The development of specialized software for spatial data analysis has seen rapid growth since the lack of such tools was lamented in the late 1980s by Haining (1989) and cited as a major impediment to the adoption and use of spatial statistics by GIS researchers. Initially, attention tended to focus on conceptual issues, such as how to integrate spatial statistical methods and a GIS environment (loosely vs. tightly coupled, embedded vs. modular, etc.), and which techniques would be most fruitfully included in such a framework. Familiar reviews of these issues are represented in, among others, Anselin and Getis (1992), Goodchild et al. (1992), Fischer and Nijkamp (1993), Fotheringham and Rogerson (1993, 1994), Fischer et al. (1996), and Fischer and Getis (1997). Today, the situation is quite different, and a fairly substantial collection of spatial data analysis software is readily available, ranging from niche programs, customized scripts and extensions for commercial statistical and GIS packages, to a burgeoning open source effort using software environments such as R, Java and Python. This is exemplified by the growing contents of the software tools clearing house maintained by the U.S.- based Center for Spatially Integrated Social Science [CSISS] (see http://www.csiss.org/clearinghouse/).

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.0016-7363.2005.00671.x

GeoDa: An Introduction to Spatial Data Analysis

This contribution discusses the problemof providing measures of significance ofprediction results when the predictionswere generated from spatial databases forlandslide hazard mapping. The spatialdatabases usually contain map informationon lithologic units, land-cover units,topographic elevation and derived attributes(slope, aspect, etc.) and the distributionin space and in time of clearly identifiedmass movements. In prediction modelling wetransform the multi-layered databaseinto an aggregation of functional values toobtain an index of propensity of the landto failure. Assuming then that the informationin the database is sufficiently representativeof the typical conditions in which the massmovements originated in space and in time,the problem then, is to confirm the validity ofthe results of some models over otherones, or of particular experiments that seem touse more significant data. A core pointof measuring the significance of a prediction isthat it allows interpreting the results.Without a validation no interpretation is possible,no support of the method or of theinput information can be provided. In particularwith validation, the added value canbe assessed of a prediction either in a fixedtime interval, or in an open-ended time orwithin the confined space of a study area.Validation must be of guidance in datacollection and field practice for landslidehazard mapping.

http://basin.earth.ncu.edu.tw/download/courses/seminar_MSc/2010/0930-1_Validation%20of%20Spatial%20Prediction%20Models%20for%20Landslide%20Hazard%20Mapping.pdf

Validation of Spatial Prediction Models for Landslide Hazard Mapping

Functions to calculate measures of spatial association, especially measures of spatial autocorrelation, have been made available in many software applications Measures may be global, applying to the whole data set under consideration, or local, applying to each observation in the data set Methods of statistical inference may also be provided, but these will, like the measures themselves, depend on the support of the observations, chosen assumptions, and the way in which spatial association is represented; spatial weights are often used as a representational technique In addition, assumptions may be made about the underlying mean model, and about error distributions Different software implementations may choose to expose these choices to the analyst, but the sets of choices available may vary between these implementations, as may default settings This comparison will consider the implementations of global Moran’s I, Getis–Ord G and Geary’s C, local $$I_i$$
 and $$G_i$$
 , available in a range of software including Crimestat, GeoDa, ArcGIS, PySAL and R contributed packages

/pdf/comparing-implementations-of-global-and-local-indicators-of-17w0ryxrr4.pdf

Comparing implementations of global and local indicators of spatial association

Simplifying complexity: a review of complexity theory

/pdf/using-neural-networks-and-gis-to-forecast-land-use-changes-a-47spf9ee4x.pdf

Using neural networks and GIS to forecast land use changes: a Land Transformation Model

Keywords: modelling spaciale ; simulation ; integration Reference Record created on 2005-06-20, modified on 2016-08-08

Spatial Analytical perspectives on GIS

Keywords: Visualisation ; interaction Homme-Machine ; qualite des donnees Reference Record created on 2005-06-20, modified on 2016-08-08

Visualization In Geographical Information Systems

The problems in teaching a course on geographical information systems in higher education are reviewed and a possible syllabus that will be most relevant to needs in the United Kingdom is suggested. The syllabus is divided into six major sections relating to the context, cartographic and spatial analytical concepts, their realization in a computing environment, operations, applications and institutional issues. A particular feature is an attempt to maintain a rigid distribution between underlying theory which is relatively stable and its more volatile operationalization on computers. The paper results from a symposium held in Leicester (U.K.) late in 1988 that brought together nine specialists in the held.

A syllabus for teaching Geographical Information Systems

Preface. Acknowledgements. List of Contributors. Part I: Introductory Essays: Part II: What is Geography? Past, Present and Future: Part III: How To Study Geography: Part IV: What's What and Who's Who in Geography? : Part V: A Geographical Directory: Part VI: What Next? : Index.

The Student's Companion to Geography

(1984). Exploring the living planet with David Attenborough. Journal of Geography in Higher Education: Vol. 8, No. 2, pp. 93-113.

Exploring the Living Planet with David Attenborough.

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