1 / 20

GIS for Environmental Science

GIS for Environmental Science. ENSC 3603 Class 22 4/2/09. Spatial Data Analysis Modeling Data mining. Spatial Data Analysis. Classical spatial analysis: Deterministic Good at answering where and what Modern spatial analysis: Probabilistic

raghnall-nulth
Télécharger la présentation

GIS for Environmental Science

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. GIS for Environmental Science ENSC 3603 Class 22 4/2/09

  2. Spatial Data AnalysisModelingData mining

  3. Spatial Data Analysis • Classical spatial analysis: Deterministic • Good at answering where and what • Modern spatial analysis: Probabilistic • Tries to answer what-if scenarios that contain a certain amount of uncertainty due to data that is incomplete or has errors. • Major objectives are to investigate possible spatial relationships such as association, neighborhood, proximity, and autocorrelation. • And to discover spatial heterogeneity such as clustering, pattern, dispersion, variability and anomaly. (Lo and Yeung,2007 Chapter 10)

  4. Spatial Data Analysis • Modern spatial analysis consists of: • a database model • a set of statistical and graphical data analysis tools • a set spatial visualization tools

  5. Spatial Data Analysis • Ways spatial analysis can be used with GIS: • Free standing, ex. CrimeStat, GeoVista • Embedded in Statistical Software, ex. SAS/GIS, S+SPATIALSTAT • Loose coupling, not used much any more • Close coupling, ex. ArcGIS Spatial analyst extension • Complete integration, ex. Idrisi

  6. Spatial Data Analysis • Descriptive Statistics • Central tendency • Mean, sum of values divided by the number of values • Median, middle value in values listed least to most • Mode, most frequently occurring value.

  7. Spatial Data Analysis • Descriptive Statistics Accessed 4-4-07 http://syque.com/quality_tools/toolbook/Variation/measuring_centering.htm

  8. Spatial Data Analysis • Dispersion • Depicts the pattern of the spread of values • Range, the difference between the largest and the smallest measurement value. • Standard deviation, The square root of the sum of the values subtracted from the mean squared, divided by the number of values minus 1. • Skewness, (mean – median)/ standard deviation, • Measures the degree of asymmetry • Kurtosis, indicates the peakedness or the spread of the frequency distribution

  9. Spatial Data Analysis • Spatial Autocorrelation • Remember Toblers First Law of Geography: • “everything is related to everything else, but near things are more related than distance things”, (Tobler, 1970) • Geary’s Index and Moran’s Index are used to measure autocorrelation.

  10. Spatial Data Analysis • Quadrant Counts • A spatial pattern is covered with a Quadrat or grid and the number of points in each quadrat are counted and tabulated. Quadrat size depends on application. • Nearest-Neighbor analysis • Measures distances between sample points and their nearest neighboring points. The mean of the nearest-neighbor distance measurements is compared with expected mean distance.

  11. Spatial Data Analysis • Trend Surface Analysis • Used with continues surfaces such as elevation. Can also be used to explore other data by showing regional and local trends.

  12. Spatial Data Analysis • Gravity models • Most widely used of the spatial interaction models. • Spatial interaction is any movement over space that results from a human process. • Ex. Population migration, journey to work, information and commodity flows, and other activity that has to do with the movement of people, goods or ideas form one place to another.

  13. Spatial Data Analysis • Gravity models • Idea borrowed from Newton’s law of gravity; • The force of attraction of two bodies is proportional t the product of their masses but inversely proportional to the squared distance separating them. • F =mi x mj/d2ij • m = mass • d = distance (straight-line) Ex. Cities close to each other have greater interaction than cities further apart, and larger cities have greater influence that smaller ones.

  14. Spatial Data Analysis • Network Analysis • Closely related to spatial interaction modeling. • A network refers to a system of lines that have distance, connectivity and are topologically structured. • Ex. Transportation lines such as roads, railways and rivers and streams.

  15. Spatial Modeling • Spatial modeling process - When spatial data analysis is used to solve a spatial problem using a sequence of geoprocessing steps.

  16. Spatial Modeling • Model, a simplified representation of the real world and it’s processes. • GIS and Modeling and managing Agricultural non-point-source pollution (AGNPS) • USGS AGNPS Water shed Modeling with GIS databases by Finn et al. http://carto-research.er.usgs.gov/watershed/ppt/AGNPS_Vegas_v2.PPT#256,1,AGNPS Watershed Modeling with GIS Other models include routing applications such as the best routes for emergency vehicles and hazardous materials transpiration.

  17. Spatial Data Mining • Uses machine learning or artificial intelligence to analyze huge amounts of geospatial data. • Data mining is also known as knowledge discovery in databases. This is the process of finding previously unknown information from huge data warehouses • A logical sequence of data-processing steps including: • Data integration and cleaning, • Data selection and transformation • Data mining • Knowledge discovery and construction • Deployment

  18. Spatial Data Mining • Spatial Data Mining Techniques are complex mathematically and computationally and include: • Spatial Classification, looks for the best criteria for data set categorization. • Spatial Prediction, regression methods for prediction. • Spatial Class/Concept Description, a generalization process. • Spatial Association, looks for topological, spatial and distance relationships. • Spatial Clustering, find the optimum number of clusters in a data set. • Spatial Outlier Analysis, Graphical and Quantitative outlier detection. • Spatial time-Series Analysis, most complex logically and technically.

  19. Read and Do • Spatial analyst Chapter 2 quick-start tutorial. Spatial PDF and Spatial.zip Data are one the Class web site. • In ArcGIS turn on Spatial Analyst: Tools, Extensions, Then right click on the tool bar to check Spatial analyst on. • Read Chapter 10 in Lo and Yeung (2007) • Next Thursday turn in ESRI Spatial Analyst quick start tutorial map in and email as a PDF. Use map layout and design on the map. • Review progress on your Project

  20. The greatest gifts you can give your children are the roots of responsibility and the wings of independence. Dennis Waitley "Money will buy a pretty good dog, but it won't buy the wag of his tail." - Anonymous 

More Related