Automated Spatial Data Generalisation in Support of Diasters Response

Strain for maintaining up-to-date maps at a range of scales for intelligence spatial decision mapping are ever growing. Many e-government agencies and private organizations projects require highly developed applications software tools in responding to disaster events using location-based services. This underpins reliance on accurate, economical and viable digital spatial information products. Albeit the fact that automated generalisation systems meet these requirements at a reduced cost, time for maintaining multiple data models and digital maps at different scales. Also, it should to be noted that automated spatial data generalisation will decreases the loading time for web mapping applications. This paper aims to reports on development of a knowledge-based solution “Generalization Expert System (GES)”. A brief description of key steps undertaken in building GES and its components will be described. GES is being developed in Java and Python for delivery of simplified spatial data. It can assist emergency planners/decision makers to produce maps which exposed to risk or affected by natural or technological disasters, and facilitating timely evacuation, rehabilitation, damage assessment and modelling. Also GES offers a convenient way to access and navigate the various GIS datasets and performs generalisation processes. Its capabilities will be demonstrated in a case study through simplifying GEODATA TOPO-250K Series 2 data into 1:500000 and 1:1000000 scales over Canberra Australia. The produced maps will assist emergency managers and field crow involved in recovery/reconstruction to understand the scope of the damage and identify locations that require support and rescue for the affected areas. To date, many advanced applications has been developed which requires high technical skills but GES has a simple and user friendly GUI that brings many benefits for users with less technical skills and knowledge of spatial data management.