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Spatial analysis

Spatial analysis is any of the formal techniques which studies entities using their topological, geometric, or geographic properties. Spatial analysis includes a variety of techniques using different analytic approaches, especially spatial statistics. It may be applied in fields as diverse as astronomy, with its studies of the placement of galaxies in the cosmos, or 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 geospatial analysis, the technique applied to structures at the human scale, most notably in the analysis of geographic data. It may also be applied to genomics, as in transcriptomics data.

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.

History[edit]

Spatial analysis began with early attempts at cartography and surveying. Land surveying goes back to at least 1,400 B.C in Egypt: the dimensions of taxable land plots were measured with measuring ropes and plumb bobs.[1] Many fields have contributed to its rise in modern form. Biology contributed through botanical studies of global plant distributions and local plant locations, ethological studies of animal movement, landscape ecological studies of vegetation blocks, ecological studies of spatial population dynamics, and the study of biogeography. Epidemiology contributed with early work on disease mapping, notably John Snow's work of mapping an outbreak of cholera, with research on mapping the spread of disease and with location studies for health care delivery. Statistics has contributed greatly through work in spatial statistics. Economics has contributed notably through spatial econometrics. Geographic information system is currently a major contributor due to the importance of geographic software in the modern analytic toolbox. Remote sensing has contributed extensively in morphometric and clustering analysis. Computer science has contributed extensively through the study of algorithms, notably in computational geometry. Mathematics continues to provide the fundamental tools for analysis and to reveal the complexity of the spatial realm, for example, with recent work on fractals and scale invariance. Scientific modelling provides a useful framework for new approaches.

Britain measured using a 200 km linear measurement

Britain measured using a 200 km linear measurement

Britain measured using a 100 km linear measurement

Britain measured using a 100 km linear measurement

Britain measured using a 50 km linear measurement

Britain measured using a 50 km linear measurement

Surface analysis — in particular analysing the properties of physical surfaces, such as , aspect and visibility, and analysing surface-like data “fields”;

gradient

Network analysis — examining the properties of natural and man-made networks in order to understand the behaviour of flows within and around such networks; and locational analysis. GIS-based network analysis may be used to address a wide range of practical problems such as route selection and facility location (core topics in the field of ), and problems involving flows such as those found in Hydrospatial and hydrology and transportation research. In many instances location problems relate to networks and as such are addressed with tools designed for this purpose, but in others existing networks may have little or no relevance or may be impractical to incorporate within the modeling process. Problems that are not specifically network constrained, such as new road or pipeline routing, regional warehouse location, mobile phone mast positioning or the selection of rural community health care sites, may be effectively analysed (at least initially) without reference to existing physical networks. Locational analysis "in the plane" is also applicable where suitable network datasets are not available, or are too large or expensive to be utilised, or where the location algorithm is very complex or involves the examination or simulation of a very large number of alternative configurations.

operations research

— the creation and manipulation of images, maps, diagrams, charts, 3D views and their associated tabular datasets. GIS packages increasingly provide a range of such tools, providing static or rotating views, draping images over 2.5D surface representations, providing animations and fly-throughs, dynamic linking and brushing and spatio-temporal visualisations. This latter class of tools is the least developed, reflecting in part the limited range of suitable compatible datasets and the limited set of analytical methods available, although this picture is changing rapidly. All these facilities augment the core tools utilised in spatial analysis throughout the analytical process (exploration of data, identification of patterns and relationships, construction of models, and communication of results)

Geovisualization

Abler, R., J. Adams, and P. Gould (1971) Spatial Organization–The Geographer's View of the World, Englewood Cliffs, NJ: Prentice-Hall.

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Awange, Joseph; Paláncz, Béla (2016). Geospatial Algebraic Computations, Theory and Applications, Third Edition. New York: Springer.  978-3319254630.

