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Nowadays, 2D GISs are common and their related theories, concepts and models like for geometrical modeling and spatial relationships of objects are also well addressed and investigated. Most of the tasks related to 2D GIS applications are quite straightforward and relatively easy to handle. 2D GIS spatial analysis such as proximity analysis or proximity computation, network analysis, overlay function, neighborhood function, metric measurement and other analytical operations are also well understood and well researched by the GIS community. However, problem started to surface once we move towards 3D domain, i.e. to add an additional dimension to the current 2D GIS situations such as in spatial data modeling, analysis and application. Manipulating and handling spatial objects become more complicated as we move toward 3D. This paper attempts to address one of the problems in 3D analytical operation, i.e. 3D proximity analysis.

As we know that vertical component of spatial data directly interacts with the X and Y from the planimetric plane and makes the description of an object even harder to define. Data structure that supports object generation, preserves and maintains relationship with the neighboring objects is important in the 3D geometrical modeling. Several researchers in this problem domain have stated that 3D conceptual model, topological relationships, data collection, and spatial analysis might comprise a wide spectrum of questions and needs a lot of efforts to realize the solution. Although advancement in computer graphics have benefited to community in terms of 3D visualization and display but some other critical aspects like 3D spatial modeling together with the semantics information and spatial operators are hardly addressed, defined, and implemented commercially.

The commercial GIS packages that able to handle 3D datasets are rather limited to surface analysis and visualizing them in 3D. GIS accepts the fact that 2.5D GIS involves a single height attached to the planimetric positions () whereas real or “true” 3D GIS should able to handle data like planimetric data with multiple heights, e.g. solid objects. Some advanced 3D tasks such as 3D overlay functions, and network functions are not available in some commercial GIS software, for examples, ERDAS’s Imagine VirtualGIS, Intergraph Inc’s GeoMedia, PCIGeomatics’s Geomatica where they provide excellent tools for 3D visualization and 3D texture models. The systems also provide some operations like surface generation, volume computation, image draping, and terrain inter-visibility can be carried. However, “true” 3D operations are hardly available. Inevitably, many issues need to be investigated.

To move on to the 3D GIS, the third dimension must not be constrained by the single XY plane only. Considering the 3D analysis is the core component of the 3D GIS, therefore, an investigation that involves data input and 3D analytical operation will be addressed in this paper. Other aspect such as databasing is out of the scope. The developed analytical operations have been tested using real datasets that cover Universiti Teknologi Malaysia (UTM) main campus.

In general, managing disaster scene is quite demanding and needs rapid spatial information on the spot where some of the required information is in the form of 3D display of a spatial query and analysis. This paper discusses the development of proximity analysis that is the 3D buffering. The corresponding algorithms that work for most of the spatial primitives, i.e. point, line, and polygon in 3D will be discussed. We tested our buffering approach by using photogrammetrically captured datasets. Finally, the paper provides outlook to the proposed work towards the development of advanced 3D analytical solutions in 3D GIS domain.

In Hungary flood disasters are the major natural hazard as a consequence of lowland topographic conditions. Due to both structural and nonstructural mitigation measures from the mid 19 century, like building dikes and developing adequate warning system, no loss of life was reported since the 1950’s. Nevertheless the value of damage to human property in flood-prone areas may reach millions of euros each year throughout the country. Flood forecast is performed by the national hydrological institute, and warning is transmitted to the local hydrological authorities about the forthcoming disaster. Disaster response measures are based on conventional hydrological information and action plans are developed with traditional methods. The latest technology of GIS and remote sensing is not yet tested in the country to support the spatial information need of disaster response.

The aim of our investigation was to test impact modeling using mostly freely available spatial data. This may ensure feasibility in a case of a future flood catastrophe in various geographic location. The decision of using non-commercial data was further based on the experience that data acquisition from dissimilar sources like local authorizes and the military mapping agency may last weeks or months and was found not suitable for rapid results.

Investigation was based on the recent flood disaster event in the Northern part of the country in the summer of 2004.

To analyze the elements possibly being damaged by the flood, the extent of the inundation was calculated. Flood simulation was executed using digital elevation model of the remotely sensed SRTM radar data and hydrological data from the Hungarian Water Resources Research Centre (VITUKI). Modeled flood extent was serving the basis of spatial analysis of the disaster impact. Settlements hit by the flood disaster were queried. Moreover the condition of flooded road and railway network was analyzed to support route planning of mitigation efforts.

Results of this investigation should help to speed up decision making processes during future disaster events. Moreover should help to reduce property damages and suffering of flood victims.

