Lecture 3b Lecture 4 Written by Austin Troy, Brian Voigt and Weiqi Zhou, University of Vermont © 2011

The Vector Data Model Multi-layer Vector Queries Spatial Join Geoprocessing

Lecture 3b 1. Vector Data Model

The Vector Data Model Three types of vector data Lecture 3b The Vector Data Model Three types of vector data Points Lines / Arcs Polygons A given layer holds a single feature type (e.g. “roads” is a line layer, “counties” is a polygon or line layer, “weather stations” is a point layer) Shapefile vs Feature Class

All lecture materials by Austin Troy © 2010except where noted Lecture 3b Point Features A collection of records with (x,y) coordinates 6 2 ID X,Y Coordinates 1 2,2 2 3,6 3 5,5 4 6,3 … 10 4,1 3,6 5 3 5,5 4 3 4 All lecture materials by Austin Troy © 2010except where noted 6,3 2 1 2,2 1 10 4,1 1 2 3 4 5 6 Image modified from ESRI Arc Info electronic help

Line Features Each point has a unique location Lecture 3b Line Features Each point has a unique location 2 points define a line segment One or several line segments define an arc The endpoints of an arc are “nodes The angle points are “vertices” (sing. Vertex) The feature is the arc, not the line Two arcs meet at the nodes

Line Features Points define lines (arcs) Arc Feature is the ARC, not the line segments Arcs meet at the nodes Line segment Vertex Node

Polygon Features Area of homogenous phenomena Lecture 3b Polygon Features Area of homogenous phenomena In a polygon layer, lines (arcs) define areas Closed region – first and last coordinate pairs are at the same location Line segments bound the polygon Computer “knows” that interior belongs to shape Lines (Arcs) Points

Rings A series of line segments (a string) that close upon each other Lecture 3b Rings A series of line segments (a string) that close upon each other NOT a polygon!!

Lecture 3b Vector Topology Definition1: Explicit encoding of spatial relationships between objects: the spatial location of each point, line and polygon is defined in relation to each other Definition2: Topology is a collection of rules and relationships that enables the geodatabase to more accurately model geometric relationships found in the world. Two major purposes: Allows for powerful spatial analysis Quality control

Vector Topology: Types Lecture 3b Vector Topology: Types Arc-node and node topology : the way that line features connect to point features Polygon topology: the way that neighboring polygons connect and share borders Route topology: the way that a line feature of one type (e.g. commuter rail line) shares segments with line features of another type (e.g. Amtrack rail line) Regions topology: the way that polygons overlap (e.g. GIS layers with a time component) or when spatially separate polygons are part of the same feature

Vector Topology: Quality Control Lecture 3b Vector Topology: Quality Control Ensuring data quality and “logical consistency” Defining complex and nuanced spatial rules. Single layer quality control: dangles overshoots polygons that don’t close adjacent polygons that do not share a border

Vector Topology: Quality Control Lecture 3b Vector Topology: Quality Control slivers overshoots dangles Not sharing border

Vector Topology: Quality Control Lecture 3b Vector Topology: Quality Control Mutli-Layer quality control: Defining spatial rules between layers Polygon rules: e.g. Must Not Overlap Line rules: e.g. Must Not Intersect Point rules: e.g. Must be Properly Inside Polygons Define and validate topology rules in ArcCatalog and ArcMap (http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#/Geodatabase_topology_rules_and_topology_error_fixes/001t000000sp000000/)

Topology Rules: Example Lecture 3b Topology Rules: Example Say we have the following layers: property lots, sidewalk, building footprints, zoning map We can specify topological rules, like: Lots must be enclosed polygons Buildings must be entirely within a lot Sidewalks must be outside a lot polygon Lots must fall entirely within a single zone Lots must either share a border with another lot or with city land, including streets and sidewalks. In a low-density zone, no more than 20 lots can be touching We can’t do this yet, but will be able to shortly

Vector Topology Table Polygon topology table Node topology table Lecture 3b Vector Topology Table Polygon topology table Lists arcs / links comprising polygon Node topology table Lists arcs / links that meet at each node Arc, or “link” topology table Lists the nodes on which each arc / link ends and polygons to right and left of each arc / link, based on start and finish nodes Table with real world coordinates for each point

Vector Topology Table A table of the polygon topology Lecture 3b Vector Topology Table A table of the polygon topology Graphical display of arcs, nodes, vertices and lines Topology table for the ARCs making up the polygons

