Definition of Spatial Analysis Spatial analysis - The process of modeling, examining, and interpreting model results. Spatial analysis is useful for evaluating suitability and capability estimating and predicting interpreting and understanding Add new hyperlinks for the yellow words

Spatial Analysis - cont. There are four traditional types of spatial analysis: Topological overlay and contiguity analysis Surface analysis Linear analysis Raster analysis Retrieval/classification/measurement Overlay (arithmetic, various conversions) Neighborhood Connectivity Add new hyperlinks for the yellow words Read more abt contiguity and other analyses

Definition of Spatial Analysis Spatial data analysis involves the application of operations to coordinate and relate attribute data. Spatial analyses are applied to solve problems related to geographic decisions Identify high crime area Selection of a best location for a new business Extent of sage brush infestation in Idaho. Spread of a disease Etc…

Definition of Spatial Analysis - cont. Spatial operations could be applied sequentially An output could serve as input Sequence of spatial operations is important Bolstad, 2005

Definition of Spatial Analysis - cont. Bolstad, 2005 one input can have many outputs many inputs can have one output

Spatial Operations Local operations Neighborhood operations Global operations Use only data at one input location to determine value at corresponding output location : Use data from both an input location and nearby location to have output value : Use data value from entire input layer to have each output value Bolstad, 2005

GIS Analysis Functions Four broad categories

1. Retrieval, Classification, & Measurement Functions Selective Search Classification/Reclassification (Overlays, combine) Identifying a set of features as belonging to a group Defines patterns Measurement Distances, lengths, perimeters, areas Search/Retrieval Examples: "Locate parcel by address“ In View mode, from Theme Pulldown menu, Select by Theme: Select features of active themes that a) completely contain b) Intersect c) area within distance of the selected features of a) here you scroll through & select from the available themes in your project Selection distance a) type in the desired distance criteria Use Query Builder Find (one at a time)

Selection Selection operations Examples Involve identifying features based on several conditions or criteria The attributes or geometry of features are checked against the conditions or criteria You can write the selected features into new output data layer You can use the selection for other analysis Examples

Select: State = Arkansas States = entirely north of Arkansas States_area>84,000 sq. mi. States both entirely north of Arkansas and larger than 84,000 sq. mi. Bolstad, 2005

Functions of Spatial Analysis Conditional selection Set Algebra Less than (<) Greater than (>) Equal to (=) Not equal to (<>) Boolean Algebra Conditions OR, AND, and NOT Bolstad, 2005

Examples of Expression in Boolean Algebra Bolstad, 2005

Select by Location - cont. Selecting options That Meet That Overlap That Contains That are Contained by That are Entirely Contained By That are Spatially Equal That Touch Click on the white text box to show a list of themes and select the theme that contains the layer you want to query. You can change this later if the layer is not in the theme you select. For the example select Gazetteer Click on the white text box to show a list of layers within the theme and select the layer you want to query. For the example select Addresses If you want to display all records of your chosen layer within your chosen boundary accept the default, Display all layer records and if you want to filter the records of your chosen layer within your chosen boundary then click next to Query layer records This defines how the records you are looking for, the first record set, should interact with the second record set That Meet: Includes only features referenced by the first record set that meet features referenced in the second record set. That Overlap: Includes only features referenced by the first record set that overlap features referenced in the second record set. That Contains: Includes only features referenced by the first record set contain features referenced in the second record set. That are Contained by: Includes only features referenced by the first record set that are contained in the second record set. That Entirely Contains: Includes only features referenced by the first record set that completely contain the features referenced in the second record set. The boundaries of the features cannot touch in any way; everything must be interior. That are Entirely Contained By: Includes only features referenced by the first record set that are completely contained in the second record set. The boundaries of the features cannot touch in any way; everything must be interior. That are Spatially Equal: Includes only features referenced by the first record set that are spatially equal to features referenced in the second record set. That Touch: Includes only features referenced by the first record set that intersect features referenced in the second record set.

