Thematic Layers in a GIS Data Stack
GIS as Digital “Map Layers" All of the layers are referenced to the same coordinate system …a spatial referencing system Each layer represents a different geographic theme, phenomena, or feature
Data Integration Integrated GISc Database
Aerial Photography
Digital Elevation Models
Satellite Imagery
Cadastral Data
Digital Line Graphs
GPS Data
Link Chris Betz Christian Carl Chris McAfee Dale Legere Donna Black
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Social, Economic,Demographic, Health, and Environmental Data
Differential GPS
Modeling Geographic Reality with Digital Data
Conceptualizing Geographic Reality
We model reality using digital data ...but first we must choose how to conceptualize reality… As discrete phenomena... readily-distinguished entities on the Earth’s surface with distinct boundaries this is an object-based view of the world As a continuous surface... entities on the Earth’s surface with continuous variation and without distinct boundaries this is a surface or field-based view of the world Which is right? depends on the phenomenon being modeled... sometimes both are “right” sometime it is scale-dependent
Spatial Data: Location Position in two- or threedimensional space Attributes What is at that location? What are it’s characteristics
2-D location attributes 3-D location …traditionally represented as maps, but now as digital representations...
USGS Data – Digital Raster Graphics
Raster images of standard topograhpic maps
but georeferenced! have map collar
Pixel attributes…?
color value Æ data?
Data Development Stream
Vector Data Model
point: primary data object single x-y coordinate pair lines: formed by joining two or more points at least two x-y coordinate pairs nodes: points composing lines polygons: formed by joining together multiple lines at least three x-y coordinate pairs
USGS – Digital Line Graphs
1:24:000 scale reproduces 7.5-min quads: Boundaries (state, county, city, national parks) Hydrography Transportation (roads, streets, railroads) Transmission lines Elevation contours and spot elevation values Basic surface cover Human-made & cultural features Geodetic survey control points & markers
TIGER/Line Files
Nominal scale: 1:100,000
Enumeration units blocks, block groups, tracts/block numbering areas, counties, cities/MA, etc. multiple hierarchies
Voting districts Congressional redistricting Supporting geography roads/streets/highways basic hydrography point & area landmarks
Raster Data Model
cell: primary data object also called “pixel” -usually for image data represented by x-y coordinate and a cell size cells are regularly spaced to cover entire data area called a tessellation
Digital Elevation Models (DEM)
Raster-format elevation data
Elevation samples at regularly-spaced intervals Large scale: 1:24,000 7.5x7.5-minute units spatial resolution = 30x30meters Intermediate scale: 1:100,000 30x30-minute units Small scale: 1:250,000 1x1-degree units
Derived Properties: Slope Angle
slope angle: change in elevation per unit horizontal change i.e., how steep is the slope?, what is its gradient? units generally are degrees or percent
Derived Properties: slope aspect
slope aspect: orientation of the line of steepest slope i.e., what direction does the slope face units generally degrees from cardinal north
ASTER Satellite Data & DEMS Isla Isabela
ASTER Satellite Data & DEMS
Triangulated Irregular Network
Fine for changing spatial resolutions in a single image/graphic – a triangle of a facet.
Relational Database Structure
Typical Database Queries: Selects & Reselects
Link to Census Data
Census attribute data - Summary Tape File (STF) data files Link to Census geographic entities in TIGER/Line files using unique Census geography IDs Æ Lets us merge a tremendously rich souce of detailed socioeconomic data (Census) with a comprehensive geography for the entire country…
Orange County, NC block groups w/ median income data (darker green = higher income)
Distance to Hospitals, Triangle Region
Hospital Service Areas: Network Analysis
Spatial Buffers: Major Roads
Distances from Roads: An Example
Southeast North Carolina: County Outlines & 2002 Satellite Image
Overlay of County Outlines, Land Use, & Land Parcels (Cadastral)
Land Cadastral: Parcels & Attributes
New Hanover County: Parcel Land Values - Subset Area
New Hanover County 1998 (Gray) and 2006 (Red): Parcel Changes
Brunswick County: Parcels, Major Rivers & 1-Mile Buffer
GIS Overlay Analysis
Overlay analyses Operate on spatial entities from two or more maps to determine spatial overlap, combination, containment, intersection…etc. one of the most “fundamental” of GIS operations formalized in 1960s by landscape architects who used acetate map overlays now a basic part of the GIS toolbox Vector overlays combine point, line, and polygon features computationally complex Raster overlays cell-by-cell comparison, combination, or operation computationally less demanding
Address Geocoding
Matches addresses in data files that have grid references (the reference theme) to those addresses in data files that do not (event table).
Process -- first matches the street name in both the event table and the reference theme, and then computes the coordinates of the addresses (odd-even; left-right); distance of address from street intersection by proportion; coordinates of the address using the coordinates of the street intersection.