Virtual 3D city models provide important information for different aspects of disaster management. In this context, up-to-dateness of and flexible access to 3D city models are of utmost importance. Spatial Data Infrastructures (SDI) provide the appropriate framework to cover both aspects, integrating distributed data sources on demand. In this paper we present CityGML, a multi-purpose and multi-scale representation for the storage of and interoperable access to 3D city models in SDIs. CityGML is based on the standard GML3 of the Open Geospatial Consortium and covers the geometrical, topological, and semantic aspects of 3D city models. The class taxonomy distinguishes between buildings and other man-made artifacts, vegetation objects, waterbodies, and transportation facilities like streets and railways. Spatial as well as semantic properties are structured in five consecutive levels of detail. Throughout the paper, special focus is on the utilization of model concepts with respect to different tasks in disaster management.

This paper tackles the need of enhanced population data for disaster management and aid delivery studies in developing countries. It analyses the usefulness of a set of spatial data layers, including medium resolution satellite imagery, for population density estimations in rural Zimbabwe. The exercise conducted on a 185 × 185km area at a grid cell size of 150m allowed us to develop a methodology that can be extended to the whole of Zimbabwe.

The surface modelling of population density was implemented by integrating 4 main variables: land use, settlements, road network, and slopes. During the modelling procedure, pixel weighting values were allocated according to pre-defined decision rules. In a final step the district population counts of the recent Zimbabwean census were distributed among all pixels of the relevant district according to the pixel weighting values. The resulting land use information and population data can be linked to vulnerability and food insecurity.

In order to be transferred to other countries, the modelling procedure needs to be adapted to case specific characteristics, the determination of which requires a certain level of local / expert knowledge. In addition, passive sensors might not provide sufficient cloud free satellite data for regions lying within the moist tropics.

Disaster management depends on large volumes of accurate, relevant, on-time geo-information that various organizations systematically create and maintain. In The Netherlands municipalities are an important party in the safety management chain when accurate geo-information is concerned. As opposed to municipalities in many other countries Dutch municipalities not only carry out central government policies but also have several policy areas in which they operate autonomously. Hence Dutch municipalities have a wealth of geo-information. This is why in the safety management chain Dutch municipalities are often referred to as the fourth column besides the police, the fire department and medical care. However, due to the municipal autonomy the geo-information is structured and described in as many ways as there are municipalities. This results in a situation in which the semantics of geo-information is not even always clear to the producer and whereby formal semantics are almost never available. On the other hand following several large scale disasters such as the fire in a pub in Volendam and the exploded fireworks factory in Enschede Dutch government has decided to reorganise disaster management in The Netherlands in so called safety regions. These regions consist of several municipalities. Thus this development increases the demand for easily accessible and readily available relevant, standardized and accurate geo-information from municipalities on a regional level.

Working from the same municipal autonomy that has created the described difficulties Dutch municipalities have organised themselves to found DataLand. DataLand is the non-profit on-stop-shop for municipal geo-information. The mission of this initiative is to make the municipal geo-information widely accessible. In fulfilling its mission DataLand together with its participating municipalities standardizes the registrations and formalizes the semantics so that third parties know what information they get. Also DataLand actively monitors the data quality.

This paper will focus primarily on how DataLand as a one stop shop offers a solution to the problem of data management between the different columns involved in disaster management. The paper will demonstrate how challenges for data management, data collection, translation, integration, classification and attributes schemes, temporal aspects (up-to-dateness, history, predictions of the future) are tackled by using simple, low cost IT solutions. Also the paper will describe how open communication is of overriding importance in creating co-operation between different institutions involved with disaster management. Secondly how this solution works in practice will be demonstrated by discussing the results of a reallife, real-time test that DataLand recently conducted together with the Regional Rescue Service Rotterdam-Rijnmond (RHRR) over the course of a year. This experiment not only showed the improved response time of this rescue service body but also for the first time gave clear insight in the user needs of disaster management bodies with regard to municipal geoinformation. In closing the paper will reflect on the lessons learned in developing a one-stop-shop for municipal geo-information. This is important as the concept of a one-stop-shop to facilitate smooth data flows between different partners involved in disaster management is also feasible for other parties involved in disaster management.