Examples of Selection by Location States adjacent to Missouri Bolstad, 2005

Examples of Selection by Location - cont. States containing a portion of Mississippi River or its tributaries are selected Bolstad, 2005

Classification Categorization of geographic objects based on a set of conditions Also known as reclassification or recoding Spatial data operation can be used along with selection operation Example: classify polygons based on size Bolstad, 2005

Classification - cont. Classification is an operation to create a new group of classes from an existing set of classes Classification is governed by a a table or array (decided by user before hand) Bolstad, 2005

Classification - example Classification of land use for obtaining your required information

Classification - cont. Binary classification You need to have two classes 0 and 1 True or false A and B Some other two level classifications Bolstad, 2005

Automatic Classification Good for many classes in one feature file (when it is practically not possible to manually classify into groups) Requires classification schemes (algorithms or mathematical formula) which will combine various classes into a single group Equal interval Defined interval Natural breaks (Jenks) Standard deviation

Classification Examples Quantile classification Bolstad, 2005

Retrieval: Selective Search addresses selected because they fall within circle The simplest spatial query can be performed on screen using the selection tools that are provided with the GIS software. For instance, you can draw a circle on screen and select all objects falling inside it. This example shows addresses that have been selected because they fall within the circle.

Reclassification (Vector) Dissolving to aggregate polygons Figure 4.4: Dissolving to aggregate polygons Another option occurs where a user wants to create aggregate polygons from a more detailed layer. An example of this might be where a user has a polygon layer where each polygon represents a farmer's field with attribute data that includes crop type. If the user is only interested in where particular crops are grown then many field boundaries represent redundant information that can be removed. This is done by what is called a dissolve operation whereby the boundaries of adjacent polygons with the same crop type are removed to form aggregate polygons. This is shown in Figure 4.4.

Reclassify by Area Size Source: http://www.iwr.msu.edu/edmodule/gis/reclssc.html (Institute of Water Research, Michigan State University) Size reclassification allows polygons to be selected based on their area. For linear features, overall length may be used as the selection criterion. Work with areas > 80 acres

Reclassify by Contiguity Source: http://www.iwr.msu.edu/edmodule/gis/reclssc.html (Institute of Water Research, Michigan State University) The contiguity function allows raster GISs to analyze all examples of a particular class (eg., all forest patches) or to operate on the individual examples of that class of data (eg., any particular forest stand). Contiguity reclassification identifies 'clumps' of raster cells having the same thematic value. Each new clump is assigned a unique value. Work with individual forest stands, rather than the class forest as a whole.

Work with elevations between 20 and 40 feet Reclassify values Work with elevations between 20 and 40 feet Change feet to meters

Buffer one of the most common spatial analysis tools specific distance representation around a feature  The distances can either be constant or can vary depending upon attribute values.  When features are close together, their buffers may overlap. The user can choose to preserve the overlaps or remove them. The buffer operation creates a new polygon data set

Examples of Buffer Bolstad, 2005

Examples of Buffer So, we really have the same data in both Arc/Info and Idrisi. The Arc/Info coverages are vector, while the Idrisi layers or images are raster. This slide shows the result of a buffer. In Arc/Info I buffered the roads by 200 units (meters). I used 200 meters as the buffer distance in Idrisi as well. [DISCUSS Z-values with Idrisi]

Vector Distance Operation: Buffers & Setbacks Buffer: A zone constructed outward from an isolated object (point or line) to a specific distance. Setback: A zone inside a POLYGON constructed by a fixed distance from the edge of the polygon; typically used to restrict building or activities too close to the edge of a property parcel. Diagram of simple buffers and a setback. NOTE: buffers go outward from lines or areas; setbacks run inside of areas (not lines). Image Source: Chrisman, Nicholas.(2002). 2nd Ed. Exploring Geographic Information Systems. p 154. fig. 6-1.

Buffer Creation: Illustrated Image Source: Chrisman, Nicholas.(2002). 2nd Ed. Exploring Geographic Information Systems. p 60. fig. 6-3.

2. Overlay Functions Arithmetic Logical Raster & Vector methods differ addition, subtraction, division, multiplication Logical find where specified conditions occur (and, or, >, <, etc.) Raster & Vector methods differ Vector good for sparse data sets Raster grid calculations easier Overlay (demo – addition)

Overlay Another common spatial analysis tool Allows the user to identify areas where features in two layers overlap.  A new data set is often created from these overlaps.  In a Union Overlay, all features are included in the new data set but the features that overlap represent a new feature.  In an Intersect Overlay, only the areas that overlap are contained in the new data set.

