Introduction to GIS. Introduction to GIS. Content

Transcription

1 1 Introduction to GIS Cees van Westen Dinand Alkema International Institute for Geo-Information Science and Earth Observation (ITC), Enschede, The Netherlands. Introduction to GIS Content Geographical data Data types (vector / raster) Data input Data organisation & management Data analysis & modelling Visualisation & output of Information

2 2 Schematic flowdiagram for the production of digital thematic maps Vegetation GIS operations Thematic object selection Point Area Line Volume Hydrology Soil - Modeling -Analysis Senarios Statistical tables GPS Aerial Satellite photographs images Digital Landscape model Cartographic processing.- Generalization - Symbolization Maps Real world Decision makers

3 3 GEOGRAPHICAL DATA Nature of geographical data geographical position - where is it? properties - what is it? dynamics (time) - when did it exist? DATA TYPES Spatial data point data (0-dimensional) linear data (1-dimensional) area data (2-dimensional) continuous surfaces (3-dimensional) Attribute data spatial attribute data non-spatial attribute data

4 4 Types of geographic phenomena Geographic field A geogr. phenom. for which, for every point in the study area, a value can be determined Or: has a value everywhere Geographic object A geogr. phenom. populate the study area with well-distinguishable, discrete, bounded entities. The space between is potentially empty Or: sparsely populating the area Continuous fields (elevation) Altitude

5 5 Discrete fields (e.g. geology) Geographic objects sparsely populating the area Position determined by: Dimension: point, linear, area? Location Shape: 0D 3D Size Orientation: direction respect to? points linear areas

6 6 Vector model In a vector-based GIS data are handled as: Points X,Y coordinate pair + label Lines series of points Areas line(s) forming their boundary (series of polygons) line feature point feature area feature Topological model: characteristics Topology: method to define spatial relations between points, lines and polygons. Topology stores: Contiguity or similarity: elements having similar characteristics (i.e. finding equal polygons). Connectivity: connection between the units (i.e. finding connected streams). Almost all GIS uses topology for vector storage.

7 7 Topological model: structure E N1 90 a3 a1 Node Topology 80 a4 Node Arcs 70 N2 N1 a1, a3, a4 N2 a1, a2, a5 60 a4 N3 a2, a3, a5 50 N4 N4 a4 a5 E N5 a6 B 40 N6 a7 N3 a2 30 a7 D a6 N5 a7 C a7 20 a2 a Arc a1 a2 Polygon Topology a3 Polygon Arcs a4 A a1, a5, a3 a5 B a2, a5, a6, a7 a6 C a7 a7 D a6 E area outside 0 map coverage Arc Topology Arc Start End Left Node Node Polyg. a1 N1 N2 E a2 N2 N3 E a3 N3 N1 E a4 N4 N1 A a5 N3 N2 A a6 N5 N5 B a7 N6 N6 B Arc Coordinate Dat a St art X Y Intermediate X Y 50, 90 90,90 90, 70 90, 10; 10, 10 10,30 10, 90 30,60 40, 70 10,30 30, 40; 60, 40; 70, 60 30, 20 60,20 70, 30; 80, 30; 80, 20 Right Polyg. A B A A B B C End X Y 90, 70 10, 30 50, 90 50, 90 90, 70 30, 20 60, 20 Raster model The entity information is explicitly recorded for a basic data unit (cell, grid or pixel)

8 8 Data models - vector and raster Vector data model y y y code code code x x x Point Line Area Raster data model Row Row Row Column Column Column Key for the vector models: intemediate point node Raster versus Vector data model Raster model Vector model Simple data structure Easy and efficient overlaying Compatible with Remote Sensing imagery High spatial variability is efficiently represented Simple for programming by user Same grid cell definition for various attributes Inefficient use of computer storage Errors in perimeter and shape Difficult to perform network analysis Inefficient projection transformations Loss of information when using large pixel sizes Less accurate and less appealing map output Complex data structure Difficult to perform overlaying Not compatible with RS imagery Inefficient representation of high spatial variability Compact data structure Efficient encoding of topology Easy to perform network analysis Highly accurate map output

