1. Review: Exam I GEOG 370 Instructor: Christine Erlien

2. Learning Goals: Ch 1 To be able to define GIS and describe the components necessary to working with GIS To be able to describe the reasons (and advances) that allowed for the development of the first GIS systems To be able to define CAC and CAD and compare their capabilities with those of GIS

3. Learning Goals: Ch 1 To be able to describe the steps of the cartographic process and the differences between traditional cartography and GIS To be able to describe the GIS subsystems and how they differ from traditional cartographic map production

4. GIS: Geographic Information Systems GIS is built on collective knowledge –Geography –Cartography –Computer science –Mathematics Many definitions, depending on whom you ask –Demers (our textbook) cites Marble & Pequet (1983), who talk about what we do with a GIS and how we do it

5. Data: Both spatial and temporal Spatial: Related to the space around us Temporal: Related to time The what and how of GIS: Data input subsystem: Collecting & preprocessing data Data storage & retrieval subsystem: Retrieval, updating, editing Data manipulation & analysis subsystem: Analysis & modeling Reporting subsystem: Display What this boils down to: “GIS is an information system that allows for capture, storage, retrieval, analysis and display of spatial data.” Marble and Pequet (1983)

6. Computer hardware Software –Data management and analysis procedures Spatial data People needed to operate the GIS Components necessary for “Doing GIS”

7. The rise of GIS Canada, early 1960s, Dr. Roger Tomlinson Need: inventory & map natural resources A huge task, aided by advances in computing technology –Computers: vacuum tubes  transistors Faster, more reliable, cheaper Larger memories  information storage as well as calculations possible –Mainframe used had 512K of memory!!!! IBM develops the drum scanner to scan lines on maps –1 st in the world Interested in more history? See http://www.casa.ucl.ac.uk/gistimeline/ http://www.casa.ucl.ac.uk/gistimeline/ for an interactive timeline

8. How does GIS differ from CAC and CAD? Computer-aided cartography (CAC): –Primarily used in map-making (display) Computer-aided drafting (CAD) –Used by architects to produce graphic images (display) –Images not linked to descriptive files What key capability of GIS is lacking in both CAC and CAD?

9. Cartographic Process Cartographic process: The steps in producing a map, beginning with data collection & resulting in a map product. –Data Collection –Data Processing Aggregation, classing, etc. –Map Production

10. Comparing traditional cartography & GIS: Inputs Traditional Data sources –Aerial photography –Digital remote sensing –Survey –Census & statistical data Data recorded as points, lines, areas on paper or Mylar GIS Data sources –Same, plus –DLGsDLGs –DEMsDEMs –Digital orthophotoquadsDigital orthophotoquads Data recorded as points, lines, areas using electronic devices

11. Comparing traditional cartography & GIS: Storage & Retrieval Traditional Storage: points, lines, areas drawn on map Retrieval: Map reading GIS Storage: –Points, lines, areas stored with spatial reference data (coordinates) & pointers –Tables of characteristics (attributes) associated with coordinates Retrieval: Computer tracks where data are stored

12. Comparing traditional cartography & GIS: Analysis & Output Traditional Analysis: Limited to data as presented on map Output: Mapping GIS Analysis: Allows access to raw data  can change aggregation or classification, or analyse further Output: May include maps, tables, charts

13. Learning Goals: Ch. 2 To be able to explain how real world objects may be generalized in the digital environment, and how their representation may change based on the scale of observation To be able to explain the difference between and identify examples of discrete and continuous data To be able to identify differences between nominal, ordinal, interval, and ratio scales. To also be able to discuss factors that may determine which spatial measurement levels we use.

14. Learning Goals: Ch. 2 To understand the necessity for a grid system for determining locations as well as the meaning of absolute versus relative location To be able to describe spatial patterns and relationships using terms such as random, regular, clustered, orientation, arrangement, diffusion, density, and spatial association To be able to explain how data collection may differ for small versus large areas and discuss the different the use of ground sampling methods for data collection

15. Generalizing Real World Objects Point –Location only Line –1-D: length –Made up of a connected sequence of points Polygon –2-D: length & width –Enclosed area Surface –3-D: length, width, height –Incorporates elevation data

17. Generalizing Spatial Objects Representing an object as point? line? polygon? –Depends on Scale (large scale vs. small scale) Data Purpose of your research –Example: House Point (small scale mapping) Polygon 3D object (modeling a city block)

18. Data: Continuous vs. discrete Continuous –Data values distributed across a surface w/out interruption –Examples: elevation, temperature, LULCelevationtemperatureLULC Discrete –Occurs at a given point in space; at a given spot, the feature is present or not –Examples Points: Town, power pole Lines: Highway, stream Areas: U.S. Counties, national parks

20. http://weather.unisys.com/surface/sst.gif

21. LULC http://landcover.usgs.gov

22. http://maps.unc.edu/MapBook/Index.asp

23. Continuous & discrete? Some data types may be presented as either discrete or continuous –Example Population at a point (discrete) Population density surface for an area (continuous)

24. http://www.citypopulation.de/World.html Population: Discrete

25. http://sedac.ciesin.columbia.edu/gpw/ Population: Continuous

26. Generalities Continuous data –Raster Discrete data –Vector

27. Spatial Measurement Levels Three levels of spatial measurement: Nominal scale Ordinal level Interval/ratio

28. Measurement Levels & Mathematical Comparisons Nominal scale –Not possible Ordinal scale –Compare in terms of greater than, less than, equal to Interval/ratio scales –Mathematical operations Interval: addition, subtraction Ratio: add, subtract, multiply, divide

29. From ESRI Map Book Volume 18, ESRI (2003)

30. A B C From Mapping Census 2000, Brewer & Suchan (2001)

31. From ESRI Map Book Volume 18, ESRI (2003)

32. Spatial Location and Reference Communicating the location of objects Absolute location –Definitive, measurable, fixed point in space –Requires a reference system (e.g., grid system such as Latitude/Longitude) Relative location –Location determined relative to other objects in geographic space Giving directions UTM

33. Spatial Location and Reference: Geographic Coordinate System (lat/long) Lines of latitude are called parallels Lines of longitude are called meridians

34. Latitude / Longitude Prime Meridian & Equator are the reference points used to define latitude and longitude

36. Spatial Comparisons Pattern analysis: An important way to understand spatial relationships between objects. Three point distribution patterns: –Regular: Uniform –Clustered –Random: No apparent organization

38. A B C

39. Describing Spatial Patterns Proximity: Nearness Orientation: Azimuthal direction (N,S,E,W) relating the spatial arrangement of objects Diffusion: Objects move from one area to another through time Density: # of inhabitants, dwellings, etc., per unit area

40. Collecting Geographic Data Small areas –Ground survey –Census Large areas –Census (less often  every 10 years) –Remote sensing –GPS (e.g., collared animals)

41. Collecting Geographic Data: Sampling & Sampling Schemes Sampling: When a census isn’t practical Types of sampling –Directed: Particular study areas selected based on experience, accessibility, etc. –Probability-based: For the total population of interest, each element has a known probability of being selected

42. Sampling & Sampling Schemes Probabilistic sampling methods –Random: Each feature has same probability of selection –Systematic: Repeated pattern guides sample selection –Homogeneous: Similar characteristics throughout the study area –Stratified: Characteristics vary throughout study area (subdivisions internally homogeneous) Features sampled w/in subdivisions

43. Probabilistic sampling methods