Spatial Data Analysis

1. Spatial Data Analysis Yaji Sripada

2. In this lecture you learn • What is spatial data and their special characteristics? • Spatial data analysis tasks and techniques • Applying region growing approaches to segmentation of area data Dept. of Computing Science, University of Aberdeen

3. Introduction • In many domains we process information in relation to its spatial location • E.g., epidemiological studies are dominated by geographical distribution of infected cases • Dr Snow’s study of London Cholera epidemic • engineering designs have a strong spatial basis • CAD/CAM systems deal with locations of components in a design • Image processing involves segmenting pixel data in relation to their location to identify objects of interest • Position aware devices such as mobile phones allow us to track individual movement • Support for Spatial data in MySQl (version 4.1 onwards) Dept. of Computing Science, University of Aberdeen

4. GIS • Advancement of Geographic Information Systems (GIS) and Global Positioning System (GPS) have allowed us to study most data in relation to its spatial location • We are now in a position to formulate well formed spatial queries or hypotheses • Technology is available to answer such queries or test those hypotheses • All of us will use more and more spatial data in the future Dept. of Computing Science, University of Aberdeen

5. Characteristics of Spatial Data • Spatial Data has two kinds of attributes • Spatial attributes –location information • E.g. longitude and latitude for points and boundary information for areas • Non-spatial attributes • E.g. rainfall or house prices • We are mainly interested in the non-spatial attributes • But want to study them taking their location (spatial attributes) into consideration • While relationships among non-spatial attributes are explicit relationships among spatial attributes are implicit Dept. of Computing Science, University of Aberdeen

6. Characteristics of Spatial Data • Objects with similar attributes usually are located nearby spatially • Everything is related to everything else but nearby things are more related than distant things – first law of Geography • In spatial statistics this property is called spatial auto-correlation • Most geographic locations are unique (spatial heterogeneity) • Therefore global parameters do not always accurately describe local values Dept. of Computing Science, University of Aberdeen

7. Spatial Data Analysis • Techniques to analyse data taking into consideration their location information. • Results of spatial data analysis change if spatial distribution of data changes • How data varies in space? • There are many stages of spatial data analysis • Pre-processing or Smoothing • Exploratory Spatial Data Analysis • Model building • For event prediction and hypotheses testing • For communication • Very similar to the stages involved in processing time series Dept. of Computing Science, University of Aberdeen

8. Data quality - Smoothing • Data quality is a serious issue in spatial databases • Inaccuracies in measurement of location information • E.g.Inaccuracies due to approximations in GPS • Inaccuracies due to integrating data (particularly in a GIS) from different sources each of which using a different approximation of location information • Simple smoothing techniques such as mean and median filters (refer to lecture 4) are still useful Dept. of Computing Science, University of Aberdeen

9. Exploratory Spatial Data Analysis (ESDA) • ESDA involves identification of data properties and formulating hypotheses from data • Visualization of data using GIS is particularly suited for ESDA • Results from ESDA often form input to subsequent stages of analysis • ESDA is an important step in the development life cycle • Developers gain lot of understanding of the underlying phenomena by performing ESDA • As a result developers have better understanding of user requirements • Therefore helps them in making better system design to fulfil user requirements Dept. of Computing Science, University of Aberdeen

10. Spatial Data Types • Three Types • Data referenced to a point • E.g. Location information of a restaurant • Data referenced to a path • E.g. Path information from my home to University • Data referenced to an area • E.g. information about a region bounded by a polygon • We can transform point data into area data by aggregating values over all the points in an area • Different data analysis tasks and techniques are employed for each of these data types Dept. of Computing Science, University of Aberdeen

11. Points Data • Event prediction • E.g. given the spatial distribution of crimes in an area, predict the likely location of a future crime • Given some actual observations predict unknown values at intermediate locations by interpolation • Spatial regression Dept. of Computing Science, University of Aberdeen

12. Paths Data • Finding least ‘cost’ path over a route map. • Navigation systems on modern cars find paths and communicate the path information graphically and by speech • A navigation system is a good example of the kind of systems we are interested in this course • They analyse spatial data to extract important information plus • They also communicate the extracted information in different forms to suit the user Dept. of Computing Science, University of Aberdeen

13. Area/Lattice data • Public domain is flooded with this type of data • E.g. census data is available for public as aggregated values over a census tract • Scrol – Scotland’s Census Results Online • Weather parameters such as temperature and rainfall are reported as aggregated values over a region such as Grampian and Lothian • Disease count data where counts of a disease are recorded for regions or counties • Technology to analyse and communicate this type of data has large impact on public life Dept. of Computing Science, University of Aberdeen

14. Segmentation • Analysis of area data to find regions that have similar values of one or more non-spatial attributes • E.g. segmentation finds areas in a country with high family income • Visualizations of segments is done using maps with different segments shown in different colours • Many computational approaches to segment area data • Partitioning • Hierarchical • Density-based • Grid-based and • Model-based Dept. of Computing Science, University of Aberdeen

15. Typical area analysis problem • Input • a table of area names and their corresponding attributes such as population density, number of adult illiterates etc. • Information about the neighbourhood relationships among the areas • A list of categories/classes of the attributes • Output • Grouped (segmented) areas where each group has areas with similar attribute values • Visualizations using maps do not need segmentation process • Census Website has plenty of examples • http://www.statistics.gov.uk/census2001/censusmaps/index.html • Textual presentation of segmented data requires segmentation • Textual presentations useful for visually impaired users Dept. of Computing Science, University of Aberdeen

16. Similarity with image segmentation • Spatial segmentation is performed in image processing as well • Identify regions (areas) of an image that have similar colour (or other image attributes). • Many image segmentation techniques are available • E.g. region-growing technique Dept. of Computing Science, University of Aberdeen

17. Region Growing Technique • There are many flavours of this technique • One of them is described below: • Assign seed areas to each of the segments (classes of the attribute) • Add neighbouring areas to these segments if the incoming areas have similar values of attributes • Repeat the above step until all the regions are allocated to one of the segments • You will work with a version of this technique in the practical 4 Dept. of Computing Science, University of Aberdeen

18. Spatio-temporal data analysis • Many spatial data sets have a temporal dimension as well • Census data from several census activities (UK collects census every 10 years) is spatio-temporal • Weather data for a region collected over a period of time is spatio-temporal • Spatio-temporal data analysis is concerned with data variation in space and time Dept. of Computing Science, University of Aberdeen

19. Summary • Spatial data analysis is concerned with data variation in space • How data changes with location • Spatial data analysis is different because of auto-correlation and heterogeneity in spatial data • Area data is ubiquitous and segmentation of area data can be achieved by region growing approaches Dept. of Computing Science, University of Aberdeen