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The successful implementation of applications in spatial reasoning requires paying attention to the representation of spatial data. In particular, an integrated and uniform treatment of different spatial features is necessary in order to enable the reasoning to proceed quickly. Currently, the most prevalent features are points, rectangles, lines, regions, surfaces, and volumes. As an example of a reasoning task consider a query of the form "find all cities with population in excess of 5,000 in wheat growing regions within 10 miles of the Mississippi River. " Note that this query is quite complex. It requires- processing a line map (for the river), creating a corridor or buffer (to find the area within 10 miles of the river), a region map (for the wheat), and a point map (for the cities). Spatial reasoning is eased by spatially sorting the data (i. e. , a spatial index). In this paper we show how hierarchical data structures can be used to facilitate this process. They are based on the principle of recursive decomposition (similar to divide and conquer methods). In essence, they are used primarily as devices to sort data of more than one dimension and different spatial types. The term quadtree is often used to describe this class of data structures. In this paper, we focus on recent developments in the use of quadtree methods. We concentrate primarily on region data. For a more extensive treatment of this subject, see [SameS4a, SameSSa, SameSSb, SameSSc, SameSga, SameSgbj.
"In an age of big data, data journalism and with a wealth of quantitative information around us, it is not enough for students to be taught only 100 year old statistical methods using ′out of the box′ software. They need to have 21st-century analytical skills too. This is an excellent and student-friendly text from two of the world leaders in the teaching and development of spatial analysis. It shows clearly why the open source software R is not just an alternative to commercial GIS, it may actually be the better choice for mapping, analysis and for replicable research. Providing practical tips as well as fully working code, this is a practical ′how to′ guide ideal for undergraduates as well as those using R for the first time. It will be required reading on my own courses." - Richard Harris, Professor of Quantitative Social Science, University of Bristol R is a powerful open source computing tool that supports geographical analysis and mapping for the many geography and ‘non-geography’ students and researchers interested in spatial analysis and mapping. This book provides an introduction to the use of R for spatial statistical analysis, geocomputation and the analysis of geographical information for researchers collecting and using data with location attached, largely through increased GPS functionality. Brunsdon and Comber take readers from ‘zero to hero’ in spatial analysis and mapping through functions they have developed and compiled into R packages. This enables practical R applications in GIS, spatial analyses, spatial statistics, mapping, and web-scraping. Each chapter includes: Example data and commands for exploring it Scripts and coding to exemplify specific functionality Advice for developing greater understanding - through functions such as locator(), View(), and alternative coding to achieve the same ends Self-contained exercises for students to work through Embedded code within the descriptive text. This is a definitive ′how to′ that takes students - of any discipline - from coding to actual applications and uses of R.
Currently, spatial analysis is becoming more important than ever because enormous volumes of spatial data are available from different sources, such as GPS, Remote Sensing, and others. This book deals with spatial analysis and modelling. It provides a comprehensive discussion of spatial analysis, methods, and approaches related to human settlements and associated environment. Key contributions with empirical case studies from Iran, Philippines, Vietnam, Thailand, Nepal, and Japan that apply spatial analysis including autocorrelation, fuzzy, voronoi, cellular automata, analytic hierarchy process, artificial neural network, spatial metrics, spatial statistics, regression, and remote sensing mapping techniques are compiled comprehensively. The core value of this book is a wide variety of results with state of the art discussion including empirical case studies. It provides a milestone reference to students, researchers, planners, and other practitioners dealing the spatial problems on urban and regional issues. We are pleased to announce that this book has been presented with the 2011 publishing award from the GIS Association of Japan. We would like to congratulate the authors!
This book covers fundamental aspects of spatial data modelling specifically on the aspect of three-dimensional (3D) modelling and structuring. Realisation of "true" 3D GIS spatial system needs a lot of effort, and the process is taking place in various research centres and universities in some countries. The development of spatial data modelling for 3D objects is the focus of this book.
Spatial Reasoning for Effective GIS by Joseph K. Berry This incisive and witty book describes the development of geographic technology from maps that simply tell us "Where is what?" to systems that help us decide "So what?" It encourages new understandings of mapped data, data analysis procedures, and the uses of maps, fostering an appreciation of GIS as an effective analytical tool in many complex processes. The cover image was generated by Innovative GIS Solutions, Inc., Fort Collins, Colo., using its RAPiD Surfing software to enhance the terrain analysis capabilities available with the ARC/INFO GIS.?* The image was created using Digital Elevation Model data for the Elsinore Valley Municipal Water District of the Santa Ana mountains in southern California. The image represents a 3-D perspective looking north toward Lake Elsinore with partial renderings of analytical hillshading and shaded relief draped on a wire frame elevation model. ?*RAPiD Surfing is a trademark of Innovative GIS Solutions, Inc., Fort Collins, Colo. ARC/INFO is a registered trademark of Environmental Systems Research Institute Inc., Redlands, Calif.
