different between ubiquitous and spatial resources
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Answer:
Spatial analysis or spatial statistics includes any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques, many still in their early development, using different analytic approaches and applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures. In a more restricted sense, spatial analysis is the technique applied to structures at the human scale, most notably in the analysis of geographic data.
Map by Dr. John Snow of London, showing clusters of cholera cases in the 1854 Broad Street cholera outbreak. This was one of the first uses of map-based spatial analysis.
Complex issues arise in spatial analysis, many of which are neither clearly defined nor completely resolved, but form the basis for current research. The most fundamental of these is the problem of defining the spatial location of the entities being studied.
Classification of the techniques of spatial analysis is difficult because of the large number of different fields of research involved, the different fundamental approaches which can be chosen, and the many forms the data can take.
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Answer:
Ubiquitous sensor network has a history of applications varying from monitoring troop movement during battles in WWII to measuring traffic flows on major highways in modern cities. Real-time information is collected from such networks especially on formation, density, and trend movements etc. On the decision support level, there lies a computational challenge on how these data can be efficiently processed, such that useful intelligence can be instantly obtained. We consider a special case of “spatial groups” in this project. Given the data collected from a ubiquitous sensor network that have different densities distributed over a large geographical area, how unique groups could be formed over them in order to maximize the total coverage by these groups. The applications could be either destructive or constructive in nature; e.g. a jet fighter pilot needs to make a real-time critical decision at a split of second to locate several separate targets to hit (assuming limited weapon payloads) in order to cause maximum damage, when it flies over an enemy terrain; a town planner is considering where to station certain resources (sites for schools and hospitals, security patrol route planning, air-born food ration drops for humanitarian aid, etc.) for maximum effect, given a vast area of different distribution of densities for benevolent purposes. This paper explores this problem from technical perspective. Experiments by using clustering algorithms and linear programming are to be conducted, for evaluating their effectiveness comparatively.
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