Raster Overlays - Logical Combination
Use Boolean logic to perform overlays create conditional statements to operate on input layers output layer is the true/false result of conditional evaluation Simple example - determine erosion potential: input layers: terrain slope angle vegetated/not vegetated If SLOPE > 5% and VEGETATION = NO then EROSION_POTENTIAL = TRUE
Raster Suitability Analysis
Siting a new landfill… desired site characteristics: low soil porosity, flat, not near residential areas
Proximity -- Raster
proximity - cells in raster data set assigned values based on distance to input features raster equivalent to vector buffering how to calculate distance? Euclidean distance “Manhattan” distance
Topology (Vector Data Model)
Topology: geometric relationships between spatial data objects
adjacency: two spatial data objects “next” to one another
containment: polygonal (area) spatial data object “surrounds” another data object (neighborhood)
connectivity: one line data object is “linked” to another Necessary! why? computers don’t “know” the spatial relationships we readily perceive by looking at a map we must explicitly describe these spatial relationships topology allows to ask “spatial” questions, e.g... What is next to X? What is near Y? What is the shortest route from A to B?
Vector Overlays
Basic idea: spatially combine/compare two data layers to: (a) generate new output data layer, or (b) assign attributes of one data layer to another most cases: one of the data layers will contain polygon entities Point-in-poly overlay Æ line-in-poly overlay Æ polypoly overlay increasing conceptual and computational complexity
Polygon-Polygon Vector Overlay
Overlay polygon layer (A) with polygon layer (B) result: what are the spatial poly combinations of A and B? » generate new data layer with combined polygons attributes from both poly layers are included in output How are polygons combined? (i.e. what geometric rules are used for combination?) UNION (Boolean OR) INTERSECTION (Boolean AND) IDENTITY Polygon overlay will generally result in a significant increase in the number of spatial entities in the output can result in output that is too complex too interpret
Point-in-Polygon Vector Overlay
Overlay point layer (A) with polygon layer (B)
in which B polys are A points spatially located? » assign polygon attributes from B to points in A
Example: comparing soil mineral content at sample borehole locations (points) with landuse (polys)...
Another example: address geocoding w/TIGER and Census data
geocode addresses to create point layer overlay Census enumeration unit polygons to assign Census attributes to points
Line-in-Polygon Vector Overlay
Overlay line layer (A) with polygon layer (B) in which B polys are A lines spatially located? » assign polygon attributes from B to lines in A
Example: assign landuse attributes (polys) to streams (lines)...
Vector to Raster (Rasterization) Simple (compared to vectorization) Affected by: output raster spatial resolution method used for determining cell values
Vector to Raster Raster spatial resolution finer resolution = better representation of the converted vector data coarser resolution = more information loss! Method used to determine cell values How do we know what is “in” each cell? We choose: cell center (centroid) majority weighting weighted values based on priority/importance
Vector to Raster - Cell Value
value at centroid assigned to the cell simple, but can over-represent small area values
Raster to Vector (Vectorization) Points & polys - relatively simple points: if cell=value, then a vector point is created at cell centroid with attribute=value polygons: polygon with attribute=value is created for all adjoining cells=value; poly boundary follows exterior of cells Lines - more complex must somehow determine: start/end/intersection points (nodes) for lines shape points along lines (vertices) topological relationships
Reclassification – Attributes & Polygon Dissolve
Global Positioning Systems (GPS) • Fully operational in 1994 • > 20 satellites, 98% operational • 6 Orbital Planes • 20,200 km orbit • ~ 12 hour orbital period • Each visible for ~ 5 hours
Validating Pasture in the Oriente
Pictures are worth a thousand words…
GOES Image of Hurricane Bonnie August 25, 1998
Different Spatial Resolutions
1-2m
30m
79m
1.1km
QuickBird, IKONOS
Landsat TM, ETM
Landsat MSS
AVHRR
Multispectral Composite
Near infrared (red gun), red (green gun), green (blue gun): “false color”
Multispectral Composite
Middle infrared (red gun), near infrared (green gun), green (blue gun): “false color”
March 6, 1993
April 23, 1993
March 16, 1993
August 28, 1993
Spatial Simulation Models: Cellular Automata & Agent Based Models
Goal: Generate LULC simulations based upon actual conditions observed through the satellite time-series and extended in time & space through derived growth rules and neighborhood interactions.
Approach: CA consists of a regular grid of cells, each of which can be in one of a finite number of K possible states, updated synchronously in discrete time steps according to a local, identical interaction rule. The state is determined by the previous states of a surrounding neighborhood of cells, and the rule is usually specified in the form of a transition function.
Cell Suitability
START
Landsat TM landcover Year = 1986
Compute cell suitability derived from static & dynamic GIS inputs
GIS inputs
Class growth: stochastic + diffusive Urban Resolve class competition
Flux classes
Separate classes
Agriculture Pasture For. Succession
Class transition probabilities (sat. time series)
based on suitabilities
Merge classes Year +1
No
Final model year? Yes
END
Modeled landcover
Population Density
DEM
Class Suitability
Income
Accessibility
Northern Ecuadorian Amazon - SISA: Income at the Farm Level
Northern Ecuadorian Amazon - SISA: Elevation and Hill Shading
Northern Ecuadorian Amazon - SISA: Slope Angle & Slope Aspect
Northern Ecuadorian Amazon - SISA: Elevation & Topographic Moisture Index
South ISA: CA Simulation 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 1996 1995 1994 1993 1992 1991 1990 1989 1988 1987
Forest Agriculture/Pasture Urban/Barren Water