Geospatial information modeling and mapping has become an important tool for the investigation and management of natural resources at the landscape scale. Spatial Statistics: GeoSpatial Information Modeling and Thematic Mapping reviews the types and applications of geospatial information data, such as remote sensing, geographic information systems
This book presents most recent research studies on mapping and spatial analysis of socio-economic and environmental indicators used by various national and international contributors to regional development projects. It gathers the best contributions to the 1st International Conference on Mapping and Spatial Analysis of Socio-Economic and Environmental Indicators for the Local and Regional Sustainable Development. The conference was held in southern Tunisia, Tataouine in March 2015.The research studies focused on generating and analyzing indicators in various domains of Agriculture, Energy, Industry, Tourism, Transport, Urban Planning, Exploitation of Natural Resources, Infrastructure, Health, Environment, Education, Information and Communication Technologies, Social Affairs and Employability, and Culture and Sport. Socio-economic and environmental indicators are important in regional development plans and strategies as they allow to observe and analyze changes in the economic growth and to measure their impact on the environment and on social networks/daily life of citizens. On the basis of well-defined geomatic approaches, and particularly, through sophisticated digital mapping and spatio-temporal analyses, authors focused on retrieving indicators to evaluate the exploitation rate of natural resources, intensity of the energy consumption in various economic sector, net migratory flows, quality checking of the air in urban areas, adaptation to climate change, and vulnerability of the coastal domain and risk of marine submersion due to sea-level rise. The book is of interest not only to investors and contributors to regional development projects, but also to all relevant policy makers.
This book explores the concept of a map as a fundamental data type. It defines maps at three levels. The first is an abstract level, in which mathematic concepts are leveraged to precisely explain maps and operational semantics. The second is at a discrete level, in which graph theory is used to create a data model with the goal of implementation in computer systems. Finally, maps are examined at an implementation level, in which the authors discuss the implementation of a fundamental map data type in database systems. The map data type presented in this book creates new mechanisms for the storage, analysis, and computation of map data objects in any field that represents data in a map form. The authors develop a model that includes a map data type capable of representing thematic and geometric attributes in a single data object. The book provides a complete example of mathematically defining a data type, ensuring closure properties of those operations, and then translating that type into a state that is suited for implementation in a particular context. The book is designed for researchers and professionals working in geography or computer science in a range of fields including navigation, reasoning, robotics, geospatial analysis, data management, and information retrieval.
Geographical Information Systems (GIS) provide an enhanced environment for spatial data processing. The ability of geographic information systems to handle and analyse spatially referenced data may be seen as a major characteristic which distinguishes GIS from information systems developed to serve the needs of business data processing as well as from CAD systems or other systems whose primary objective is map production. This book, which contains contributions from a wide-ranging group of international scholars, demonstrates the progress which has been achieved so far at the interface of GIS technology and spatial analysis and planning. The various contributions bring together theoretical and conceptual, technical and applied issues. Topics covered include the design and use of GIS and spatial models, AI tools for spatial modelling in GIS, spatial statistical analysis and GIS, GIS and dynamic modelling, GIS in urban planning and policy making, information systems for policy evaluation, and spatial decision support systems.
Recent years in North America have seen a rapid development in the area of crime analysis and mapping using Geographic Information Systems (GIS) technology. In 1996, the US National Institute of Justice (NIJ) established the crime mapping research center (CMRC), to promote research, evaluation, development, and dissemination of GIS technology. The long-term goal is to develop a fully functional Crime Analysis System (CAS) with standardized data collection and reporting mechanisms, tools for spatial and temporal analysis, visualization of data and much more. Among the drawbacks of current crime analysis systems is their lack of tools for spatial analysis. For this reason, spatial analysts should research which current analysis techniques (or variations of such techniques) that have been already successfully applied to other areas (e.g., epidemiology, location-allocation analysis, etc.) can also be employed to the spatial analysis of crime data. This book presents a few of those cases.