9 9 Digitizing maps Scanning (automatic digitizing) Converting existing data X Y Editing Improving Vectorizing Apply attributes Sensor Raster mode Manual digitizing Improving Apply attributes Y Vector mode X Digital Landscape Model Selecting a digitising technique (1) Digitising technique Type of document Requirements Manual Digitising Document on tablet Complex maps / interpret ation from satellite imagery or aerial photographs Digit ising tablet Manual Digitising Document on-screen Complex maps / interpret ation from satellite imagery or aerial photographs Scanner or scanned document Semi-automa tic ( or int eractive ) digitising ( fully ) automa tic digitising Simple documents that require some interpretation Simple documents or separates with one type of informa tion Scanner or scanned document / processing software for semiautomatic line tracing Scanner or scanned document / processing software for the vectorisa tion process

10 10 Obtaining spatial data from 3 rd party Clearinghouses for metadata Lots of free data available: quality and detail often low or unknown DCW: Data depot (Commercial) sale of data by National Mapping Organisations and increasingly with private organisation Dutch topographic survey: ESRI: Analysis operations Retrieval, (re)classification & measurement operations Overlaying operations Neighbourhood operations Connectivity operations (after Aronoff, 1989, page 196)

11 11 1a) Data selection and retrieval Data retrieval involves the selective search, manipulation and output of data without the requirement to modify the geographic location of the features involved. (linked) Spatial and attribute data are retrieved. No changes are made to the location of spatial elements, and No new spatial elements are created To find out what exist at a particular location. What is at...? Retrieval of spatial data (points, lines, polygons or mapping units in a raster map), which are an answer to the question Where is...? Retrieval of information using conditional, logical and arithmetical operators Selection Queries Based on geometric /spatial characteristics WHERE IS.? or Using attribute data associated with spatial features WHERE ARE FEATURES WITH? or a Combination of both

12 12 Selection Queries Spatial selection using topological relationships containment (within) overlap (intersect) neighbourhood (adjacent) distance (within or beyond a distance) or combination Spatial Relationships disjoint covered by meet contains equal covers inside overlap

13 13 Spatial query Select all clinics in district A. Spatial query - Select features adjacent to other features also called MEET relationship. Share common boundaries. Apply only to line and polygon features. Features adjacent to the original selection polygon Original selection polygon

14 14 Spatial query - Select features that are nearest to other features Example: find nearest road to each clinic. Shortest distance Road Identification number Spatial query Select features intersect with other features Intersect or overlap relationships Two polygons intersect if they share a common area. Two lines intersect if they share a common line segment. A line and a polygon intersect if the line is partially or completely inside the polygon.

15 15 Spatial query - Intersect relationship Example: Select all the roads that are partially or completely located in district B. Data retrieval (raster model) Slope Rock Soil PIXEL INFORMATION Row Column Value x: y: Map 1 Soil 2 Table Soil Soil: Silt Thickness: 5 Recnr Soil Thickness 1 Sand 10 2 Silt 5 3 Clay 15 Map 2 Rock 3 Table Rock Rock: Granite Strenght: High Map 3 Slope 2 Table Slope Slope: Gentle

16 16 1b) (re)classification (re)classification involves the selection and presentation of a selected layer of data based on the classes or values of a specific attribute. It involves looking at an attribute, or a series of attributes, for a single data layer and classifying the data layer based on the range of values of the attribute. Examples: Reclassify a soil map into a PH map Classify an elevation map into classes with intervals of 50 m. Classifications Based on the number of classes before and after the classification, three types of classifications can be differentiated: a) one to one (1:1): The number of classes before is the same as the number of classes after the classification process: there are no changes in the geometry of the spatial objects, they have been re-assigned. b) many to one (M:1): The number of classes after the classification is smaller than the number of classes before the process: generalization, aggregation, merging, dissolving c) one to many (1:M): The number of classes after the classification process is more than the those before the classification: in vector format spatial objects are split in different objects; in raster format e.g. unique ID s are assigned to each pixel in the output map