Lecture 3b Spaghetti Data Model Non-topological data model that looks like a vector data set Collections of line segments and points with no connectivity or topology No relative relationships encoded in this model Each line segment is “unaware” of the other line segments

2. Multi-layer vector queries in ArcGIS Lecture 3b 2. Multi-layer vector queries in ArcGIS

Lecture 3b Selecting By Location Let’s say we want to gather information about houses in four sample neighborhoods to identify which houses are within fire hazard zones

Select By Location Selection Method Selection layer Lecture 3b Select By Location Selection Method Selection layer Selection overlay layer Selection rule

Select By Location Now with “sample houses” active, we click select by theme and tell it to choose features that intersect the features of fire hazard zone

Select By Location Those that overlay a hazard zone are selected Lecture 3b Select By Location Those that overlay a hazard zone are selected Selected Not selected

Select By Location …Zooming in to one of those neighborhoods Lecture 3b Select By Location …Zooming in to one of those neighborhoods

Lecture 3b Select By Location Now we run statistics on the Price attribute of the selected records. This tells us that 1955 houses overlay fire zones it also tells us that the mean price for these properties is $467,551!

Lecture 3b Select By Location If we invert the selection with the Switch Selection button, we see that on average houses outside the fire zones are worth less! Only $246,752

Select By Location: Distance Lecture 3b Select By Location: Distance Now, say we want to select features from layer A that are within a distance of features in layer B. In this case we’ll select houses in our sample neighborhoods that are within 1 mile of a Starbucks

Select By Location : Distance Lecture 3b Select By Location : Distance This time we use a different selection method Note how we can specify the distance for selection

Select By Location: Distance Lecture 3b Select By Location: Distance Results in the following selection

Select By Location :Distance Lecture 3b Select By Location :Distance Zooming into a neighborhood…

Select By Location :Distance Lecture 3b Select By Location :Distance Now if we run statistics on price again… Those within a mile of a Starbucks have a mean value of $504,972 Those not within a mile of a Starbucks have a mean value of $273,866! By the way, these are real data, I’m not making this up!!

Select By Location :Distance Lecture 3b Select By Location :Distance For that same selection we could get statistics on a different variable—here we’ll look at lot size Those within a mile of a Starbucks have a mean size of 8776 square feet Those not within a mile of a Starbucks have a mean lot size of 10,024 sq feet. Why might that be?

Select By Location :Distance Lecture 3b Select By Location :Distance You can also select features in a point layer that are within a specified distance to a linear feature in another layer. Here we’ll find houses in a neighborhood within a mile of a highway Note that these smaller roads are in a different layer

Select By Location :Distance Lecture 3b Select By Location :Distance Scenario: There’s going to be a parade on Valley Boulevard and the city needs to inform all those homeowners on or near the route. We want to select points located within a specified distance of a single feature within a layer? Here we’ll find all homes within 500 meters of Valley Blvd. First a simple query to identify Valley Boulevard

Select By Location :Distance Lecture 3b Select By Location :Distance Once that feature is selected we can use “Select by location” to identify the affected properties Notice that this time we check “Use selected features”

Select By Location :Distance Lecture 3b Select By Location :Distance We end up selecting only the houses within 500 m of Valley Blvd

Select By Location: Polygons Lecture 3b Select By Location: Polygons Generally polygons in one layer do not perfectly coincide with those in another. Using the default selection method (Intersect) the entire polygon is selected even if only a small part is coincident. However, there are many other methods we can choose from that will change the number of polygons selected.

Selecting By Location: Polygons Lecture 3b Selecting By Location: Polygons Example: let’s select any census tract that intersects a fire zone; here’s the pre-selection map

Select By Location: Polygons Lecture 3b Select By Location: Polygons Using the “intersect” selection method we get this

Select By Location: Polygons Lecture 3b Select By Location: Polygons Using “that are completely within” method, we return no selected features. With “have their center in” we get

Select By Location: Selected Records Lecture 3b Select By Location: Selected Records Intersect Completely within

What can be done with multi-layer selections? Lecture 3b What can be done with multi-layer selections? Once a selection has been done using “select by location” you can do all the same things you would do with a single-layer selection: Make a new layer from the selection Do statistics on it Make a new field in that layer Calculate or recalculate a field for a selection

