Intro. To GIS Lecture 5 Downloading and Exploring Datasets March 4 th , 2013

1. Intro. To GISLecture 5Downloading and Exploring DatasetsMarch 4th, 2013

2. Reminders • Please turn in last week’s homework • Midterm review in 2 weeks (March 13th) • Review Session: next Wed (March 6th)

3. REVIEW:“Heads-up” digitizing • Also known as on-screen digitizing • Scanned maps or aerial photographs used to trace features and record locations • Paper maps require a large format scanner • Images must be georeferenced • Can still be very time-consuming

4. REVIEW: Georeferencing • When images with unknown coordinates are fed into GIS • 2D georeferencing: resize (rescale), rotate, and translate to fit • Control points • Transformations: • Polynomial • First order (affine) • spline • Historic Map

5. REVIEW: New Shapefile • Create New Shapefile • Point, polyline, polygon • Coordinate System • Empty attribute table

6. REVIEW: New Attributes • Before you start to edit, add fields: • Consider the information you need to store about the features you will be digitizing (i.e. type, name) • Name: no spaces, characters • Choose the correct field type • For text, edit length (max = 254)

7. REVIEW: The Editor Toolbar • Options greyed out depending on feature type • Tools for creating or modifying features • Use to open the attribute window • Allows you to edit attributes for selected feature • Attributes can also be input into table directly

8. REVIEW: Geocoding • Converting street addresses to XY coordinates Attribute Table Reference Layer (Indexed Network) Results

9. REVIEW: Applications • Mapping restaurants in downtown Boston • Mapping customers' addresses for your business/education • Mapping households with high power consumption (e.g. nstar)

10. 750 Meadowlark From: 700 To: 799 } Offset Meadowlark St. 725 Meadowlark REVIEW: Interpolation

11. REVIEW: Address Locator • Choose locator type • Specify street data • Choose the right fields • From address • To address • Prefix (i.e. East, West) • Street Name • Street Type (Rd./St./Ave.) • Suffix

12. REVIEW: Rematching • Fixing the unmatched addresses

13. REVIEW: Remote Sensing

14. 03_16_Figure REVIEW: Remote Sensing Platforms Unmanned Airborne Vehicles

15. REVIEW: Earth Observing (EO)/Infrared (IR) Remote Sensing Systems • Space borne • CORONA • IKONOS / Geoeye (high spatial res.) • Quickbird / WorldView (high spatial res.) • Landsat/ SPOT (medium spatial res.) • MODIS/VIIRS/AVHRR (low spatial res.) • Airborne (UAV) • AVIRIS • Predator • Global Hawk

16. REVIEW: Concept of Resolution • Spatial • Spectral • Temporal • Radiometric

17. REVIEW: Spectral Resolution • Electromagnetic Spectrum Pan band

18. REVIEW: Spectral Resolution • Electromagnetic Spectrum Reflectance (%)

19. REVIEW: Spectral Resolution • Panchromatic (one single band, e.g. CORONA, old aerial photographs, IKONOS/Quickbird Pan band) • Multispectral (several bands, e.g. Landsat, MODIS) • Hyperspectral (many bands, e.g. AVIRIS) Courtesy of Guam Coastal Atlas

20. REVIEW: Trade-off between Spatial and Spectral Resolution • In order to maintain a reasonable level of energy (or signal) reaching the camera (or imaging system), the relation between the pixel size (or pixel area) and spectral bandpass (channel width) must be considered: E (or signal) Spectral bandpass Energy Pixel area

21. REVIEW: Airborne Remote Sensing • Collected by cameras mounted on planes • Multiple passes over a short time period • Orthorectified once images are joined • Perspective view

22. OrthophotoVs. Aerial photo/remotely sensed photo • Bonus question: due on Wed (March 6th) • What is the difference between an aerial photo and an orthophoto?

23. 03_23_Figure Very similar

24. REVIEW: LiDAR • Light Detection and Ranging – laser elevations!

25. Downloading Datasets

26. Downloading Datasets • If somebody asked you to make a map, where would you go to find the data? • Data often available online in digital formats • GIS files may exist with the attributes you need • Do some research to find out who has your data

27. Downloading Datasets • Starting your web search… • Topic: environment, government, business, health • Geography: neighborhood, city, state, country, world • Time frame: one vs. many years; historical data? • Sources: • Government agencies (local, state, federal, int’l.) • Non-profit organizations • Private corporations?

