Spatial and Terrain Analysis PDF

Spatial and Terrain Analysis  Introduction The process of examining the locations, attributes, and relationships of features in spatial data through overlay and other analytical techniques in order to address a question or gain useful knowledge. Sometimes also known...

Spatial and Terrain Analysis  Introduction The process of examining the locations, attributes, and relationships of features in spatial data through overlay and other analytical techniques in order to address a question or gain useful knowledge. Sometimes also known as geoprocessing Spatial analysis extracts or creates new information from spatial data. They range from basic spatial analysis involving simple overlay to advanced analysis including network analyst or terrain analysis. Spatial analysis can be performed on both vector and raster data types. They can be perform on locational and attribute data The success of spatial analysis is dependent on a number of operators which could be mathematical, Boolean or relational. Relational Boolean operators operators Vector Analysis Arithmetic Relational Boolean operators operators operators Raster Analysis  Summary of Boolean Operations “And” = Feature selected must meet two or more criteria (attributes) e.g a farm to be selected must have a certain specified area & yield “Or” = Here you are selecting a feature with two or more attributes that meet certain criteria e.g a magisterial district with one name or another with a diff name. When the search operation meets one or both, the records are selected “Not” = Selects a feature with all other attributes except the one mentioned not to be included in the operation e.g select all mag distr but not Durban  Attribute Queries Attribute data quarries are performed when we desire to extract or search for certain characteristics of a feature layer This is particularly useful when dealing with a large database which would take too much time if we attempted to select those characteristics one-by-one In ArcMap the functionality for querying attributes is found on the software menu where the option “select by attribute” is found This spatial query type differs from others in that it is perform only on one feature layer e.g querying the attributes of a country which includes its population, surface area, provinces, districts, etc. The next two slides are examples of how attribute query can be used to identify African countries with surface areas less than 300 000 sqkm  Spatial Analysis: Spatial Queries These are queries related to the spatial location of features and their relationships with one another. For example one can query: – Features that are located at a certain distance from another – Features that intersect others – Features that are completely within the boundaries of other features In the ArcMap environment, these spatial queries can be perform using the geoprocessing tool called “select by location” found on the ArcPro software menu. Unlike attribute queries that involve one feature layer, spatial queries involves two feature layers. However, both queries result in the selection of features on the map view and in their attribute tables. Eight Spatial Queries in ArcMap Are Completely within Complete Contain Have their Center In Contain the Center Of Intersect Are Within Distance of Touch the boundary of That have a line segment with  Overlay Analysis: Overlays Definition: The Vertical piling/stacking of themes or layers of features in a GIS workspace. “Putting one theme or layer on another” or merging database There are generally two main types – Vector overlays – Raster overlays Vector overlays can be performed in different ways: – Point on line: rare – Point on point: very rare, very theoretical – Point on polygon: quite common – Line on line: intersections of two networks – Line on polygon: common – Polygon on polygon: common but problematic Point on point & Point on line overlays rarely result in intersections & are rarely applied © 2011 demap  Merging ✓ When this happen, attributes are merged  Intersect Intersect merges only the parts that share common space, where the two themes overlap. In other words, it integrates two spatial data sets extracting and preserving only those features falling within the spatial extent common to both themes.e.g geology & slope  Union This operation joints two themes (layers) so you can visualize their full extent not just the common areas. e.g combinations of slope and soil type represent high risk for erosion You have to decide up-front whether to intersect or union  Clip This operation allows you to cut and extract a portion of a feature using the outline of another feature.e.g cliping out an area affected by industrial solution or a portion of a river crossing a region It allows you select features spatially rather than from their attribute tables  Raster Overlay In raster overlays or merge, mathematical operations: +, -, * and / Mathematical operations also called _____ For this to happen, cells have to be coded to allow operations between corresponding cells of the same code There are ambiguities when cell extent for both layers do not correspond – resolved by resampling before analysis  Raster Overlay Illustrated + = Above is an example of two raster overlays by addition. As exemplified : 1st row, Input 1 + Input 