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

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

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

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

REVIEW: New Shapefile Create New Shapefile – Point, polyline, polygon – Coordinate System – Empty attribute table

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)

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

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

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

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

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

REVIEW: Rematching Fixing the unmatched addresses

REVIEW: Remote Sensing

03_16_Figure REVIEW: Remote Sensing Platforms Unmanned Airborne Vehicles

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

REVIEW: Concept of Resolution Spatial Spectral Temporal Radiometric

REVIEW: Spectral Resolution Electromagnetic Spectrum Pan band

REVIEW: Spectral Resolution Reflectance (%) Electromagnetic Spectrum

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

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: Pixel area Spectral bandpass Energy

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

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

03_23_Figure Very similar

REVIEW: LiDAR Light Detection and Ranging – laser elevations!

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

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?

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

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

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

Data Sources International: – United Nations – Food & Agriculture OrganizationFood & Agriculture Organization – The Nature Conservancy The Nature Conservancy – OpenStreetMap Geofabrik extracts: Metro areas:

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

Data Structures/Models in GIS Vector – ??? Raster – ???

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

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

Topology Concepts – Adjacency – Enclosure – Connectivity Terms to be defined – Node – Arc – Polygon

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….

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?

Non-spatial Data Or Attributes Record Field (Attribute)--- It could be either numeric or text) 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)

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

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

Hierarchical Organizing Attribute Data

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

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

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

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

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

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

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

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

Joining Tables Tables downloaded as text or CSV may need to be opened and saved as Excel files first First row of table should contain short headers with no special characters (or spaces, ideally) Table must have an ID that matches geography

One-to-one relationships A one-to-one relationship means that each record in one table has only one matching record in another table

Many-to-one relationships Many-to-one means multiple records in the table match to one record in another table

Joining Tables Usually you will choose to “Keep All Records” Always Validate Join – Maybe a mismatched ID – Sometimes missing records in the join table Joined fields will display in the target layer table

Relating Tables Relates are used when tables have a one-to- many or many-to-many relationship Attributes are not appended to the table, but selecting a record in one table will select all related records in another table

More on Joins and Relates Join field must be same format (number / text) To remove a join or relate, right-click the target layer again and choose the join or relate to remove, or Remove All Joins/Relates To preserve the attributes joined to a layer, you should export it to a new file

Homework & Lab Read Appendix and Ch. 5 – Q’s 3,4,6, 8, Lab this week: Selecting and Joining Data – Chapters 8, 9, and 10 in the lab book Please submit last week’s homework