Spatial analysis and modeling

• Spatial is relating to the position, area, shape and size of things. • Spatial describes how objects fit together in space, on earth. • Data are facts and statistics collected together for reference or analysis. • Spatial data are data that are connected to a place in the Earth. • Spatial data are data/information about the location and shape of, and relationships among, geographic features, usually stored as coordinates and topology 1 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

• Principally, there are three spatial data components that need to be stored for GIS data:  geometric data,  thematic data, and  a link identification (ID) for the geometric and the thematic component. 2 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

 Spatial data link between the geometric component (which deals with the location of the data by means, for example, of a reference coordinate system) and the thematic component (it provides the attribute values of the data, e.g. names, and other identifiers (IDs) of the data).  Object or feature needs to be geometrically and thematically described Spatial Analysis and Modelling by Tadele Feyssa, Wollega 3 University

Spatial data • Components of Spatial Data 4 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

• All GIS software has been designed to handle spatial data. • Spatial data are characterized by information about position connections with other features and details of non-spatial characteristics 5 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

SPATIAL DATA SPATIAL NON-SPATIAL Wolega University ADDRESS NAME Block 41 Block 32 MAP DATABASE Spatial Analysis and Modelling by Tadele Feyssa, Wollega 6 University

SPATIAL DATA CRITERIA: • X-Y Coordinate System • Shape • Area/Size • Perimeter • Distance • Neighborhood Spatial Analysis and Modelling by Tadele Feyssa, Wollega 7 University

WHAT ARE ELEMENTS OF SPATIAL DATA? Building Topography Land use Utility Soil Type Roads District Land Parcels Spatial Analysis and Modelling by Tadele Feyssa, Wollega 8 University Nature of Geography Objects

ATTRIBUTES: • Explains about spatial data • Relevant non-spatial data • Words or Numbers • Qualitative methods • Quantitative methods Spatial Analysis and Modelling by Tadele Feyssa, Wollega 9 University

NATURE OF SPATIAL DATA (GEOGRAPHIC OBJECTS) • spatial component – relative position between objects – coordinate system • attribute component – explains spatial objects characteristics • spatial relationship – relationship between objects • time component – temporal element 10 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

11 Analysis Analysis is the process of inferring meaning from data. Analysis is carried visually in a GIS  Analysis in a GIS can also be carried out by measurements, statistical computations, fitting models to data values other operation Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

12 Spatial analysis concept Spatial analysis is the process by which we turn raw data into useful information Spatial analysis is the crux of GIS because it includes all of the transformations, manipulations, and methods that can be applied to geographic data to add value to them, to support decisions, and to reveal patterns and anomalies that are not immediately obvious Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

 In a narrow sense, spatial analysis has been described as a method for analyzing spatial data, while in a broad sense it includes revealing and clarifying processes, structures, etc., of spatial phenomena that occur on the Earth’s surface. Ultimately, it is designed to support spatial decision-making, and to serve as a tool for assisting with regional planning and the formulation of government policies, among other things. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 13 University

Spatial Analysis Spatial Analysis and Modelling by Tadele Feyssa, Wollega 14 University

The data analysis domain of a GIS, includes a variety of data processing functions that aim at deriving spatial relationships, patterns and trends, that are implicit in the source data. The results of data analysis may be used immediately for spatial problem solving and decision making or as input for further spatial analysis and modeling. Spatial data analysis is computing from existing, stored spatial data new information that provides new insight Spatial Analysis and Modelling by Tadele Feyssa, Wollega 15 University

Spatial analysis could be either for prescriptive or predictive applications. Prescriptive model:  Used for planning & site selection. This involve the use of criteria & parameters to quantify environmental, economic & social factors. The model enumerates a number of conditions to be met. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 16 University

Predictive model:  A forecast is made of the likelihood of future events. Various spatial data layers used (raster or vector). Analytical questions, such as why or what if.  It is intended to construct models and perform predictions. E.g. Pollution, erosion, landslides. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 17 University

Application Road construction Construction in mountainous areas is complex engineering task, Cost factors, such as the number of tunnels & bridges to be constructed,  Volume of rock & soil to be removed. GIS can help to compute such costs on the basis of an up-to-date DEM and soil map. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 18 University

Modeling Models are used in many different ways, from simulations of how the world works, to evaluations of planning scenarios, to the creation of indicators of suitability or vulnerability Model is a simplification of reality in be viewed as a model. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 19 University

Modeling  In the field of GIS, modelling provide understanding of the way the world works with sufficient precision and accuracy to allow prediction and confident decision-making. Modeling concern the way in which analyses are carried out using standard functionality Spatial Analysis and Modelling by Tadele Feyssa, Wollega 20 University

2 GIS Analysis Functions This chapter is organized to take you from data to information and ultimately to decision-making. It covers some of the options in GIS for data analysis. Data analysis is the most interesting part of a GIS project. B/c it is where one can start to find answers to some of his/her questions, and use GIS to help develop new questions for research. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 21 University

GIS Analysis Functions … Analysis that is undertake with GIS may lead to new information that will inform decision making. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 22 University

Once the data input process is complete and your GIS layers are preprocessed, you can begin the analysis stage. GIS analysis functions use the spatial and non-spatial attribute data to answer questions about real-world Analyzing geographic data requires critical thinking and reasoning. When use GIS to address real-world problems, you will come up against the question that which analysis function you want to use and to solveS ptathial Aenaly spis anrd oModbellinlge by Tmadeles Feyssa, Wollega 23 University

You look for patterns, associations, connections, interactions, and evidence of change through time and over space. GIS helps you analyze the data sets and test for spatial relationships, but it does not replace the necessity for you to think spatially. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 24 University

