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Gis basic

  1. Preparation and Presentation of Geospatial Data in Maps AFM Tariqul Islam Scientific Officer, BARI,Gazipur E-mail: afmtareq@gmail.com
  2. What is Spatial Data? 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 SPATIAL DATA • RASTER • VECTOR • Real World Source: Defense Mapping School National Imagery and Mapping Agency
  4. Raster and Vector Data 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 data provide 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 system of 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 data are 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: The cell 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.
  12. Visualization of Spatial Data
  13. What is GIS ? • A method to visualize, manipulate, analyze, and display spatial data • “Smart Maps” linking a database to the map
  14. 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
  15. 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
  16. Geospatial Data Preparation for Mapping
  17. Data Collection • Can be 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)
  18. Data Collection Techniques Field/Raster Object/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
  19. 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
  20. Vector Primary Data Capture • 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
  21. Secondary Geographic Data Capture • 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
  22. Map Types • Different demands 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
  23. A single symbol map each pink shaded polygon is a state
  24. Categorical Data points lines polygons
  25. Nominal data • Data identified 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.
  26. Ordinal data • Data are 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
  27. A geological map is a Unique Values Map based on categorical data representing different formations, or other geological units
  28. Numerical data • Numbers that 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
  29. Symbols associated with numerical 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
  30. Classed map Unclassed map
  31. • Polygons-numeric data are 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
  32. Two varieties of precipitation 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
  33. Normalized data • Some features 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
  34. Dot density maps can 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
  35. Chart maps
  36. 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
  37. Raster data • Two types 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
  38. Discrete raster • Best using 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
  39. a) Thematic raster discrete unique values- geology b) Thematic raster continuous classified values- elevation
  40. c) Thematic raster continuous stretched elevation