Interactive application of computer assisted mass appraisal and geographic information systems

GIS (Geographic Information Systems) are concerned with the manipulation and analysis of spatial data at a "Geographic" scale. Apart from issues of storage, database query and visualization, they must deal with several significantly different types of spatial information. These may be roughly classified as: discrete objects; networks; polygonal maps; and surfaces. Each of these has a specific set of assumptions associated with it, a specific data structure, and a specific set of algorithms. This produces a high level of complexity in the construction, manipulation, analysis and comparison of these datasets. We demonstrate that when "here " is replaced by "closest to here " the resulting proximal query (a Voronoi diagram) may be used to manipulate the four categories described above, with a resulting simplification of the system. All discrete objects become "fields ", with a value at any location. These fields provide the 'context' of the object, and many applications require both the primal and dual for analysis: the Quad-Edge is therefore the data structure of choice. Thus in 2D the 'context' the dual set of adjacent neighbours and neighbourhoods, as identified by the Voronoi diagram greatly assists in the interpretation and analysis of the data. In 3D the same thing holds true: the dual graph of the 3D object set in 3D GIS perhaps a model of the rooms of a building interior gives a clear structure for expressing their adjacency relations. A direct modification of the Quad-Edge - splitting the four parts into two pairs, each containing one primal and one dual half-edge -provides a structure appropriate for CAD-type nonmanifold modelling, which we demonstrate by the construction of a large building complex, using Euler-type operators. Thus, again, the dual graph provides the context for the (primal) geometric data.

Applying a CART-based Approach for the Diagnostics of Mass Appraisal Models

GIS (Geographic Information Systems) data showcase locations of earth observations or features, their associated attributes and spatial relationships that exist between such observations. Analysis of GIS data varies widely and may include some modeling and predictions which are usually computing-intensive and complicated, especially, when large datasets are involved. With advancement in computing technologies, techniques such as Machine learning (ML) are being suggested as a potential game changer in the analysis of GIS data because of their comparative speed, accuracy, automation, and repeatability. Perhaps, the greatest benefit of using both GIS and ML is the ability to transfer results from one database to another. GIS and ML tools have been used extensively in medicine, urban development, and environmental modeling such as landslide susceptibility prediction (LSP). There is also the problem of data loss during conversion between GIS systems in medicine, while in geotechnical areas such as erosion and flood prediction, lack of data and variability in soil has limited the use of GIS and ML techniques. This paper gives an overview of the current ML methods that have been incorporated into the spatial analysis of data obtained from GIS tools for LSP, health, and urban development. The use of Supervised Machine Learning (SML) algorithms such as decision trees, SVM, KNN, and perceptron including Unsupervised Machine Learning algorithms such as k-means, elbow algorithms, and hierarchal algorithm have been discussed. Their benefits, as well as their shortcomings as studied by several researchers have been elucidated in this review. Finally, this review also discusses future optimization techniques.

Developing a geographical information system (GIS) for agricultural development in Belize, Central America

Location‐only and use‐availability data: analysis methods converge

The correlation and synthesis of a variety of two- and three-dimensional data is necessary to conceptualize and characterize ground-water flow systems in regional or site-specific areas. This paper presents an integrated, multidisciplinary, step-wise approach for conceptualizing, characterizing, and numerically simulating ground-water flow systems using Geographic Information Systems (GIS). The method presented involves seven iterative steps that can be accomplished manually or by using GIS: 1) Data gathering and preparation; 2) Field (on-site) conceptualization; 3) Surface characterization; 4) Subsurface characterization; 5) Hydrogeologic model characterization; 6) Hydrologic system characterization; and 7) Numerical model simulation. GIS may be used for: 1) data management, analysis, and visualization; 2) the integration of diverse data sources; 3) rapid development, visualization, and testing of alternative hydrogeologic and hydrologic system models; 4) the incorporation of contouring, attribute tables, and statistical analysis during data analysis and model development; and 5) the integration of basic and interpreted data bases with numerical modeling for purposes of model design, parameter discretization, statistical evaluation, and sensitivity analysis.

of ensuring data compatibility, exchange of data and use of data processing results by a wide range of researchers and other users. The solution of problems mentioned above can be significantly facilitated by creation and application of geographical information systems (GIS) technology, as a cumulative set of means of data filing, joint processing and analysis, with geographical or spatial reference. This technology comprises traditional methods of using databases with advantages of full-fledged visualization and geographical (spatial) analysis. These capabilities can distinguish GIS from other information systems and provide unique opportunities to its application for solving a wide spectrum of problems, related to analyzing and forecasting phenomena and events of the surrounding world, by contemplating and emphasizing main factors and causes, and also their possible consequences, by planning strategic solutions and current results of undertaken activities. The recent study the GIS technology was applied for construction and program realization of integrated geoinformation environment, including digital maps comprising various thematic data levels on geoscience. INDEX TERMS: 0520 Computational Geophysics: Data analysis: algorithms and implementation; 0530 Computational Geophysics: Data presentation and visualization; 0555 Computational Geophysics: Neural networks, fuzzy logic, machine learning; 0594 Computational Geophysics: Instruments and techniques; KEYWORDS: Geographic information system, geoinformatics, artificial intelligence.

