Spatial Data Analysis Yaji Sripada

Dept. of Computing Science, University of Aberdeen2 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 Aberdeen3 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 Aberdeen4 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 Aberdeen5 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 Aberdeen6 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 Aberdeen7 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 Aberdeen8 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 Aberdeen9 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 Aberdeen10 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 Aberdeen11 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 Aberdeen12 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 Aberdeen13 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 Aberdeen14 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 Aberdeen15 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 – smaps/index.htmlhttp:// smaps/index.html Textual presentation of segmented data requires segmentation –Textual presentations useful for visually impaired users

Dept. of Computing Science, University of Aberdeen16 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 Aberdeen17 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 Aberdeen18 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 Aberdeen19 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