17 17 Example: A geological map Reclassified geological map Geological map reclassified in 7 classes based on geological age Geological map reclassified in 3 classes based on type of lithology Many to one classification

18 18 Reclassifying a map with attribute data Attribute table: City blocks Attribute map: Land Use Cityblocks Landuse 001 Institutional 002 Commercial 003 Commercial 004 Residential 005 Residential 006 Residential 007 Industrial 008 Residential 009 Industrial 010 Industrial 011 Residential 012 Industrial 013 Residential 014 Residential 015 Residential Map: City blocks Map Calculation Input map: Landuse Domain: Landuse Pasture Bare rocks Bare rocks Pasture Lake Lake Rocks=iff(landuse="bare rocks", landuse, "?") Domain: lands Land water Ouput map: Rocks??????? Bare rocks?????????????????? Ouput map: Lands Land Lands=iff((landuse="pasture")or(landuse="bare rocks"),"land","water") Water Ouput map: Pastarea Domain: bit Pastarea = landuse = "pasture"

19 19 Data reclassification from tables Soils 2 (re-assigment based on attribute column) 3 COLUMN (X-AXIS) Soilstab.tbl Recnr Type 1 Alfisol 2 Mollisol 3 Redzina Infilt 1 infilcol Infilt Soils.soilstab.infilcol COLUMN (X-AXIS) Data classification by slicing DEM DEMCLAS COLUMN (X-AXIS) CDEM Bound class demclas 900 m 0 meter COLUMN (X-AXIS) class 3: m class 2: m class 1: m Clfy(dem,cdem)

20 20 Automatic classification The user specifies only the number of classes in the output data set, the. software determines the class break points. Examples are: Equal Interval and Equal Frequency (Zmax-Zmin)/n n: number of classes (a) Original data set New codes Original codes 1 1, , ,6 3 No. of pixel s (b) Equal interval classification New codes Original codes , No. of pixel s (c) Equal frequency classification 1c) Measurement (through geometric coordinates and topological relationship) Nr% Col% Area& Peri& Name$ NoName NoName NoName E+038 TOTAL_AREA Polygons areas and length

21 21 Measurements for each feature type Feature type Measurement Point Point x,y coordinates number of points distance between points Line Straight x,y coordinates of the beginning line and the end vertex points (nodes) length direction Curved line length shortest distance between the start and the end nodes curvature distribution of direction polygon Box x,y coordinates of opposite corners width and the length Area Circle x,y coordinates of the centre radius Polygon area perimeter X,Y coordinates of the centroid Extent of the polygon, e.g. the x,y coordinates of the lower-left and upper-right corner of the smallest rectangle that covers the pol ygon exactly. Measurements between features Feature 1 Feature 2 Distance measurements Point Point Euclidean distance (the length of a straight line) between the two points Point Line The distance between the point and the nearest location on the line. Point Polygon The distance from the point to the nearest location on the boundaries of the polygon The distance from the point to the centroid of the polygon. line Line If the two lines are not intersected, the shortest distance between the two lines. If they are intersected, this value would be zero. Line Polygon The shortest distance from a location on the line to a location on the polygon boundary. If the line touches or intersects the polygon boundary, this value would be zero. The shortest distance from a location on the line to the centroid of the polygon. Polygon Polygon The shortest distance between the two boundaries. If any distance value is zero, the two polygons are touched. The distance between the centroids of the two polygons The x,y coordinates of the intersection points between two polygon boundaries.