A geographical information system (GIS) was used to develop a regression model designed to predict flood magnitudes in the Sandy and Clackamas river basins in Oregon. Manual methods of data assembly, input, storage, manipulation and analysis traditionally used to estimate basin characteristics were replaced with automated techniques using GIS-based computer hardware and software components. Separate GIS data layers representing (1) stream gage locations, (2) drainage basin boundaries, (3) hydrography, (4) water bodies, (5) precipitation, (6) landuse/land cover, (7) elevation and (8) soils were created and stored in a GIS data base. Several GIS computer programs were written to automate the spatial analysis process needed in the estimation of basin characteristic values using the various GIS data layers. Twelve basin characteristic data parameters were computed and used as independent variables in the regression model. Streamflow data from 19 gaged sites in the Sandy and Clackamas basins were used in a log Pearson Type III analysis to define flood magnitudes at 2-, 5-, 10-, 25-, 50- and 100-year recurrence intervals. Flood magnitudes were used as dependent variables and regressed against different sets of basin characteristics (independent variables) to determine the most significant independent variables used to explain peak discharge. Drainage area, average annual precipitation and percent area above 5000 feet proved to be the most significant explanatory variables for defining peak discharge characteristics in the Sandy and Clackamas river basins. The study demonstrated that a GIS can be successfully applied in the development of basin characteristics for a flood frequency analysis and can achieve the same level of accuracy as manual methods. Use of GIS technology reduced the time and cost associated with manual methods and allowed for more in-depth development and calibration of the regression model. With the development of GIS data layers and the use of GIS-based computer programs to automate the calculation of explanatory variables, regression equations can be developed and applied more quickly and easily. GIS proved to be ideally suited for flood frequency modeling applications by providing advanced computerized techniques for spatial analysis and data base management.

Both the geographic information system (GIS) and transportation modeling environments have seen continually developing analytic concepts and techniques. However, these developments have seldom resulted in the integration of GISs and transportation models. This paper explores the potential inherent in merging of these environments through a systematic investigation of the fundamental basis of integration. To do this, the traditional four step transportation modeling process is extended to include input and output steps. We then define functional components for GIS data handling — data management, manipulation, and analysis. The steps of modeling are matched against the list of GIS data handling functions within a matrix‐based framework. GIS functions that enhance a land‐use based urban transportation modeling process are then categorized. Conclusions are drawn and directions for future developments are discussed.

Part 1 What is a geographic information system?: geographical information systems - an overview, Duane F.Marble geographical information systems - a new frontier, Roger F.Tomlinson a classification of software components commonly used in geographical information systems, Jack Dangermond GIS versus CAD versus DBMS - what are the differences?, David J. Cowen. Part 2 Examples of practical applications: MAGI - a state-level grid cell system, State of Maryland ARC/INFO - a modern GIS, Environmental Systems Research Institute technical description of the DIME system, US Bureau of the Census prinicpal components of the census bureau's TIGER file, Joel Sobel the Tiger system - automating the geographical structure of the United States, Robert W.Marx current and potential uses of geographical information systems - the north American experience, Roger F.Tomlinson an experiment in land-use allocation with a geographical information system, C.Dana Tomlin and Kevin M.Johnston Integration Of Geological Datasets For Gold Exploration In Nova Scotia, G.F.Bonham-Carter, F.P.Agterberg and D.F.Wright modeling community vulnerability to hazardous materials using geographical information systems, Robert B.McMaster representing and applying knowledge about spatial processes in environmental management, J.R.Davis, P.Whingham, and I.W.Grant. Part 3 Operations and problems of building a database: creating large digital files from mapped data, Hugh W.Calkins manual digitizing systems, E.Alan Cameron interaction between the cartographic document and the digitizing process, Donna J.Peuquet and A.Raymond Boyle a review of digital data commonly available and some of the practical problems of entering them into a GIS, Jack Dangermond efficient digitizing through the combination of appropriate hardware and software for error detection and editing, Nicholas R.Chrisman. Part 4 GIS internals - data representation and analysis techniques: spatial data models - a conceptual framework and comprehensive review, Donna J.Peuquet historical data as an explicit component of land information systems, Ric Vrana it makes me so CROSS, David Douglas the accuracy of map overlays - a reassessment, Nicholas R.Chrisman. Part 5 Design and evaluation of GIS: establishing a geographical information system in relation to its use, a process of strategic choices, W.H.Erik DeMan a conceptual model of the manual digitizing process, Duane F.Marble, Jean P.Lauzon and Matthew McGranaghan performance eval and work-load estimation for GIS, Michael Goodchild and Brian R.Rizzo liability for information, Earl F.Epstein and Howard Roitman.

