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Lectures 1-3

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Geography 3211A/B
Jacek Malczewski

Lecture 1 and 2 Geographical / Spatial Data ­ A datum is regarded as spatial if it can be associated with a location ­ Spatial data that have reference to a location on the Earth’s surface are termed geographical or georeferenced data. Spatial vs. non-spatial statistical analysis ­ Non-spatial analysis o Spatial (geographical) data are analyzed using conventional statistical methods o The geographical coordinates are excluded from the computational procedures. o The results are independent of the spatial arrangement of geographical entities. ­ Spatial analysis o Spatial (geographical) data are analyzed using spatial statistical methods. o The geographical coordinates are included into the computational procedures o The results depend on the spatial arrangement of geographical entities. Properties of Spatial Data ­ Spatial dependence o The first law of geography: all things are related, but nearby things are more related than distant things. ­ Spatial heterogeneity (or non-stationarity) o The second law of geography: conditions vary (“smoothly”) over the Earth’s surface. ­ The properties of geographical (spatial) data present a fundamental challenge to the classic (non-spatial) statistics. ­ They violate the classic assumptions of independence and homogeneity. ­ Spatial statistics: methods specifically designed to analyze the properties of geographical data. Exploratory Spatial Data Analysis (ESDA) ­ What is exploratory data analysis (EDA)? o Objectives of EDA  Pattern detection in data  Hypothesis formulation from data  Some aspects of model assessment o EDA’s methods  Graphical and visual methods (histograms, box plots, scatter plots)  Descriptive methods rather than formal hypothesis testing  Importance of “staying close to the original data” ­ What is exploratory spatial data analysis (ESDA)? o Extension of EDA to detect spatial properties of data o Additional techniques to those found in EDA for:  Detecting spatial dependence  Detecting spatial heterogeneity (homogeneity or stationarity) ­ GIS Data and Linking o GIS data  Attribute data (tables, graphs, etc.)  Geographical data (maps) o Linking: Dynamic graphics  Linking attribute data (histogram) and geographical data (map)  Visualizing data in the attribute space and geographical space simultaneously  Useful for exploring spatial stationary (homogeneity) of spatial patterns and processes. ­ ESDA Techniques o Spatial Heterogeneity (homogeneity)  Linked Histogram • Linking attribute data (histogram) and geographical data (map)  Linked -plot (Box and Whisker Plot) • Provides a graphical summary of important features of a dataset o Median value is the middle value in the data set.  Described as being the second quartile o Q1 – the lower quartile (lower 25% of data) o Q3 – the upper quartile (upper 25% of data) o Interquartile range (data between th
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