Overlay Example Analysis Tools select Overlay Intersect tool Union tool

Examples Bolstad, 2005

Overlay Example - cont. Vector overlay Bolstad, 2005

Overlay: Combining Attributes Select attributes of interest for a given location (Raster & vector methods do this differently, but the results are similar)

Vector based Overlay 3 main types of vector overlay point-in-polygon line-in-polygon polygon-on-polygon Vector based GIS Overlay In a vector-based system, overlay operations are much more complex than in a raster-based system. This is because the topological data is stored as points, lines and/or polygons. This requires relatively complex geometrical operations to derive the intersected polygons, and the necessary creation of new nodes (points) and arcs (lines), with their combined attribute values. In a vector-based system, topological map overlay operations allow the polygon features of one layer to be overlaid on the polygon, point, or line features of another layer. Depending on the objectives of the Overlay operation, different output features can result. Classification of Vector Overlay Operations Topological vector overlay operations can be classified via two methods: 1. Through the elements contained in the layers to be overlaid (i.e.. whether the layers contain point, line or polygon elements), or 2. By operation type (for example; the user wants to generate a layer comprising of the Union, Intersection, or some other Boolean operation of the two input layers). When classifying the vector overlay operation via method one, the element types each layer contains are considered. The following table identifies which overlay options exist for each possible combination of element types contained in the two input layers. Input layer element typesPointsLinesPolygonsPointsPoints CoincidePoint int LinePoint in PolygonLinesPoint in LineLine IntersectionLine in PolygonPolygonsPoint in PolygonLine in PolygonPolygon Overlay Complex databases such as GIS classify vector overlay operations via method two, where the particular overlay operation a user wishes to perform defines which element types may be contained in the two input layers.

point-in-polygon example Vector based overlay New Meteorological Station Attribute Table Point ID Land Use 1 Forest 2 Forest 3 Non-Forest point-in-polygon example

line-in-polygon example Vector based overlay line-in-polygon example

polygon-in-polygon example Vector based overlay polygon-in-polygon example

Raster Based Overlay: Simple Addition Ratings assigned to categories for a county Solid Waste Plan. Ratings for grid cells were added to obtain a composite ranking. It is possible to make direct calculation on the raster data layers. Image Source: Chrisman, Nicholas.(2002). 2nd Ed. Exploring Geographic Information Systems. p 144. fig. 5-12.

Raster Overlay: Boolean Combine Sources in this image have been processed using a centroid-to-cell rule. The output shows two queries, each using two of the input sources. Image Source: Chrisman, Nicholas.(2002). 2nd Ed. Exploring Geographic Information Systems. p 125. fig. 5-3.

Raster Overlay: Composite Combine Image Source: Chrisman, Nicholas.(2002). 2nd Ed. Exploring Geographic Information Systems. p 126. fig. 5-4.

Overlay Example - cont. Raster overlay

Vector Overlay: Composite Structure A composite topological structure is constructed by finding all intersections, then polygons are labeled with a unique identifier & linked to the source attribute tables. Process creates a single coverage with links to all the attributes. Attribute-based operations can produce results from the table and apply to the coverage. Image Source: Chrisman, Nicholas.(2002). 2nd Ed. Exploring Geographic Information Systems. p 127. fig. 5-5.

3. Neighborhood Functions Basic Functions Average, diversity, majority, minimum/maximum, and total Parameters to define: Target location(s) Specification of neighborhood Function to perform on neighborhood elements 3 parameters Target location(s) Specification of neighborhood Function to be performed on elements in neighborhood Basic Neighborhood Functions Average Diversity Majority Maximum, minimum Total

3. Neighborhood Function (cont) Search Operation most common neighborhood operation Example count the number of customers within 2 miles of the grocery store

3. Neighborhood Functions (cont) Point or Line in Polygon Operation Vector Model specialized search function Raster Model polygons one data layer points or lines in separate data layer Buffers (demo - point, line, polygon)