2 = Output, hence: ✓ 3+11 = 14 ✓ 3+12 = 15 ✓ 1+10 = 11 2nd row..........,etc 3rd row..........,etc  Merging ✓ When this happen, attributes are merged  Map Algebra (Multiplication) OVERLAY BY MULTIPLICATION DISTRICT CROP AREA OVERLAY 1 2 X = 1 2 3 4 1 3 4 B B OVERLAY BY MAXIMUM VALUE 3 3 4 4 2 2 4 3 4 0 1 0 5 5 5 5 5 5 + = 2 4 6 4 1 1 4 4 6 RAINFALL : RAINFALL: RAINFALL: 1980 1981 1980 - 1981  Importance of data overlays Visualization of patterns, distributions & trends Necessitates ✓ Merging (could be made permanent) – see previous notes. ✓ Clipping ✓ Intersecting ✓ Union of features Makes it possible to perform site suitability analysis Makes it possible to analyze impacts Facilitates spatial decision making  Proximity Analysis: Buffering Create polygons that surround other points, lines, or polygons To identify areas surrounding geographic features Identify / select features that then fall within / outside the boundary of the buffer Provide summary measures of proximity  You can buffer points, lines and polygons  Proximity Queries These involve: – querying distances from A-B – shortest route between A & B – The quickest route from A-B – nearest or closest feature – features found within a certain radius (3 km). Advantage: Determines nearest hospital, how close a school is to a polluting industry, which customers are found within a certain distance, distances to cover within a service area, etc  Other forms of Spatial Analysis Dissolve: Use the Dissolve process when you want to remove boundaries or nodes between adjacent polygons or lines that have the same values for a specified attribute Recoding and Classification Residential Commercial Taro Rice  Joins Sometimes a particular dataset may not be enough to perform a certain form of analysis. This may necessitate additional information from another feature layer or more descriptive data (attributes) from an existing table Joins by themselves are not strictly a form of data analysis but they enhance spatial analysis yielding a better result. Generally there are two kinds of joins, namely a tabular join and a spatial join. A tabular join, joins an attribute table (non-spatial) from an external source to the attribute of a GIS layer. Alternatively, the join may be between 2 attribute tables of two GIS layers. The one table is added to the GIS layer, no new shapefile is created. To be successful, the two tables must have a common field (example on the next slide).  Attribute table Mapped Attribute table (external source) Common field Additional data needed Notice that the output table contain data from both input tables  Network Analysis This type of analysis is performed on features represented by a set of connected line segments, for example road, rail or drainage (river) network Works better with vector data model which have lines and nodes For successful operations, lines must connect Includes shortest route b/w two or more features (cities, ports), quickest route to a feature, nearest facility (clinic, police station), Service area (territory served by a service provider, producer, etc). Terrain / Surface Mapping and Analysis Surfaces represent phenomena that have values at every point across an area These may be elevation surface, temperature values for a temperature surface, pollution surface, etc In terrain analysis, surfaces are related to the z coordinate (x = longitude, y= latitude and z = elevation or height above sea-level) These surfaces can be represented using points, contours, TINs and rasters Between these measured (z) locations, values are assigned to the surface where there is no value by interpolation  Defining Interpolation Is the process of using points with known values to estimate values at other unknown points. Through interpolation, we can estimate, for example, rainfall value at a location with no recorded data Spatial interpolation requires two basic inputs: – Control point – An interpolation method 31  From Tobler’s First Law of Geography to Interpolation “Everything is related to everything else, but near things are more related than distant things” Waldo Tobler (1969) Similarly, the justification behind spatial interpolation is the assumption that points closer together in space are more likely to have similar values than points more distant 32 33  Types of Interpolation Two main types: – Global interpolation – use all the available data; – Local interpolation – only use data in vicinity of the point being estimated. Can also be classified as: – Exact – predicts the same value as the know value at control point; – Inexact – predicts a different value from the know value at the control point. Thirdly, may be: – Deterministic – provide no assessment of possible errors with predicted values – Stochastic – provide assessment of prediction errors with estimated values  Application of Interpolation Spatial interpolation may be used in GISs: – To provide contours for displaying data graphically – To calculate some property of the surface at a given point – Frequently is used as an aid in the spatial decision making process both in physical & human geography Spatial interpolation is typically applied to a raster with estimates made for cells 35  Spatial Sampling Before we can interpolate we need some data values We ideally want as many sample points as possible, as widely spread