spatial analysis

Spatial Data Analysis (SDA)

Spatial is considered as a core infrastructure of modern IT world, which is substantiated by business transactions of major companies. Spatial data analysis focuses on detecting patterns and exploring and modeling relationships between such patterns in order to understand processes responsible for observed patterns. In this way, spatial data analysis emphasizes the role of space as a potentially important explicator of socioeconomic systems, and attempts to enhance understanding of the working and representation of space, spatial patterns, and processes.

The analysis of spatial interaction data has a long and distinguished history in the study of a wide range of human activities, such as transportation movements, migration, and the transmission of information.

This module is designed to present a firm understanding of spatial data science to the learners, who would have a basic knowledge of data science and data analysis, and eventually to make their expertise differentiated from other nominal data scientists and data analysts. This introductory course defines spatial data science and answering why spatial is special from three different perspectives – business, technology, and data.

Prerequisites                          : GLM, SFDS, MFDS

Objectives/Content               :

  1. Introduction to spatial analysis and exploratory spatial data analysis. Specificities and problems with spatial data. Proximity and accessibility analysis. Spatial concentration analysis and density maps. Introduction to spatial interpolation.
  2. Introduction to spatial statistics. Overview of spatial statistics methods and techniques. Introduction to spatial autocorrelation. Measurement and interpretation of global autocorrelation indexes. Measurement and rendering of local autocorrelation, clustering and spatial association indexes.
  3. Introduction to regression analysis of spatial data. Ordinary least square, geographical weighted regression and introduction to spatial autoregression methods.

Reference:

  1. Li, D., Wang, S., & Li, D. (2015). Spatial data mining. Berlin, Heidelberg: Springer Berlin Heidelberg.
  2. Toms, S.: ArcPy and ArcGIS – Geospatial Analysis with Python. (2015). https://doi.org/10.1017/CBO9781107415324.004.
  3. Dorman, M.: Learning R for Geospatial Analysis. (2014). https://doi.org/10.1002/ejoc.201200111.
  4. Bahgat, K.: Python Geospatial Development Essentials. Packt Publishing (2015).
  5. Lawhead, J.: Learning Geospatial Analysis with Python. Packt Publishing (2013).
  6. Eldawy, A., Mokbel, M.F.: The Era of Big Spatial Data: A Survey. Now Publishers (2016).
  7. Fischer, M.M., Wang, J.: Spatial Data Analysis: Models, Methods and Techniques. Springer Berlin Heidelberg (2011).
  8. Haining, R.P., Haining, R.: Spatial Data Analysis: Theory and Practice. Cambridge University Press (2003).
SDA1Introduction to Spatial Data Analysis– Introduction to SDA
– Use case examples
– The nature of spatial data (conceptualization and Representation, spatial data matrix, Quantifying spatial dependence)
– Spatial Data sampling
– Preprocessing on spatial data
– Spatial data visualizations
– Spatial Clustering (hotspot)
– Hypothesis testing and spatial autocorrelation
– Spatial interpolation

SDA2Introduction to Spatial Data Analysis 2– Krigging
– Spatial point process
– Spatial variation models
– Models for discrete-valued spatial variables
– Geographically Weighted Regression (GWR)
– Hierarchical Bayesian models
– Spatio temporal analysis