intro. to gis lecture 5 downloading and exploring datasets march 4 th , 2013
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
Intro. To GISLecture 5
Downloading and Exploring Datasets
March 4th, 2013
Reminders
• Please turn in last week’s homework
• Midterm review in 2 weeks (March 13th)
• Review Session: next Wed (March 6th)
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:799
750 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
Refle
ctan
ce (%
)
• 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
E (or signal)
• 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 areaSpectral bandpassEnergy
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 6th)• What is the difference between an
aerial photo and an orthophoto?
03_23_Figure
Very
sim
ilar
REVIEW: LiDAR• Light Detection and Ranging – laser
elevations!
Downloading Datasets
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– 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– 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
Data Sources
• International:– United Nations –
Food & Agriculture Organization– The Nature Conservancy– OpenStreetMap
• Geofabrik extracts: http://download.geofabrik.de/
• Metro areas: http://metro.teczno.com/
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, 10-11
• Lab this week: Selecting and Joining Data– Chapters 8, 9, and 10 in the lab book
• Please submit last week’s homework