22 22 2) Overlaying operations Map overlaying involves the integration of multiple referenced / overlapping data layers vector based (geometrically complicated and poor operator performance) raster based (cell by cell) (Vector) overlay using polygons Polygon intersection operation After Bonham-Carter Result : new set of polygons common to both maps New topology has to be constructed

23 23 Polygon operators, examples Polygon clipping: restrict spatial extent to generalised outer boundary Polygon overwrite: first layer has priority over the second layer Vector overlay, examples impose After Bonham-Carter stamp join compare

24 24 (Raster based) overlay operation tools Arithmetic functions (+, -, *, /) Relational functions (<, >, =) Logical operations (and, or, xor, not) Conditional functions ( if, then, else ) Arithmetic operations Map A Map B Map C MapC= MapA MapC1= MapA + MapB Map C MapC2= ((MapA - MapB)/(MapA + MapB)) *100 Map C

25 25 Relational functions Map A Map B Output = MAP A > MAP B Output = FALSE 1 = TRUE Logical functions Boolean operators A AND B = A B intersection A OR B = A B union A XOR B = A B exclusion A NOT B = A B negation

26 26 Relational and logical operators F F F F F F F F F F F F F F = forest 7 = 700 m 6 = 600 m 4 = 400 m MapD=(MapA= Forest ) and (MapB <500) MapD1=(MapA= Forest ) or (MapB <500) MapD2=(MapA= Forest ) xor (MapB <500) MapD3=(MapA= Forest ) and not (MapB <500) Map D Map D = false 1 = true Map D Map D Conditional functions F F F F F MapC= iff(mapa= Forest,1,?) Map C ????? F F F? 1 1? 1 F F F?? F F??? MapC1=iff((MapA= Forest ) and (MapB= 700),1,0) F = forest 7 = 700 m 6 = 600 m 4 = 400 m Map C = false 1 = true? = undefined

27 27 Crossing operation G G F F F F G G G G F F G G G G F F G L L F F F L L L F F F L L L F F F Geology S S S S S S S S S S S S S S S S S S A A A A A A A A A A A A A A A A A A Legend Landuse F forest G Grass L Lake Legend Geology A Alluvial S Shale Cross table Landuse Geology Npix Forest * Alluvial Forest * Shale Grass * Alluvial Grass * Shale Lake * Alluvial Forest Forest Grass Grass Lake Alluvial Shale Alluvial Shale Alluvial Cross map G*S G*S F*S F*S F*S F*S G*S G*S G*S G*S F*S F*S G*S G*S G*S G*S F*S F*S G*A L*A L*A F*A F*A F*A L*A L*A L*A F*A F*A F*A L*A L*A L*A F*A F*A F*A Two-dimensional table Landuse G G F F F F G G G G F F G G G G F F G L L F F F L L L F F F L L L F F F Legend Landuse F forest G Grass L Lake Forest Grass Lake Alluvial Suitable Unsuitable Unsuitable Shale Unsuitable Suitable Unsuitable Geology S S S S S S S S S S S S S S S S S S A A A A A A A A A A A A A A A A A A Legend Geology A Alluvial S Shale Output map S S U U U U S S S S U U S S S S U U U U U S S S U U U S S S U U U S S S Legend Output map S Suitable U Unsuitable

28 28 3) Neighbourhood operations Evaluate the characteristics of an area surrounding a specific location interpolation functions topographical functions search functions Interpolation The procedure of estimating unknown values at unsampled sites using known values of exisitng observations Typical examples: Point interpolation (rainfall, spot heights, etc) Line interpolation (contours)

29 29 Digital Elevation Model (DEM) Elevation Zones Applications of DEMs Slope steepness maps, showing the steepness of the slope in degrees, percentages, or radians for each pixel. Slope direction maps (also called slope aspect maps), showing the compass direction of the slope (between degrees). Slope convexity maps, showing the change of slope angles within a short distance. From these maps you can see if the slope is straight, concave or convex in form. Hill shading map (or shadow maps), showing the terrain under an artificial illumination, with bright sides and shadows. Three dimensional views showing a bird s eye view of the terrain from a user defined position above the terrain. Cross-sections indicating the altitude of the terrain along a digitized line. Volume maps (or cut-and-fill maps)

30 30 Search functions Search functions determine the value of a target feature according to some characteristic of its neighbourhood Search function average diversity majority maximum, minimum total Description the average of the values in the neighbourhood a measure of diversity of the values in the neighbourhood, such as variance or standard deviation the number of occurrences for each value in the neighbourhood is determined; the value occurring most frequently is the calculated result the maximum/minimum value in the neighbourhood is returned. the summed total of the values in the neighbourhood Search functions The county boundaries first has to be derived from a political boundary map. Next, the selected county is used to extract the corresponding portions of the land use map. Only then the search operation which counts how many land use types do occur within the county is applied.