Neighborhood Functions: 4 x 4 Window Processing

Neighborhood Functions: 4X4/Annulus/Circular/Wage Neighborhood Processing

Neighborhood Functions: Wedge Neighborhood Processing

4. Connectivity Functions Used to accumulate values over an area being navigated Parameters to define: specification of way spatial elements are connected rules that specify allowed movement along interconnections a unit of measurement

4. Connectivity Functions (cont). Proximity Operation measure of the distance between features not restricted to distance; can be noise, time, pollution, etc. Parameters to define: target location unit of measure function to calculate proximity (distance/time/noise) area to be analyzed Proximity Measure of the distance between features Not always distance, can be time, noise, etc. Need: target locations unit of measure function to calculate proximity (time/distance) area to be analyzed

Example: Connectivity (Raster) Image Source: Chrisman, Nicholas.(2002). 2nd Ed. Exploring Geographic Information Systems. p161. fig. 6-4. Proximity Operation: Distance From Neighbor

Example: Connectivity (Vector) Proximity Operation: Road Buffer

4. Connectivity Functions (cont). Contiguity Operation spatial units are connected - defines “unbroken area” Contiguity measures: size of neighboring area(s) shortest/longest straight line distance across adjacent area(s) specific shape of neighboring area(s)

Combines adjacent units together when they share a common attribute Contiguity Functions Combines adjacent units together when they share a common attribute May allow areas to be broken by certain features (such as roads) Accomplished in raster by collecting pixels until value changes In vector, one simply removes arcs separating matching polygons (dissolve) Combines adjacent units together when they share a common attribute

4. Connectivity Functions (cont). Network Operations set of interconnected lines that represent a set of features through which resources flow Common network functions shortest path problem (route optimization) location-allocation modeling (resource allocation) traveling salesperson problem (route optimization) route tracing (prediction of network loading) 3 GIS Network Functions prediction of network loading - flood control route optimization - emergency routing resource allocation - metropolitan service zones - fire, police

Spread Function: Calculation of Distance Fig 7.32 The Calculation of Distance Using a Spread Function

Spread Function: Equidistant Travel Zones from Target (A) Fig 7.33 Travel Zones Defined Using a Spread Function. Equidistant travel zones in 1 km increments from the target (A) are indicated by the concentric rings. The shortest travel distance from A to B is shown by the dashed line.

Real time tracking, route-finding, best to respond Emergency Services Real time tracking, route-finding, best to respond Emergency services By using the GIS as a computerised map, controllers of police vehicles and ambulances can instantly call up a detailed map of the area around an incident. By tracking the vehicles in real time and using route-finding GIS functions, the controller can identify the best vehicle to attend and give directions for the fastest way to the incident. They can even store historical information and look for incident patterns and black spots.

4. Connectivity Functions (cont). Visibility Analysis Operations identification of areas of terrain that can be seen from a particular point on the surface Viewshed Operation uses digital elevation model data (DEMs) or..... digital terrain model data (DTMs) or...... triangulated irregular network data (TINs) DEM: A digital model of height (elevation or altitude) represented as regularly or irregularly spaced point height values. DTM: A digital model of a topographic surface using information on height, slope, aspect, breaks in slope and other topographic features. TIN: An irregular set of height observations in a vector data model. Lines connect these to produce an irregular mesh of triangles. The faces represent the terrain surface and the vertices represent the terrain features (elev points). Intervisability Viewshed Data Sets TIN TIN Tables

Connectivity Function Example: Viewshed Analysis predicting what sites can see each other uses: providers of cellular phones service can determine areas served by transmission facilities and then locate new sites to serve blind spots. fig 8-1: Cross sectional view of a line of sight as it intersects the surface. Near objects block objects that fall below the previous angle. Obstructions on the surface can be modeled by increasing the height of the surface. ------------------------- Intervisibility Functions This GIS function is typified by the phrase LINE OF SIGHT. It is a graphic depiction of the area that can be seen from the specified target areas. Areas visible from a scenic lookout, or the required overlap of microwave transmission towers can be mapped using this procedure. Intervisibility functions rely on digital elevation data to define the surrounding topography. Applications such as landscape layouts, military planning, and the obvious communication utilization are best serviced. The output of this function is somewhat unique in that it is often displayed in a SIDE VIEW format. The vertical field of view and maximum viable distance are the component parameters. Image Source: Chrisman, Nicholas.(2002). 2nd Ed. Exploring Geographic Information Systems. p 198. fig. 8-14.

