the terrain during multiscaling, the features have increasing values of energy and entropy, and decreasing values of contrast and entropy, indicating decreasing roughness. Mountains have the highest roughness as compared to the other features over the scales, while basins have the lowest roughness. For correlation, the DEMs and mountains exhibit similar patterns at the initial scales, indicating that mountains are the more dominant of the three predominant physiographic features in deciphering terrain character. For each parameter, similar trends are observed in the plots for the four different pixel pair orientations, indicating similar trends of change of surface texture in the different orientations over the scales. However, varying values are observed for the different orientations, depending on textural uniformity in the corresponding orientations. The results obtained demonstrate that GLCM can be an appropriate tool for classifying landforms from multiscale DEMs based on the different texture characteristics of the landforms.

Chris Tagg
Product Manager
1Spatial
United Kingdom


Bio
Chris leads the 1Spatial company products strategy and advises on spatial data management workflows. Prior to joining 1Spatial, he worked in the financial sector in the City of London. During this time he assisted in designing, implementing and supporting business critical front-office trading systems and workflows, specialising in Straight Through Processing. Chris joined 1Spatial in 2005 bringing with him his wealth of experience of rules-based systems, business process design and automation.

Abstract
National Spatial Data: Unlocking the Potential through Innovation, Automation and Efficiency
Geographic data underpins the growth of national economies and the efficient delivery of government services. In the UK, Ordnance Survey mapping data is so vital that an independent report put its value to the British economy at more than £100 billion. National spatial data holdings are critical to decision making. Therefore these data holdings have to be accurate, up-to-date and managed to a guaranteed standard. Organisations such as National Mapping and Cadastral Agencies, utilities companies, defence and government departments that create and maintain these spatial big data holdings face a number of important challenges. They need to deliver from a ‘product-ready’ database and therefore have to find ways to be more efficient and consistent with their data capture, maintenance and publication processes. In order to manage these ‘product-ready’ databases, the right systems need to be in place to support each phase of the data supply chain. The sheer size of these databases and the very high rates of change demanded on them mean that automation must play a critical role. Using a National Mapping and Cadastral Agency case study and live demonstration, this session will show how, by implementing automation, large national spatial data holdings can now be easily managed and maintained. We will demonstrate how organisations are able to be agile and accommodate high rates of real-world change so that they can deliver existing and innovative new products to meet the evolving needs and requirements of their data consumers.