28. Data Sources • Municipal GIS departments • Parcel boundaries, zoning, wards + precincts • Street centerlines, sidewalks, building footprints • Infrastructure • Water supply, sewers, storm drains • Electric, gas, broadband • Municipal facilities – police, fire, DPW, schools • Cities & towns may charge a fee for a copy of data

29. Data Sources • State Agencies • MassGIS is a repository for many agencies • Political boundaries, roads, other infrastructure • Hydrography, Wetlands, Open Space • Orthophotos, DEM, Shaded Relief • GIS data for some other states is much harder to find!

30. Data Sources • Federal Agencies • The National Atlas, U.S. Census TIGER • USGS: Nat’l. Hydro. Dataset, DEMs, Orthophotos • NASA: Earth Observing System Clearinghouse • NOAA: Coastal Data, Weather, Fisheries • NWS: National Wetlands Inventory • NRCS: Soil Data Mart (NATSGO, STATSGO, SSURGO) • FEMA: Floodplains & Disaster Locations

31. Data Sources • International: • United Nations – Food & Agriculture Organization • The Nature Conservancy • OpenStreetMap • Geofabrik extracts: http://download.geofabrik.de/ • Metro areas: http://metro.teczno.com/

32. Exploring the Data • Check the metadata • “Item Description” in ArcCatalog • Details about source, attributes, date, methods • Make a map, play with symbology and labels • Get a sense of the range of values for attributes • Figure out which attributes will be useful to you

33. Data Structures/Models in GIS • Vector • ??? • ??? • Raster • ??? • ???

34. Topology • How does the machine know about relative positions of various features like point, line polygon? • Through Topology

35. Vector Data and Topology • Topology • The arrangement for how point, line, and polygon features share geometry • Or knowledge about relative spatial positioning • Two types of vector models exist in a GIS • Geo-relational Vector Model • Arc Coverage (has topology) >>> format: binay • Shape files (no topology) >>>> format: *.shp, *.shx, *dbf, etc. • Object-based Vector Model • Includes classes and geodatabases >>> format: *.mdb

36. Topology • Concepts • Adjacency • Enclosure • Connectivity • Terms to be defined • Node • Arc • Polygon

37. OK…. • No matter what if we have topology or not we can ask questions from a GIS database (spatial or non-spatial) to do some quick analysis….

38. Query • A query is a “question” posed to a database (attribute data) • Examples: • Mouse click on a map symbol (e.g. road) may mean • What is the name of road pointed to by mouse cursor ? • Typing a keyword in a search engine (e.g. google, yahoo) means • Which documents on web contain given keywords? • SELECT ‘FROM Senator S’ WHERE S.gender = ‘F’ means • Which senators are female?

39. Non-spatial Data • Or Attributes Field (Attribute)--- It could be either numeric or text) Record The Shape Field/Object ID tells about the type of vector feature (point/polygon/line)… It is where the coordinates are also stored (you do not see them here)

40. Organizing Attribute Data • Flat Files • Hierarchical • Relational (databases) • Object-oriented (database)

41. Organizing Attribute Data • Flat Files • Spreadsheets (e.g. excel spreadsheet)

42. Organizing Attribute Data • Hierarchical

43. Organizing Attribute Data • Relational (What is commonly used in GIS) • Various tables (databases) are “linked” through unique identifiers

44. Query: Making Selections • Usually interested in some subset of the data • Selections can be made in two primary ways: • Select by Attribute – specify matching criteria • Select by Location – based on spatial proximity

45. Query: Select by Attributes • Or Structured Query Language (SQL) • Enter criteria for one or more fields • Numeric values =,<,>,<> • Nominal values = ‘text’ • Change criteria or narrow results based on additional criteria

46. Select by Attribute Tips • Be careful with case sensitivity and spaces • Use parentheses to carefully construct a query • Use “Boolean” Operators (AND, OR, NOT, LIKE) • AND means both criteria, OR means either • NOT allows you to exclude some criteria • LIKE lets you be more flexible, use wildcard characters (_ for one character, % for many) • Verify your expression to make sure it works

47. Spatial Query: Select by Location • Use vectors to select data from other vectors • Same selection methods as Select by Attribute • Choose Target & Source • Many options for the spatial selection method

48. Spatial Query: Select by Location • Spatial selection methods • Target intersects source • …within a distance of… • contain, completely contain • within, completely within • Clementini (not boundary) • are identical to; touch the boundary of; share a line; crossed by the outline of

49. Select by Location Tips • Make sure Target and Source are correct • Combine with Select by Attributes • Check “Use Selected Features” under Source • Option to apply a search distance when not using the “within a distance of” method

50. Joining and Relating Tables • Many datasets are available in tabular format • Excel (.xls, .xlsx), comma-spaced values (.csv), text • Tables can be imported to ArcMap and linked points, lines, or polygons using a common ID