Viewshed aka Intervisibility

Environmental Impact Analysis 3-D environmental impact analysis By building a 3-D model of a landscape it is possible to simulate the construction of a new feature which may have an impact on the natural beauty of an area. For example, planning a wind farm. By using accurate map data for the area, a realistic model can be created and viewed from all angles. This will help identify the location that the new wind farm will have the least impact upon. 3D landscape model impact on natural beauty

The 3rd Dimension: Height Analysis Contours Hill shading Spot height symbols Cliff & slope symbols Viewpoint symbols Source: http://www.ordnancesurvey.co.uk/gisfiles/section2/page14.html ©Crown copyright 2003

With Spatial Analysis Tools Use What You Can Do? Find suitable locations. Find the best path between locations. Perform distance and cost-of-travel analyses. Perform statistical analysis based on the local environment, small neighborhoods, or predetermined zones. Generate new data using simple image processing tools. Interpolate data values for a study area based on samples. Clean up a variety of data for further analysis or display.

3D height data changing water levels-danger areas Flood Risk Flood risk Using 3-D height data and map data for river features it is possible to build a computer model of changing water levels; this can be used for predicting flood patterns and identifying areas in danger. By combining this model with address data, the likelihood of individual properties being flooded can be assessed. This is not just of environmental concern but of great value to insurance companies. 3D height data changing water levels-danger areas

Derived Mapping: Data from images Source: http://www.ordnancesurvey.co.uk/gisfiles/section6/page4c.html ©Crown copyright 2003 Imagery – usually from aerial or satellite sensors – is widely used on GIS platforms as a backdrop to vector mapping. An image may contain an abundance of visual information that is not conveyed by the points, lines and polygons of a vector map. As far as GIS software is concerned, however, an image is a dumb background. A key research challenge is to derive vector objects from imagery. An image is a raster dataset: it is a grid of squares or pixels. Each pixel has a numerical value that may relate to colour, height or indeed virtually anything measurable. Numerical Values Color Representation

Derived Mapping: Data from images Source: http://www.ordnancesurvey.co.uk/gisfiles/section6/page4c.html ©Crown copyright 2003 Human interpretation is often used to derive data from imagery. An operator traces lines over the on-screen image in a technique known as heads-up digitising. This process remains very labour intensive, however, and significant efforts are being made to find ways to automate it. Aerial Imagery Digitized Buildings

Derived Mapping: Data from images Source: http://www.ordnancesurvey.co.uk/gisfiles/section6/page4c.html ©Crown copyright 2003 One approach is to look for abrupt changes or discontinuities in the image that will equate to a line feature in a map. This can be achieved using an edge detection algorithm that applies a mathematical function to each pixel and its immediate neighbours in turn. The result is an image of lines that can simply be converted into vectors. These lines are often very messy, however, and this method is best used where the discontinuity itself is distinct and separate. An alternative approach is to use software to look for similar clusters of pixels and thereby classify the image into distinct areas. Where successful, this will identify real objects such as buildings, fields and bodies of water within a classified image, which may then be converted into a vector map. Accurately and appropriately deriving vector data in this way is a complex activity at the forefront of research. The ability to automatically generate a map from an aerial photograph or satellite image is a holy grail of GIS because it would help make data far more inexpensive and up to date. Satellite Imagery Derived Area Map This is a goal: Not there yet!

Airport Noise Pollution noise complaints mapped by address location Airport-noise pollution Restrictions on the permissible levels of aircraft noise affect all busy airports. GIS can help monitor not only the noise itself but also complaints from nearby residents. The spread of sound from the airport can be mapped against the nearby built-up areas to identify how many houses are going to be affected by high noise levels. By logging the addresses of people who complain about noise, the airport can monitor the effectiveness of their noise control measures and whether or not the airlines are obeying guidelines.