The ability to navigate a 3d virtual environment in real-time has a high priority in connection with different applications for disaster management. This paper presents GRIFINOR — a platform for applications within this area. As a part of GRIFINOR, three new innovations have been promoted. They are presented in this paper as well. In many situations it is important that geoinformation can be accessible for queries within a very short time-frame (minutes). Questions about spatial reasoning and volumetric calculations in connection with different types of simulation has been very dependent on a priori models and very fast computer graphics hardware and software. It is also essential that the features of the model become real objects with attributes etc. Urban 3d models have traditionally been built as wire frame models. This makes it very difficult and in some situations impossible to attach geoinformation to the spatial structure in a way so that it is useful for real-time navigation in 3d. These wire frame models are not suitable for either a connection to a spatial database or for spatial queries. Centre for 3D GeoInformation at Aalborg University, is developing a system that handles 3D data structures as objects with facilities to support real-time geovisualization. The need for a new concept in this area has been one of the major motivations for the development of GRIFINOR. Instead of dealing with simple geometry in a CAD-based environment, GRIFINOR is developed to support object-oriented technology.

This paper presents a decision support system integrated in a GIS with the aim of managing optimal navigation of emergency medical vehicles in urban areas. The focus is particularly on individuation of the shortest route for ambulance facilities. We do not simply propose a solution to the calculation of the shortest route in the traditional terms of distance to be traveled but rather the individuation of a route bearing in mind the set of factors that can cause delayed ambulance response. For this purpose, an “expert system” is integrated with the traditional algorithms for calculating the shortest route (such as Dijkstra’s algorithm). To build the expert system, a set of rules is deduced from observation of the behavior of ambulance drivers, and a model of the urban road network is built in order to apply the algorithm calculating the shortest route. We thus aim to provide a decision support tool that can maximize emergency service vehicle response, an evidently critical factor with a life or death impact. Finally, we stress the theoretical and practical difficulties of interaction among two such different tools as the “expert system” and a mathematical method calculating the “shortest route”.

This paper discusses GIS and disaster preparedness and management by a public safety agency at the scale of a small metropolitan area or jurisdiction within a larger metropolitan area. After briefly reviewing the local, South Florida, context, the paper focuses first on the agencies experiences with mostly Federal GIS programs — ALOHA, CATS/JACE and HAZUS-MH — geared to managing natural and man-made disasters. The next portion of the paper focuses on an evolving enterprise GIS within the public safety agency that has included projects related to: use of GIS technology for critical incident response; homeland security; and incorporating and anticipating wireless technology in data collection, and distribution. The paper concludes with lessons learned and recommendations in terms of both technology and organizational imperatives.

The POIRE project focused on making the information demand of crisis managers and disaster fighters more explicit by a stepwise refinement of their need for relevant information. A five step analysis was used to define the basis questions for each working process defined to be activated during crisis management and disaster fighting. Maps appeared a valuable method for communicating the answers on the raised questions. Maps that look more like PowerPoint sheets than GIS based presentations. The presentation of the core information implies focus on what to show and what to omit. The huge amount of data to be effectively searched in moments of crisis, requires a smart information retrieval instrument. An instrument that opens data by location, object, actor, process and organization. The classic item and pull down menu driven GIS tools need extensions for non GIS users. Ontology based menus can probably fulfill this requirement. Managing the sudden tsunami of data requires support of information management and intelligent post processing of logged data. Distinction between ‘must know’ and ‘might need to know’ needs implementation. Information should ideally be available within three mouse clicks. GIS technology can also contribute on managing the huge amount of distribution and monitoring of workload. By providing a common operational picture (SITPLOT) and features for having both overview and drill down functionality, users receive a powerful set of information processing tools enabling them to execute their jobs effectively. The merging of SCADA and GIS mechanism promise this functionality. Last but not least, clickable maps that open position located documents and vice versa (G-linked documents) provide the information process tools required. One might think that ICT is the “Haarlemmer Oil” lubricant that solves all information processing problems. One should however bear in mind, that ‘ease of use’ and not a tsunami of functionality is the factor of success.

The ability of an agency or group of agencies to manage any disaster rather than just react to crises is critically dependent on the immediate availability and flow of geographic information to responders in the field for decision support. Such information can include location of emergency management facilities, state of transportation routes, presence of population centres, susceptibility of environmental and habitat zones, scenario and simulation models etc.

However, in order that such information fits the needs of the responders we need to move beyond location-aware computing in which primarily the location of the user is considered. In particular, individual information needs optimized on the basis of location, user, goals and tasks allow for the determination of situation-adapted information solutions. This chapter discusses the potential of task-centred adaptation of geographic information for effective disaster response. It examines issues such as content adaptation, concepts of domain, utility and task-related optimization. The ultimate goal is to achieve task-specific delivery of appropriate geographic information to those in the field who are responding to an emergency situation