The challenges of how to ensure sustainable urban development are currently one of the important agenda among governments around the world. The stakeholders require the latest and high volume of geographic information for the decision making process to efficiently respond to challenges, improve service delivery to citizens, and plan a successful future of the city. However, it is timeconsuming and costly to get the available information and some of the information is not up-to-date. Recently, GeoWeb 2.0 technological advances have increased the number of volunteers from non-professional citizen to contribute to the collection, sharing, and distribution of geographic information. The information known as Volunteered Geographic Information (VGI) has generated another approach of spatial data sources that can give up-to-date, huge volume of data, and available geographic information in a low cost for various applications. With this in mind, this paper presents a review of literature based on the potential use of Volunteered Geographic Information (VGI) in measuring sustainability of urban development. The review highlighted that social, economic, and environment as three pertinent pillars relating to the use of VGI for measurement sustainable urban development.
This paper addresses the need to develop a Local Geospatial Data Infrastructure (LGDI) for sustainable urban development. This research will highlight the effective and efficient framework for the development of local infrastructure. This paper presents a framework (a combination of domain based and goal based frameworks) for developing a Local Geospatial Data Infrastructure. The basis of this research is on a case study conducted in a Malaysian city. The main focus of the case study was on measuring and assessing sustainability. Six conceptual frameworks were produced based on 6 key dimensions of sustainability. The developed framework consists of 6 conceptual data models and 6 conceptual data structures. It was concluded that 30 spatial data layers are needed of which 12 data layers are categorized as point shape, 17 data layers are categorized as polygon shape and 1 data layer as line shape category.
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