31 31 4) Connectivity operations To characterise spatial units that are connected Contiguity functions Proximity functions buffer zones Thiessen polygons nearest feature identification Spread functions Seek functions Network functions Proximity functions The measurement of distances between features (distance in units of length, travel time, etc.) Typical examples: Determination of buffer zones along groundwater exploration wells Construction of Thiessen polygons Determination of accessibility to drinking wells

32 32 Distance calculation C 2 =A 2 +B 2 The distance from a source pixel to its horizontal vertical neighbors is 1, and the distance from a source pixel to its diagonal neighbors is the square root of 2 (= ). A B C Proximity analysis

33 33 Buffer zone determination Rainfall stations Distance calculation Distance zones around rainfall stations Spread functions SPREAD FUNCTIONS EVALUATE PHENOMENA THAT SPREAD, DILUTE OR ACCUMULATE WITH DISTANCE TYPICAL EXAMPLES: Determination of inundated area due to dam construction Determination of flooded area due to dike burst Spreading of pollution

34 34 Spread functions Areas with an elevation below 2.35 m.a.s.l. Areas with an elevation below 2.35 m.a.s.l. and connected with the dike failure Total volume: M3 Total area: M2 Dike failure Spread functions (Iteration with propagation) First Iteration Iteration: successive repetition of a mathe matical operation, using the result of one calculation as input for the next. Propagation Second Iteration Propagation: the newly calculated value for a pixel is used in calculating the next line instead of in the next iteration. Third Iteration Fourth Iteration Flooded Cell

35 35 Seek functions SEEK FUNCTIONS DETERMINE OPTIMUM PATHWAYS USING (A) SPECIFIED DECISION RULE(S) TYPICAL EXAMPLES: Determination of the path of water flow Highway planning Seek functions Contour map Flow paths x: starting points x Automated flow path generation x x

36 36 Network analysis Network partitioning Optimal path finding 1 A 6 B J C 7 M L K 8 5 P I 4 Nodes D 2 E 9 1 N F O G H 9 Costs Lines A P Costs What is the best tract for the of a new Highway? Análisis Cadastral parcel National park Risk area Land-use Soil Slopes Alternative 1 Alternative 2 Alternative 3 Digital Terrain Model Topography Decision makers

37 37 Legend of Hazard Map Rockfall occurrence Rapid landslide Earth flow Landslide NOMINAL Very high 1.1: Very High Hazard to 2.1 rockfall 2.2 occurrence 2.3 during 2.4 rainy High season 1.2: Very High Hazard to rapid landslide and small rockfall occurrence during rainy season 1.3: Very High Hazard to 3.1 earth flow 3.2 occurrence during Moderate rainy season 1.4: Very High Hazard to reactivation of landslides 2.1: High Hazard to rockfall ocurrence during rainy 4 season Low 2.2: High Hazard to to rapid landslide and small rockfall occurrence during rainy season 2.3: High Hazard to earth flow occurrence during rainy season 2.4: High Hazard to reactivation of landslides 3.1: Moderate Hazard to rockfall ocurrence during rainy season 3.2: Moderate Hazard to rapid landslide and small rockfall occurrence during rainy season 3.3: Moderate Hazard to earth flow occurrence during rainy season 3.4: Moderate Hazard to reactivation of landslides 4: Low Hazard to landslide occurrence. Stable ORDENAL Hillshaded relief + topo elements

38 38 Satellite imagery + topo elements Geological map

39 39 Lahar hazard map Pyroclastic flow hazard map

40 40 3D visualisation (1) Landsat TM (false colour) satellite image draped over Digital Terrain Model (DTM)