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In a cadastre, the 2D parcel is nowadays correctly considered to be a special case of the 3D parcel because the rights and restrictions extend beyond the surface itself. Storing, representing and manipulating a true 3D parcel however has not yet been satisfactorily achieved because of constraints in data modeling and software development. Significant research has been done to identify the best ways to represent a 3D solid, with rigorous mathematical testing on the respective merits of alternative approaches. Software companies have come up with their own ways of storing and validating 3D data, mostly as extensions of the 2D concepts. However, validation rules of one software may not be acceptable within another software"s validation environment. The validation itself can be specified in great detail but sometimes this leads to redundant, repetitive or unnecessary processing. Because of the high volume of data a typical organization may be expected to handle, it is necessary for the rules to be streamlined and efficient. In this paper, validation is initially approached to answer questions such as: what is validation? why it is necessary to validate?, and how do we validate?. Limiting the scope to the 3D geometry or spatial representation of a 3D cadastre, the paper takes a novel approach in identifying the various aspects of validation of a 3D cadastral parcel and identifies the critical validation factors. It examines the validity within individual parcels and the relationship between adjoining or overlapping parcels in 2D or 3D. Although it is difficult to ensure completeness of rules, critical validation rules are examined for each identified factor.2

ABSTRACT:Floods are one of the most destructive natural disasters that threaten communities and properties. In recent decades, flooding has claimed more lives, destroyed more houses and ruined more agricultural land than any other natural hazard. The accurate prediction of the areas of inundation from flooding is critical to saving lives and property, but relies heavily on accurate digital elevation and hydrologic models. The 2011 Brisbane floods provided a unique opportunity to capture high resolution digital aerial imagery as the floods neared their peak, allowing the capture of areas of inundation over the various city suburbs. This high quality imagery, together with accurate LiDAR data over the area and publically available volunteered geographic imagery through repositories such as Flickr, enabled the reconstruction of flood extents and the assessment of both area and depth of inundation for the assessment of damage. In this study, approximately 20 images of flood damaged properties were utilised to identify the peak of the flood. Accurate position and height values were determined through the use of RTK GPS and conventional survey methods. This information was then utilised in conjunction with river gauge information to generate a digital flood surface. The LiDAR generated DEM was then intersected with the flood surface to reconstruct the area of inundation. The model determined areas of inundation were then compared to the mapped flood extent from the high resolution digital imagery to assess the accuracy of the process. The paper concludes that accurate flood extent prediction or mapping is possible through this method, although its accuracy is dependent on the number and location of sampled points. The utilisation of LiDAR generated DEMs and DSMs can also provide an excellent mechanism to estimate depths of inundation and hence flood damage

Transit-oriented development (TOD) links residential, retail, commercial, and community service developments to frequent, accessible rail transit services to stimulate sustainable development in the form of decreased land use and transport integration. A mixed-use shopping mall can be developed as a TOD with moderate to high density with diverse land use patterns and well-connected street networks centred around and integrated with a rail transit station. Shopping mall developments are now considered as the retail, social, and community centres of their communities. Therefore, understanding their services' mixed impact on nearby transit stations will provide further insight into the success of the TOD approach. As a result, this study aims to review and link the recent literature on attractiveness factors of shopping malls and the design factors of TOD and report the researchers' analytic observations (themes) clarifying transit-oriented shopping mall developments' (TOSMDs) attractiveness factors. The review systematically synthesises 208 guiding articles. It uses the elements of the extended service marketing mix (product, price, place, promotion, people, physical evidence, and process) and the five factors related to TODs (density, diversity, urban design, destination accessibility, and distance) as an indicator system for the factors determining the attractiveness of TOSMD. The review outcome is utilised to establish a conceptual framework for the attractiveness of rail TOSMDs. The study revealed fragmented causes of attractiveness factors of rail TOSMDs. It contributes to further understanding of TOD as it crossreviews retail and urban design literature findings. The resultant conceptual framework will also inform and potentially enhance the existing rail transit station passenger forecasting models and increase the economic sustainability of rail transit networks.

Purpose The purpose of this paper was to develop an integrated framework for assessing the flood risk and climate adaptation capacity of an urban area and its critical infrastructures to help address flood risk management issues and identify climate adaptation strategies. Design/methodology/approach Using the January 2011 flood in the core suburbs of Brisbane City, Queensland, Australia, various spatial analytical tools (i.e. digital elevation modeling and urban morphological characterization with 3D analysis, spatial analysis with fuzzy logic, proximity analysis, line statistics, quadrat analysis, collect events analysis, spatial autocorrelation techniques with global Moran’s I and local Moran’s I, inverse distance weight method, and hot spot analysis) were implemented to transform and standardize hazard, vulnerability, and exposure indicating variables. The issue on the sufficiency of indicating variables was addressed using the topological cluster analysis of a two-dimension self-organizing neural network (SONN) structured with 100 neurons and trained by 200 epochs. Furthermore, the suitability of flood risk modeling was addressed by aggregating the indicating variables with weighted overlay and modified fuzzy gamma overlay operations using the Bayesian joint conditional probability weights. Variable weights were assigned to address the limitations of normative (equal weights) and deductive (expert judgment) approaches. Applying geographic information system (GIS) and appropriate equations, the flood risk and climate adaptation capacity indices of the study area were calculated and corresponding maps were generated. Findings The analyses showed that on the average, 36 (approximately 813 ha) and 14 per cent (approximately 316 ha) of the study area were exposed to very high flood risk and low adaptation capacity, respectively. In total, 93 per cent of the study area revealed negative adaptation capacity metrics (i.e. minimum of −23 to <0), which implies that the socio-economic resources in the area are not enough to increase climate resilience of the urban community (i.e. Brisbane City) and its critical infrastructures. Research limitations/implications While the framework in this study was obtained through a robust approach, the following are the research limitations and recommended for further examination: analyzing and incorporating the impacts of economic growth; population growth; technological advancement; climate and environmental disturbances; and climate change; and applying the framework in assessing the risks to natural environments such as in agricultural areas, forest protection and production areas, biodiversity conservation areas, natural heritage sites, watersheds or river basins, parks and recreation areas, coastal regions, etc. Practical implications This study provides a tool for high level analyses and identifies adaptation strategies to enable urban communities and critical infrastructure industries to better prepare and mitigate future flood events. The disaster risk reduction measures and climate adaptation strategies to increase urban community and critical infrastructure resilience were identified in this study. These include mitigation on areas of low flood risk or very high climate adaptation capacity; mitigation to preparedness on areas of moderate flood risk and high climate adaptation capacity; mitigation to response on areas of high flood risk and moderate climate adaptation capacity; and mitigation to recovery on areas of very high flood risk and low climate adaptation capacity. The implications of integrating disaster risk reduction and climate adaptation strategies were further examined. Originality/value The newly developed spatially explicit analytical technique, identified in this study as the Flood Risk-Adaptation Capacity Index-Adaptation Strategies (FRACIAS) Linkage/Integrated Model, allows the integration of flood risk and climate adaptation assessments which had been treated separately in the past. By applying the FRACIAS linkage/integrated model in the context of flood risk and climate adaptation capacity assessments, the authors established a framework for enhancing measures and adaptation strategies to increase urban community and critical infrastructure resilience to flood risk and climate-related events.