3. Spatial Join —assigning attributes by location Lecture 3b 3. Spatial Join —assigning attributes by location

Lecture 3b Spatial Join Assigns attribute data from features in one layer to spatially coincident features in another polygon attributes to point features point attributes to point features point to line distances between two layers Simply adds attributes to the attribute table

Lecture 3b Spatial Join Right clicking on the “to” layer and selecting Joins and Relates >>> Join Specify what you want to join by location and choose which layer we are joining from

Lecture 3b Spatial Join In this case we are going to join tracts to the houses from our sample neighborhoods. Each house inherits all the attributes of the tract in which it falls. This is a great way to assign data from layer to another Note that this creates a new layer

Lecture 3b Spatial Join Now we can plot houses by any of the attributes that were in the tracts database. Here’s a plot of houses graduated by percent unemployment of the tract to which they belong

Spatial Join: Distance Lecture 3b Spatial Join: Distance We can also do spatial joins based on distance. Whenever we join a point or line layer to another point or line layer, for each feature in the TO layer it gives us the attributes of the nearest feature in the FROM layer PLUS the distance between those features in whatever map units we specify

Spatial Join:Distance Lecture 3b Spatial Join:Distance Suppose we want to assign the name of the nearest major road to our housing layer.

Spatial Join:Distance Lecture 3b Spatial Join:Distance “from” layer Two options numerical summary of the lines that intersect each point assign all attributes from the nearest line. We choose the latter

Spatial Join: Distance Lecture 3b Spatial Join: Distance Name of nearest highway is an attribute for each housing point; here I’m plotting out categorically (by road name) after completing the join All lecture materials by Austin Troy © 2010except where noted

Spatial Join: Distance Lecture 3b Spatial Join: Distance The Euclidean distance from each point to the nearest road feature is recorded in the attribute “Distance.” Here I’m plotting out distance to nearest major road. All lecture materials by Austin Troy © 2010except where noted

Spatial Join: Distance Lecture 3b Spatial Join: Distance We can also do a join to get the distance from a series of points in one layer to a series of points in another: here is distance of houses to nearest Starbucks All lecture materials by Austin Troy © 2010except where noted

Spatial Join: Polygons Lecture 3b Spatial Join: Polygons Intuitive when it comes to assigning attributes to points and lines What about polygons? Layer A Layer B

Spatial Join: Polygons Lecture 3b Spatial Join: Polygons Problem: A polygon in layer A may overlay several polygons in layer B. Whose attributes do you give it? Spatial join and summarize (e.g. by average) the values of all the polygons of layer B that overlap the polygons of layer A. Example: Say we have a census tract layer with all sorts of demographic info (e.g., population, race) and we have a zip code layer with no demographic info attached to it. Our client is doing a marketing study and needs to have a map showing median age and percent Hispanic by zip code. We have both these attributes in our tract data layer and need to “transfer” them to the zip code layer

Spatial Join: Polygons Lecture 3b Spatial Join: Polygons Unfortunately, the tract boundaries and zip code boundaries do not match up. Note that tracts are not nested within zip codes—they cut across

Spatial Join:Polygons Lecture 3b Spatial Join:Polygons To deal with this we join two polygon layers based on their spatial location and choose the “summarize” option (the first radio button). This allows us to choose a statistic by which to summarize the value of all the constituent tract polygons for each zip code polygon. In this case we’ll choose “average” as our statistics All lecture materials by Austin Troy © 2010except where noted

Spatial Join:Polygons Lecture 3b Spatial Join:Polygons This results in a new output zip code layer with the average of every census tract variable; here median age is plotted; ArcGIS calls it Avg_MEDAGE so that we know what statistic this is based on

Lecture 3b 4. Vector Geoprocessing

Purpose of Geoprocessing Lecture 3b Purpose of Geoprocessing Tools for breaking down the size of map features: Union, Intersect, Clip Tools for increasing the size of map features: dissolve and merge (indirectly) ArcInfo and ArcToolbox include various other geoprocessing overlay operations, such as Update and Dissolve Regions

Union Combines features of two themes Each theme is treated the same Lecture 3b Union Combines features of two themes Each theme is treated the same Goes to extent of largest theme Keeps all line work, creates new polygons Breaks down features into smaller minimum mapping units Can use selected features option too Keeps all attributes

Lecture 3b Tools: Union only accepts polygon features

Finding Geoprocessing Tools Lecture 3b Finding Geoprocessing Tools

Lecture 3b Intersect Yields polygons representing areas that are common to both layers Preserves line work within common extent Creates new, smaller polygons Preserves all attributes from both

Lecture 3b Union vs. Intersection Union is the entirety of two overlapping sets of features Intersection identifies the common area Continuous and exhaustive vs. “island” polygons have different ramifications for these tools Intersect: “1 AND 2” Layer 1 + Layer 2 Union: “1 OR 2” Layer 2 Layer 1 +

Union vs. Intersection: Example Lecture 3b Union vs. Intersection: Example Suppose we have deer wintering areas in one layer and conserved lands in another.