as possible. The area or volume sampled at each point is known as the support There are various ways in which sample points might be selected  Point /Areal Interpolation Given a number of points whose locations and values are known, the value of other points at fixed locations is determined Point interpolation is used for data which can be collected at point locations (e.g. weather station) Point to point interpolation is the most frequently performed type of spatial interpolation done in GIS 37  Interpolation methods There are many interpolation methods. For the purpose of this course, I will present two commonly used interpolation methods 38  Thiessen Polygons Local, exact, deterministic Thiessen polygons are constructed around the known data points All points within the same polygon are closer to that data point than to any other To construct a Thiessen polygon: – Join sample points by lines forming sides of a triangle. – Draw lines at right angles at the mid point of each side of the triangles. These lines define the polygons 39  Inverse Distance Weighted (IDW) Local, exact and deterministic This is one of the simplest and most readily available methods It is based on an assumption that the value at an un- sampled point can be approximated as a weighted average of values at points within a certain cut-off distance The sample points are weighted during interpolation such that the influence of one point relative to another declines with distance from the unknown point you want to create 40 Inverse Distance Weighted (IDW) 41  Kriging Local, exact and stochastic It differs from other local interpolation methods because it assess the quality of prediction with estimated prediction errors Kriging identifies the optimal interpolation weights and search radius Regarded by many as the best method 42 Triangulated Irregular Networks (TIN) TIN tries to create a surface formed by triangles of nearest neighbor points 43 Image Source: Mitas & Mitasova (1999)  Types of surfaces 1. Digital Elevation Models (DEM) From previous slides it can be observed that surfaces can be created from vectors (points or contours) For example, from contour lines or spot heights, DEMs (Digital Elevation Models) are created DEM is a raster format (grids) of elevation, including a z-value for height DEMs as a form of terrain analysis offers better visualization than contour maps for land use planning and decision making. Types of surfaces  Types of surfaces 2. Triangulated Irregular Networks (TIN) A form of 3-D representation is TIN TIN: Triangulated irregular networks, a network of triangles connecting points Each created triangle represents a terrain surface assumed to be of uniform elevation, slope or aspect Major visual difference between DEM and TIN 3-D representations is that DEM is in rectangular or squared grids Also raster DEM is regular as opposed to irregular TIN DEM is a raster model while TIN is a topologic model DEM and TIN are referred to as DTMs (Digital Terrain Models) and are useful for terrain analysis and the creation of surfaces.  Types of surfaces TIN Structure TIN Surface  Types of surfaces 3. Slope and Aspect Some surfaces are derived from raster or existing surfaces. This is the case with slope, aspect, hillshade, viewsheds TIN is very efficient in the calculation of slope and aspect Slope determines steepness & critical for resource managers (erosion & landslides assessment,) and land use planners (residential, roads, mountain sports, etc) Aspect is the direction to which the slope faces (N,E,S,W) Aspect is important in agricultural and risk management practices (fire) Both can help in predicting direction of downhill flows (hazards) These analyses can only be conducted in GIS with the availability of elevation data (contours, DEM, TIN) Slope can be measured in percentage or in degrees. In degrees, values range between 0 and 90 degrees, where 0 indicates no slope. Aspect is also measured in degrees. North is 0 degrees, east is 90 degrees, south is 180 degrees, and west is 270 degrees. Slope Illustrated Aspect illustrated  Types of surfaces 4. Shaded relief & Viewshed A shaded relief (Hillshade) means part of the topography is illuminated and the other is in shade. This happens when light is shun to a 3-D relief from a specified angle Viewshed identifies the areas which can be seen from one or more observation points or lines Displaying a hillshade underneath your elevation and the output from the Viewshed function gives a very realistic impression of the landscape and clearly indicates what locations an observer can see from the observation point They help in the location of overhead power/telecommunication lines, landfill sites, some kinds of strategic sites, etc In ArcMap, you have to activate 3-D Analyst in extensions from the tools menu in order to perform these analysis  Types of surfaces Combining Elevation & Hillsade Hillshade Elevation overlaid on Hillshade Viewshed Viewshed displayed under hillshade  Visibility Analysis The availability of altitude, slope and/or aspect can also assist in performing: ✓ Visibility Analysis - line of sight from a particular object or feature and a visibility profile ✓ Soil Erosion Modeling and creating "what-if" type models, used for hazard assessment and prediction.  Visibility Analysis Line of Sight Visibility Profile