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Gis basic
This document discusses the preparation and presentation of geospatial data in maps. It defines spatial data as data that identifies geographic locations on Earth, such as natural or constructed features. It describes the main types of spatial data as raster, vector, and real world data. Raster data represents features as a grid of cells, while vector data represents features as points, lines, and polygons. The document discusses techniques for collecting, classifying, and mapping different types of spatial data, including raster, vector, nominal, categorical, ordinal, and numerical data. It also covers topics like thematic mapping, data normalization, and chart maps.
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Gis basic
- 1. Preparation and Presentationof Geospatial Data in Maps AFM Tariqul Islam Scientific Officer, BARI,Gazipur E-mail: [email protected]
- 2. What is SpatialData? Spatial Data – it is the data or information that identifies the geographic location of features and boundaries on Earth , such as natural or constructed features, oceans , and more . Spatial data is usually stored as coordinate and topology, and is data that can be mapped.
- 3. Types of SPATIALDATA • RASTER • VECTOR • Real World Source: Defense Mapping School National Imagery and Mapping Agency
- 4. Raster and VectorData Models Vector Representation X-AXIS 500 400 300 200 100 600 500 400 300 200 100 Y-AXIS River House 600 Trees Trees B B B B B B B B G G BK B B B G G G G G Raster Representation 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Real World G G Source: Defense Mapping School National Imagery and Mapping Agency
- 5. Vector Data Vector dataprovide a way to represent real world features within the GIS environment. A vector feature has its shape represented using geometry. The geometry is made up of one or more interconnected vertices. A vertex describe a position in space using an x, y and optionally z axis. In the vector data model, features on the earth are represented as: • points • lines / routes • polygons / regions • TINs (triangulated irregular networks)
- 6. Vector Data This systemof recording features is based on the interaction between arcs and nodes, represented by points, lines and polygons. A point is a single node, a line is two nodes with an arc between them, and a polygon is a closed group of three or more arcs. With these three elements , it is possible to record most all necessary information. Points Lines Polygons
- 7. Vector Data Advantages :Data can be represented at its original resolution and form without generalization. Graphic output is usually more aesthetically pleasing (traditional cartographic representation); Since most data, e.g. hard copy maps, is in vector form no data conversion is required. Accurate geographic location of data is maintained. Disadvantages: The location of each vertex needs to be stored explicitly. For effective analysis, vector data must be converted into a topological structure. This is often processing intensive and usually requires extensive data cleaning. As well, topology is static, and any updating or editing of the vector data requires re-building of the topology.
- 8. Raster Data Raster Data– cell –based data such as aerial imagery and digital elevation models. Raster data is characterized by pixel values. Basically, a raster file is a giant table, where each pixel is assigned a specific value from 0 to 255. The meaning behind these values is specified by the user – they can represent elevations, temperature, hydrology and etc.
- 9. Raster Data Raster dataare good at: • representing continuous data (e.g., slope, elevation) • representing multiple feature types (e.g., points, lines, and polygons) as single feature types (cells) • rapid computations ("map algebra") in which raster layers are treated as elements in mathematical expressions • analysis of multi-layer or multivariate data (e.g., satellite image processing and analysis) • hogging disk space
- 10. Raster Data Advantages :The geographic location of each cell is implied by its position in the cell matrix. Accordingly, other than an origin point, e.g. bottom left corner, no geographic coordinates are stored. Due to the nature of the data storage technique data analysis is usually easy to program and quick to perform. The inherent nature of raster maps, e.g. one attribute maps, is ideally suited for mathematical modeling and quantitative analysis. Grid-cell systems are very compatible with raster-based output devices, e.g. electrostatic plotters, graphic terminals.
- 11. Raster Data Disadvantages: Thecell size determines the resolution at which the data is represented.; It is especially difficult to adequately represent linear features depending on the cell resolution. Accordingly, network linkages are difficult to establish. Processing of associated attribute data may be cumbersome if large amounts of data exists. Raster maps inherently reflect only one attribute or characteristic for an area. Most output maps from grid-cell systems do not conform to high-quality cartographic needs.
