Haining, R. P. 1990 Spatial data analysis in the social and environmental sciences. (Cambridge: Cambridge University Press).  Home/Search   Document Not in Database   Summary   Related Articles   Check

....from geo spatial data, the focus of this work, are crucial to organizations which make decisions based on large spatial datasets. These organizations are spread across many domains including ecology and environmental management, public safety, transportation, public health, business, and tourism [3, 14, 18, 11, 32, 37]. Association rule nding [13, 1, 2, 13, 22, 30, 31, 33] is an important data mining technique which has helped retailers interested in nding items frequently bought together to make store arrangements, plan catalogs, and promote products together. Spatial association rules [17] are spatial ....

R.J. Haining. Spatial Data Analysis in the Social and Environmental Sciences. In Cambridge University Press, Camb dge, U.K, 1989.

....geo spatial data, the focus of this work, are crucial to organizations which make decisions based on large spatial datasets. These organizations are spread across many domains including ecology and environmental management, public safety, transportation, public health, business, travel and tourism [3, 12, 15, 9, 23, 26, 29]. We will focus on the application domain of ecology where scientists are interested in nding frequent co occurrence among boolean spatial features, e.g. drought, El Nino, substantial Supported in part by the Army High Performance Computing Research Center under the auspices of the Department ....

R.J. Haining. Spatial Data Analysis in the Social and Environmental Sciences. In Cambridge University Press, Camb dge, U.K, 1989.

....on large spatial data sets. These organizations are spread across many domains including ecology and environment management, public safety, transportation, public health, business, travel and tourism. Albert and McShane, 1995, Hohn and A.E. Liebhold, 1993, Issaks et al. 1989, Krugman, 1995, R.J.Haining, 1989, Shekhar et al. 1993, Yasui and Lele, 1997] Classical data mining algorithms [Agrawal, 1994, Fayyad, 1997] often make assumptions(e.g. independent, identical distributions) which violate the first law of Geography: everything is related to everything else but nearby things are more related ....

R.J.Haining (1989). Spatial Data Analysis in the Social and Environmental Sciences. Cambridge University Press, Cambridge, U.K.

....geo spatial data, the focus of this work, are crucial to organizations which make decisions based on large spatial data sets. These organizations are spread across many domains including ecology and environment management, public safety, transportation, public health, business, travel and tourism. [2, 15, 17, 21, 29, 35, 39]. Classical data mining algorithms [1, 10] often make assumptions(e.g. independent, identical distributions) which violate the first law of Geography: everything is related to everything else but nearby things are more related than distant things [5, 36] In other words, the values of attributes ....

R.J.Haining. Spatial Data Analysis in the Social and Environmental Sciences. Cambridge University Press, Cambridge, U.K., 1989.

....as observations on time series, it is perhaps surprising that not more use has been made of this source of information. With an adequate choice of explanatory variables, this spatial dependence may be readily drawn into a model. The literature on spatial statistics is substantial (see for example [7, 8, 21, 24, 15, 1, 16], and more recently [9, 3, 13] Attention has also been given to the potentials for integrating Geographical Information Systems (GIS) with modelling and analysis tools, for instance in [14, 10, 19] and [20] Some of the analytical tools are available in the SPLUS spatial statistics module ....

Robert P. Haining. Spatial data analysis in the social and environmental sciences. Cambridge University Press, Cambridge, 1990.

....from the other techniques. Introduction There has been recent growth in literature examining the question of whether road construction, or more broadly transport infrastructure development, causes deforestation in developing countries (e.g. Chomitz and Gray [3] Nelson and Hellerstein [5], see also Kaimowitz and Angelsen[4] for a review of the literature) This literature is based on the basic von Thunen insights on the role of location and transportation costs in influencing spatially explicit economic activities. However, with diminishing frontier areas, the age of new road ....

Haining, R. Spatial Data Analysis in the Social and Environmental Sciences. Cambridge: Cambridge University Press, 1990.

....Most methods Donato Malerba, Floriana Esposito and Francesca A. Lisi 542 are exploratory and when applied to spatially correlated data some of them are of unknown reliability having been developed initially, like so many areas in statistics, for situations where observations are independent [9]. This contrasts with the nature of spatial data where spatial objects are influenced by their neighboring objects as pointed out by [7] In recent times, alternative approaches to spatial analysis have been emerged. In particular, the extension of data mining methods and techniques to spatial ....

Haining, R. 1990. Spatial data analysis in the social and environmental sciences. Cambridge University Press.