Automating the land valuation process carried out in land consolidation schemes

As a valuable source for historical research, cadastral maps have been useful in historical Geographic Information Systems (GIS) research. This article takes the cadastral maps of the French Concession in Shanghai (1931–1941) as an example to reconstruct the spatial distribution and changes of landowners' real estate, tracking nationalities, land value and parcel splitting/merging, and to explore the spatial correlation with the population, commercial shops and land value by combining the socio-economic data during this period. We argue that the use of a data-enriched historical cadastre offers a unique vantage point to understand the spatial and economic dynamics at work in the French Concession. This example highlights the need for accurate and integral historical data and how these play a decisive role in the process of the historical reconstruction in historical GIS.

We are modifying our environment at unprecedented rates and scales. We can, however, debate the specific spatial dimensions, rates and significance of these changes. Throughout history, technology has always been a key factor facilitating change. Technology in use today can create environmental change on spatial and temporal scales never before possible. Yet, it also offers us the ability to facilitate our investigations and leads to a more complete understanding of human impact on our environment. Through appropriate use of technologies we can move a significant way toward an environmentally sound management of the Earth's natural resources. Planning and development can no longer take these natural resources for granted. We have to consider not only the needs of the current generations but also those of future generations. The concept of sustainable development, originally set forth in the World Conservation Strategy (1980) and popularized by the Brundtland Commission in 1987 has been defined as “development that meets the needs of the present without compromising the ability of future generations to meet them” (Estes et al. 1992). This statement, however, is still a declaration of intent and has to be specified and tested as to the degree to which it can be made operational within given regions of the globe for specific resources of concerns. Further compounding the issue is the growing necessity of examining such local, regional or national problems in the context of global climatic change scenarios. To carry out this concept of resource development with due consideration of the mid-term and the long-term effects of such development on the environment will require that researcher, planners and decision-makers have access to unprecedented quantities and types of resource and environmental information. Today, a whole new suite of technologies that can facilitate the acquisition of data and the integration of these data into resources management decision processes are emerging. Use of these technologies for data collection, manipulation, analysis and information extraction can improve our understanding of the global environment and through site characterization of specific study areas, measure, map, monitor and model changes in that environment, and provide decision-makers with the information they require for environmentally sound planning and development. Significant among these technologies are remote sensing and geographic information systems (GIS). Remote sensing devices on aircraft and satellites are capable of recording environmental information at staggering rates with significant economies of scale for many purposes. Geographic information systems (GIS) can integrate these data with other spatial data (for example, maps) and non-spatial date (for example, tables or text) to facilitate new forms of analyses. It has to be noted, however, that these technologies are crossing the boundaries of many traditional disciplines, and the development of academic programs in remote sensing and GIS for environmental monitoring and management poses a challenge to established programs. This paper addresses the potential of remote sensing and GIS technologies for environmental monitoring and management. It reports on the efforts at the University of Vechta to establish a new University program in environmental monitoring and management. This program is placed in an interdisciplinary environment and integrates advanced spatial technologies such as remote sensing and GIS with ecological science and environmental planning. First results of a new graduate level course in Environmental Monitoring will be presented.

A number of studies suggest that integrating Geographical Information Systems (GIS) into Geography teaching in schools has been and is challenging, and it seems that much of the early promise for the technology supporting learning in Geography has not been realised. This paper examines the progress made in Northern Ireland in implementing GIS in secondary schools. The deployment of a centrally procured entry-level GIS, in the context of a programme of centralised provision of information and communication technology (ICT) services to all schools, is examined and the results of an online survey of 85 Geography teachers provides an insight into how effective that provision has been. This combination of a regional strategy on GIS, curriculum changes and increased access to computers seems to have ensured that GIS is being used in many Geography classrooms. There is evidence that a range of GI systems are being used in schools and in a number of different ways, but mostly for teacher rather than for pupil use at present. Teachers expressed a need for coordinated training in order to make full use of the hardware and software available.

ABSTRACT: Geographic Information Systems (GIS) are being used increasingly as a method of preparing, analyzing, and displaying data for watershed analysis and modeling. Although GIS technology is a powerful tool for integrating and analyzing watershed characteristics, the initial preparation of the necessary database is often a time consuming and costly endeavor. This demonstration project assesses the viability of creating a cost‐effective spatial database for urban stormwater modeling from existing digital and hard‐copy data sources. The GIS was used to provide input parameters to the Source Loading and Management Model (SLANM), an empirical urban stormwater quality model. Land use characteristics, drainage boundaries, and soils information were geocoded and referenced to a base data layer consisting of transportation features. GIS overlay and data manipulation capabilities were utilized to preprocess the input data for the model. Model output was analyzed through postprocessing by GIS, and results were compared to a similar recent modeling study of the same watershed. The project, undertaken for a small urban watershed located in Plymouth, Minnesota, successfully demonstrates that the use of GIS in stormwater management can allow even small communities to reap the benefits of stormwater quality modeling.