Union vs. Intersection: Example Lecture 3b Union vs. Intersection: Example Union gives us land that is EITHER conserved OR that is a deer wintering areas

Union vs. Intersection: Example Lecture 3b Union vs. Intersection: Example Intersect gives us land that is BOTH, and preserves all polygon boundaries within that common extent

Tools: Clip Input Features = Point, Line or Polygon Lecture 3b Tools: Clip Input Features = Point, Line or Polygon Clip Features = Polygon

Clipping highways for Merced Lecture 3b Clipping highways for Merced Note that the “Use selected features only” option was used

Lecture 3b Tools: Dissolve

Dissolve: Example Dissolve zip codes (small) into counties (large) Lecture 3b Dissolve: Example Dissolve zip codes (small) into counties (large)

Lecture 3b Dissolve: Example Choose the dissolve field: e.g. Dissolve based on the County field

All lecture materials by Austin Troy © 2010except where noted Lecture 3b Dissolve : Example Summarize the resulting field values. For instance, I could calculate the average area of the zip codes that intersect each county. All lecture materials by Austin Troy © 2010except where noted

Lecture 3b Dissolve : Example Now we have created a county map, and for each county we have an attribute that equals the sum of the populations of the constituent zip codes

Lecture 3b Merge Allows you to “join” two adjacent or non-adjacent layers into a single layer Like “tiling” Best when attributes match

Merge Often when you merge you will want to follow up by dissolving. Lecture 3b Merge Often when you merge you will want to follow up by dissolving. This is because artificial polygon boundaries were created at the borders by the act of splitting the data up into tiles Merging joins multiple tiles into a single layer and dissolving reunites polygons that were split up in the tiling process

Lecture 3b Tools: Buffering Buffering is when you draw a polygon around a feature (point, line or polygon); Here we’re buffering a stream

Lecture 3b Tools: Buffering Based on distance Based on attribute

Tools: Buffering Width of buffer can vary with an attribute value Lecture 3b Tools: Buffering Width of buffer can vary with an attribute value We can recalculate that attribute to make a meaningful buffer width Example from lab: a buffer based on traffic volume

Combining Multiple Geoprocessing Tools: Example Lecture 3b Combining Multiple Geoprocessing Tools: Example Suppose we create fixed buffers around deer wintering areas and water bodies, and a variable buffer around roads, based on traffic:

Combining Multiple Geoprocessing Tools: Example Lecture 3b Combining Multiple Geoprocessing Tools: Example Then we could, for instance, find areas that are near deer wintering areas and water bodies but far from traffic: First we would intersect the deer wintering and water body buffers, yielding areas that are near both. Next we union the result with the traffic buffer. Finally, query the attributes to determine which areas meet your criteria.

Combining Multiple Geoprocessing Tools: Example Lecture 3b Combining Multiple Geoprocessing Tools: Example The intersection of deer wintering buffers and water buffers is the area in the red

Combining Multiple Geoprocessing Tools: Example Lecture 3b Combining Multiple Geoprocessing Tools: Example The union of that intersection with the traffic buffer:

Combining Multiple Geoprocessing Tools: Example Lecture 3b Combining Multiple Geoprocessing Tools: Example Now we can query for polygons that were created from the intersection (met the two good criteria) and for areas that are not within a traffic buffer

Combining Multiple Geoprocessing Tools: Example Lecture 3b Combining Multiple Geoprocessing Tools: Example We can then create a layer from that—Note that we have created entirely new polygon boundaries and geometry by cutting and splicing these buffers together.

Combining Geoprocessing Tools Lecture 3b Combining Geoprocessing Tools Involve multiple tasks performed in sequence, such as those that clip, buffering, intersect, union, then select datasets. Build and run a “model” in Model Builder Step by step instructions Specify input, output, other parameters, order of operation Create and run a script

Lecture 3b Model Builder