....outlier , the difference functions are Z i and I i , where Z i = f(i) Gamma f oe f , I i = P j (W ij Z[f(j) and f and oe f are the mean and standard deviation of the attribute function f(i) The statistic test function ST is (Z[f(i) Theta ( P j (W ij Z[f(j) 0. A scatterplot [7, 15] shows attribute values on the X axis and the average of the attribute values in the neighborhood on the Y axis. A least square regression line is used to identify spatial outliers. A scatter sloping upward to the right indicates a positive spatial autocorrelation (adjacent values tend to be ....

R.P. Haining. Spatial Data Analysis in the Social and Environmental Sciences. Cambridge University Press, 1993. 34

....geo spatial data, the focus of this work, are crucial to organizations which make decisions based on large spatial datasets. These organizations are spread across many domains including ecology and environmental management, public safety, transportation, public health, business, travel and tourism [3, 12, 15, 9, 23, 26, 29]. We will focus on the application domain of ecology where scientists are interested in finding frequent co occurrence among boolean spatial features, e.g. drought, El Nino, substantial 1 Supported in part by the Army High Performance Computing Research Center under the auspices of the Department ....

R.J. Haining. Spatial Data Analysis in the Social and Environmental Sciences. In Cambridge University Press, Cambfidge, U.K, 1989.

....data are crucial to organizations which make decisions based on large spatial data sets. These organizations are spread across many domains including ecology and environment management, pub1 2 lic safety, transportation, public health, business, travel, and tourism [AM95, HGL93, IES89, Kru95, Hai89, SYH93, SNM 95, YL97] Extracting interesting and useful patterns from spatial datasets is more difficult than extracting corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation. ....

R.J. Haining. Spatial Data Analysis in the Social and Environmental Sciences. In Cambridge University Press, Cambridge, U.K, 1989.

....data are crucial to organizations which make decisions based on large spatial data sets. These organizations are spread across many domains including ecology and environment management, pub1 2 lic safety, transportation, public health, business, travel, and tourism [AM95, HGL93, IES89, Kru95, Hai89, SYH93, SNM 95, YL97] Extracting interesting and useful patterns from spatial datasets is more difficult than extracting corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial autocorrelation. ....

R.J. Haining. Spatial Data Analysis in the Social and Environmental Sciences. In Cambridge University Press, Cambridge, U.K, 1989.

.... simple standard regression specifications A number of authors have investigated the efficiency of OLS relative to GLS estimator when the errors are serially or spatially correlated by using various efficiency criteria (see Bloomfield and Watson, 1975; Kramer, 1980; Kramer and Donninger, 1987; Haining, 1990; Griffith, 1988; Cordy and Griffith, 1993; Kramer and Baltagi, 1996) The most remarkable feature of the results obtained is that the relative efficiency depends mainly on the error process considered and the degree of correlation. Another aspect of the resulting analysis shows the behaviour of ....

Haining, R. (1990): Spatial Data Analysis in the Social and Environmental Sciences. Cambridge University Press, Cambridge .

....the focus of this work, are crucial to organizations which make decisions based on large geospatial data sets. These organizations are spread across many domains including ecology and environment management, public safety, transportation, public health, business logistics, travel and tourism. [2, 26, 30, 36, 46, 64, 72]. Classical data mining algorithms [1, 19] often make assumptions(e.g. independent, identical distributions) which violate the first law of Geography: everything is related to everything else but nearby things are more related than distant things [12, 66] In other words, the values of ....

R.J.Haining. Spatial Data Analysis in the Social and Environmental Sciences. Cambridge University Press, Cambridge, U.K., 1989.

....ecological and biological processes whose form is usually represented in terms of classical physical theory. Unlike statistical models which are parsimonious, these models are usually rich in structure, and their process of estimation is somewhat different from their statistical neighbors (Haining, 1990). Moreover, the possibility for error propagation through space and through different elements of the model structure gives rise to somewhat different types of visualization from those in the statistical domain (White, 1984) Such models to date have not been coupled to GIS or other forms of ....

....also concerned with processes of aggregation over space (and time) across space through interactions, and through various data and model operations. There are new possibilities for building error models and incorporating these into various statistical and mathematical models of spatial processes (Haining, 1990). So far, there has been little research into ways in which such integrations might occur. But with the growth in data intensive contexts, in software to represent and model such data, and in new ways of communicating such material through visualization, it is essential to have clear and ....

Haining, R.P. (1990) Spatial Data Analysis in the Social and Environmental Sciences. Cambridge University Press, Cambridge, UK.

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Haining, R. P. 1990 Spatial data analysis in the social and environmental sciences. (Cambridge: Cambridge University Press).

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Haining, R. 1989. Spatial Data Analysis in the Social and Environmental Sciences. In Cambridge University Press, Cambridge, U.K.

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Haining, R., 1990, Spatial Data Analysis in the Social and Environmental Sciences: Cambridge University Press, Cambridge, 409 p.

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