ISBN

Banerjee, Sudipto; Carlin, Bradley P.; Gelfand, Alan E. (2014), Hierarchical Modeling and Analysis for Spatial Data, Second Edition, Monographs on Statistics and Applied Probability (2nd ed.), Chapman and Hall/CRC,  9781439819173

ISBN

Benenson, I. and P. M. Torrens. (2004). Geosimulation: Automata-Based Modeling of Urban Phenomena. Wiley.

Fotheringham, A. S., C. Brunsdon and M. Charlton (2000) Quantitative Geography: Perspectives on Spatial Data Analysis, Sage.

Fotheringham, A. S. and M. E. O'Kelly (1989) Spatial Interaction Models: Formulations and Applications, Kluwer Academic

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doi

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MacEachren, A. M.

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CrimeStat: A Spatial Statistics Program for the Analysis of Crime Incident Locations

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doi

Miller, H. J. and J. Han (eds.) (2001) Geographic Data Mining and Knowledge Discovery, Taylor and Francis.

O'Sullivan, D. and D. Unwin (2002) Geographic Information Analysis, Wiley.

Parker, D. C.; Manson, S. M.; ; Hoffmann, M. J.; Deadman, P. (2003). "Multi-agent systems for the simulation of land-use and land-cover change: A review". Annals of the Association of American Geographers. 93 (2): 314–337. CiteSeerX 10.1.1.109.1825. doi:10.1111/1467-8306.9302004. S2CID 130096094.

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Scheldeman, X. & van Zonneveld, M. (2010). . Bioversity International.

Training Manual on Spatial Analysis of Plant Diversity and Distribution

Fisher MM, Leung Y (2001) Geocomputational Modelling: techniques and applications. Springer Verlag, Berlin

Fotheringham, S; Clarke, G; Abrahart, B (1997). "Geocomputation and GIS". Transactions in GIS. 2 (3): 199–200. :10.1111/j.1467-9671.1997.tb00010.x. S2CID 205576122.

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Openshaw S and Abrahart RJ (2000) GeoComputation. CRC Press

Diappi Lidia (2004) Evolving Cities: Geocomputation in Territorial Planning. Ashgate, England

Longley PA, Brooks SM, McDonnell R, Macmillan B (1998), Geocomputation, a primer. John Wiley and Sons, Chichester

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Murgante B., Borruso G., Lapucci A. (2009) "Geocomputation and Urban Planning" Studies in Computational Intelligence, Vol. 176. Springer-Verlag, Berlin.

Reis, José P.; Silva, Elisabete A.; Pinho, Paulo (2016). . Urban Geography. 37 (2): 246–271. doi:10.1080/02723638.2015.1096118. S2CID 62886095.

"Spatial metrics to study urban patterns in growing and shrinking cities"

Papadimitriou, F. (2002). "Modelling indicators and indices of landscape complexity: An approach using G.I.S". Ecological Indicators. 2 (1–2): 17–25. :10.1016/S1470-160X(02)00052-3.

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Fischer M., Leung Y. (2010) "GeoComputational Modelling: Techniques and Applications" Advances in Spatial Science. Springer-Verlag, Berlin.

Murgante B., Borruso G., Lapucci A. (2011) "Geocomputation, Sustainability and Environmental Planning" Studies in Computational Intelligence, Vol. 348. Springer-Verlag, Berlin.

Tahmasebi, P.; Hezarkhani, A.; Sahimi, M. (2012). "Multiple-point geostatistical modeling based on the cross-correlation functions". Computational Geosciences. 16 (3): 779–79742. :10.1007/s10596-012-9287-1. S2CID 62710397.

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ICA Commission on Geospatial Analysis and Modeling

An educational resource about spatial statistics and geostatistics

A comprehensive guide to principles, techniques & software tools

Social and Spatial Inequalities

National Center for Geographic Information and Analysis (NCGIA)

– the world body for mapping and GIScience professionals

International Cartographic Association (ICA)