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- 13. What is GIS? • A method to visualize, manipulate, analyze, and display spatial data • “Smart Maps” linking a database to the map
- 16. GIS data formats(files) • Shapefiles • Coverages • TIN (e.g. elevation can be stored as TIN) – Triangulated Irregular Network • Grid (e.g. elevation can be stored as Grid) • Image (e.g. elevation can be stored as image) Vector data Raster data
- 17. Shape Files • Nontopological •Advantages no overhead to process topology • Disadvantages polygons are double digitized, no topologic data checking • At least 3 files .shp .shx .dbf
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- 19. Data Collection • Canbe most expensive GIS activity • Many diverse sources • Two broad types of collection – Data capture (direct collection) – Data transfer • Two broad capture methods – Primary (direct measurement) – Secondary (indirect derivation)
- 20. Data Collection Techniques Field/RasterObject/Vector Primary Digital remote sensing images GPS measurements including VGI Digital aerial photographs Survey measurements Secondary Scanned maps Topographic surveys DEMs from maps Toponymy data sets from atlases
- 21. Primary Data Capture •Capture specifically for GIS use • Raster – remote sensing – e.g., SPOT and IKONOS satellites and aerial photography, echosounding at sea – Passive and active sensors • Resolution is key consideration – Spatial – Spectral, Acoustic – Temporal
- 22. Vector Primary DataCapture • Surveying – Locations of objects determines by angle and distance measurements from known locations – Uses expensive field equipment and crews – Most accurate method for large scale, small areas • GPS – Collection of satellites used to fix actual locations on Earth’s surface – Differential GPS used to improve accuracy
- 23. Secondary Geographic DataCapture • Data collected for other purposes, then converted for use in GIS • Raster conversion – Scanning of maps, aerial photographs, documents, etc. – Important scanning parameters are spatial and spectral (bit depth) resolution
- 24. Map Types • Differentdemands require different types of maps – Dependent on the data being used. • Different maps can have many symbols, or only one symbol. – Depends on what you’re trying to show. • Maps might use – Nominal data- names or ID’s objects – Categorical data- separates data into groups or classes – Ordinal data- separates data based on quantitative rank – Numerical data- data based on numbers with a standard interval between them
- 25. A single symbolmap each pink shaded polygon is a state
- 26. Categorical Data points linespolygons
- 27. Nominal data • Dataidentified or named by some type of label – Can be text or number • Maps often have many objects, almost all of which have points, lines and polygons that are identified as some unique feature – Points may be a city or house – Lines may be rivers, faults, railroads, roads, etc. – Polygons may be parks, states, counties, countries, etc.
- 28. Ordinal data • Dataare grouped by rank according to some quantitative measure – Cities may be small medium or large – Students may earn A B C or D’s in class – Soils may have I, II, III, IV infiltration • The data must be represented by unique values maps and colors must show or portray an increasing sense of value
- 29. A geological mapis a Unique Values Map based on categorical data representing different formations, or other geological units
- 30. Numerical data • Numbersthat represent continuous phenomena that fall along a regularly spaced interval – Rainfall, elevations, populations, chemical concentrations, etc. • Equal changes in the interval involve equal changes in the thing being measured • Ratio vs Interval numerical data – Ratio measured with respect to some meaningful zero point • Ex.- Rainfall; if zero, then no rain has fallen • Can add, subtract, multiply and divide these data – Interval measured against no meaningful zero point • Ex.- Temperature in F or C scale; has a regular scale, but zero on the thermometer does not mean a total lack of temperature. • Any data that can have a negative value is Interval (e.g. elevation) • Interval can only support addition and subtraction
- 31. Symbols associated withnumerical data • Points and Lines typically arranged so that the bigger the numerical attribute number, the larger the point or the thicker the line – Graduated Symbols- Points and lines are divided into classes with a given range of values for each class and a symbol unique to that class • A classed map – Proportional Symbols- numeric value is proportional to the size of the symbol • Creates what is referred to as an unclassed map
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- 33. • Polygons-numeric dataare typically represented by colors – Can vary by hue, saturation or intensity – Changes in rainfall are commonly represented this way with each class a deeper shading of the color (intensity) for that shape Symbols associated with numerical data
- 34. Two varieties ofprecipitation maps using color intensity The top map uses a monochromatic intensity ramp to represent various increasing amounts of annual rainfall The bottom is a two toned color ramp of the same data, with yellow = dryer and green = wetter Graduated color maps or Choropleth maps
- 35. Normalized data • Somefeatures will have larger symbols due to larger attribute values associated with larger coverages or areas – Larger counties will often have more farmland or larger populations, but it will be spread out over larger areas. – Normalizing the population to area (people divided by square miles) keeps the symbols from being disproportionally larger and therefore seemingly more important
- 36. Dot density mapscan normalize the data by letting each dot represent 1 million people. the more dots, the more people in that state. Can be arranged in specific locations in the state too
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- 38. Classifying (grouping) data •Many methods for grouping numeric data – Depends what you want to show • Natural breaks (Jenks)- looks for gaps in data values • Equal interval-equal size for the intervals • Defined interval- range of values defined by user • Quantile- same number of features in each class – Class defined • Geometric interval- each class multiplied by a coefficient to create the next class • Standard deviation- the statistical deviation from normal of the data in any attribute field • Manual (arbitrary) breaks- self explanatory
- 40. Raster data • Twotypes of rasters – Thematic Raster and Image raster • Thematic- 2 categories – Discrete- coded values identify discrete regions of similar values • e.g., geology or land use – Continuous- values change continuously from one location to another • e.g., elevation or precipitation • Image- from satellites and photos – Pixels are given lightness/darkness values from 0-255 with 0 being black and 255 being white
- 41. Discrete raster • Bestusing Unique Values classification – Each value receives a color • Geology map example on next slide Continuous raster • Classified – Values divided into classes and classes are given colors • Elevation map example on next slide • Stretched – Values are scaled to one of 256 color shades • Elevation map c) on next slide
- 42. a) Thematic rasterdiscrete unique values- geology b) Thematic raster continuous classified values- elevation
- 43. c) Thematic rastercontinuous stretched elevation