R-GIS教程：R中的空间数据：将R用作GIS

该文章详细说明了ArcGIS软件中空件可视化操作流程。

亚马逊上有一本中译本的书，鉴于大家普遍反映译者翻译的很差，共享下原版的电子版。 经典好书也需要能够理解其中内容的人才能体现其价值所在。给需要的童鞋提供资源。

Introduction Spatial Data Modeller, SDM, is a collection of tools for adding categorical maps with interval, ordinal, or ratio scale maps to produce a predictive map of where something of interest is likely to occur. This release of SDM has been modified to only work with ArcGIS 9.3. The SDM modifications in this version have to do with changes in Band Collection Statistics and intersection of points in rasters. These errors caused by changes in Band Collection Statistics and intersection of points in rasters can be subtle; tools such as Calculate Weights, Area Frequency, NN Input Files, MS Large and MS Small compute had to be modified. All of the tools have help files that include references to publications discussing the applications of the methods implemented in the tool. Several of the tools create output rasters, tables, or files that require the user to enter a name. Default values are provided in most cases to serve as suggestions of the style of naming that has been found useful. These names, following ArcGIS conventions, can be changed to meet the user’s needs. To make all of the features of SDM work properly it is required that several Environment parameters are set. See the discussion of Environment Settings below for the details. The Weights of Evidence, WofE, and Logistic Regression, LR, tools addresses area as the count of unit cells. It is assumed in the WofE and LR tools that the data has spatial units of meters. If your data has other spatial units, these WofE and LR tools may not work properly.

Spatial Data Analysis: Theory and Practice provides a broad-ranging treatment of the field of spatial data analysis. It begins with an overview of spatial data analysis and the importance of location (place, context and space) in scientific and policy-related research. Covering fundamental problems concerning how attributes in geographical space are represented to the latest methods of exploratory spatial data analysis and spatial modelling, it is designed to take the reader through the key areas that underpin the analysis of spatial data, providing a platform from which to view and critically appreciate many of the key areas of the field. Parts of the text are accessible to undergraduate and master’s level students, but it also contains sufficient challenging material that it will be of interest to geographers, social scientists and economists, environmental scientists and statisticians, whose research takes them into the area of spatial analysis.

Statistics for Spatial Data (Wiley Series in Probability and Statistics) Noel Cressie | Wiley-Interscience | January, 1993 | 928 pages | English | PDF Spatial statistics-analyzing spatial data through statistical models-has proven exceptionally versatile, encompassing problems ranging from the microscopic to the astronomic. However, for the scientist and engineer faced only with scattered and uneven treatments of the subject in the scientific literature, learning how to make practical use of spatial statistics in day-to-day analytical work has approached the impossible. Designed exclusively for the scientist eager to tap into the enormous potential of this analytical tool and upgrade his range of technical skills, Statistics for Spatial Data is a comprehensive, single-source guide to both the theory and applied aspects of current spatial statistical methods.

《空间数据分析教程》是2010年科学出版社出版的图书，作者是王劲峰，廖一兰，刘鑫。

The Design And Analysis Of Spatial Data Structures - Hanan Samet

In this paper, we propose a global and local tensor factorization (GLTF) to solve the multi-criteria recommendation problem. It leverages additional criterion-specific ratings in addition to existing user-item rating data for better recommendation. Moreover, it can jointly learn a global predictive model and multiple local predictive models, not only can discover the overall structure of the entire rating tensor, but also capture diverse rating behaviors of users in individual sub-tensors...The