GIS Analysis Functions… What makes GIS unique is the ability to: link data to spatial locations and query and summarize these data based on specific analysis requirements. Functionally, GIS provides a sophisticated tool for reporting the results of a database. These reports may be for an entire dataset (or table) or for a portion of the dataset (e.g., based on the results of a query or data summary) Spatial Analysis and Modelling by Tadele Feyssa, Wollega 25 University

Analysis FUNCTIONS of GIS Includes: Measurements Query Extraction Proximity Classification Topology  Network analysis … Spatial Analysis and Modelling by Tadele Feyssa, Wollega 26 University

MEASUREMENT Measurements are simple numerical values that describe aspects of geographic data.  Measurement functions in GIS includes Distance,  Perimeter and Area Spatial Analysis and Modelling by Tadele Feyssa, Wollega 27 University

 Many types of interrogation ask for measurements  we might want to know the total area of a parcel of land, or the  distance between two points, or the length of a stretch of road and in principle all of these measurements are obtainable by simple calculations inside GIS. Comparable measurements by hand from maps can be very tedious and error-prone. 28 Spatial Analysis and Modelling by Tadele Feyssa, Wollega Tadele F Spatial Analysis University and Modelling

Measuring Distance There are many ways to measure distance.  Most GIS programs have a ruler button that allows you to measure distances across a map. After clicking the button, you point on the map where you want to begin your distance measurement and then click at the ending point (or intervening points that define the path you want to measure). Spatial Analysis and Modelling by Tadele Feyssa, Wollega 29 University

Many vector-based systems measure distances along existing vector line networks, like streets, sewers, and railroads Example: shortest distance from Wollega University to Nekemte bas station This type of distance measurement relies on topological network relationships Spatial Analysis and Modelling by Tadele Feyssa, Wollega 30 University

Measuring Area/Perimeter Many vector systems automatically generate area and perimeter measurements for polygon features and store these values in prescribed attribute fields.  The systems that do not have this automatic function do provide a way for you to generate area and perimeter and store the results in user-defined fields.  Once calculated and stored, you can select multiple polygon features and sum their area and perimeter Spatial Analysis and Modelling by Tadele Feyssa, Wollega 31 University

MEASUREMENT DISTANCE Perimeter AREA/SIZE X Y A B C D 5 KM A- B = 20 = 40% B- C = 20 = 40% C - D= 10 = 20% 2 Spatial Analysis and Modelling by Tadele Feyssa, Wolleg1a 0 km 32 University

QUERY Queries are the most basic of analysis operations, in which the GIS is used to answer simple questions posed by the user.  No changes occur in the database, and no new data are produced with these type of selection Spatial Analysis and Modelling by Tadele Feyssa, Wollega 33 University

Cont… The operations vary from simple and well-defined queries like ‘how many houses are found within 1 km of this point’, to vaguer questions like ‘which is the closest city to Los Angeles going north’, where the response may depend on the system’s ability to understand what the user means by ‘going north’ Spatial Analysis and Modelling by Tadele Feyssa, Wollega 34 University

Attribute Query (Boolean Selection)  It involves picking features based on query expressions, which use Boolean algebra (and, or, not), set algebra (>, <, =, >=, <=), arithmetic operators (=, -, *, /), and user-defined values. Simply put, the GIS compares the values in an attribute field with a query expression that you define Spatial Analysis and Modelling by Tadele Feyssa, Wollega 35 University

Cont…  For example, if you want to select every restaurant whose price is considered inexpensive, you would use a query expression like “PRICE = $” where “PRICE” is the attribute field under investigation, “=” is the set algebra operator, and “$” is the value Spatial Analysis and Modelling by Tadele Feyssa, Wollega 36 University

Spatial Selection (Spatial Searches/query) While attribute queries select features by sorting through records in a data file, spatial selection chooses features from the map interface.  In most cases, it selects features from one layer that fall within or touch an edge of polygon features in a second layer (or an interactively drawn graphic polygon). Spatial Analysis and Modelling by Tadele Feyssa, Wollega 37 University

Cont…. There are many types of spatial selection like point in polygon, it is a spatial operation in which points from one feature dataset are overlaid on the polygons of another to determine which points are contained within the polygons. 38 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University Tadele F Spatial Analysis and Modelling

Cont…. There are many types of spatial selection like point in polygon, it is a spatial operation in which points from one feature dataset are overlaid on the polygons of another to determine which points are contained within the polygons. 39 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University Tadele F Spatial Analysis and Modelling

Extracting portions of data helps to isolate specific areas for further processing or data analysis. Similar to queries and selection sets, extraction functions can reduce the size of datasets and/or facilitate more complex interpretation. Queries and selection sets also allow to isolate portions of a dataset Extraction techniques differ in that these portions of data are isolated in a permanent way - through the creation of new data layers Spatial Analysis and Modelling by Tadele Feyssa, Wollega 40 University

Cont… GIS software packages provide a suite of tools to extract data, the most useful being, clip,select, and split Extracts input features that overlay the clip features. Working much like a cookie-cutter. This is particularly useful for creating a new feature class— also referred to as study area or area of interest (AOI)—that contains a geographic subset of the features in another, larger feature class Spatial Analysis and Modelling by Tadele Feyssa, Wollega 41 University

Cont… Use this tool to cut out a piece of one feature class using one or more of the features in another feature class as a cookie cutter 42 Clip is useful for developing a subset of features from a series of existing data layers to match a common boundary. Eg Addis Ababa city planner might wish to look at a street network layer, but only those streets falling within a Addis Ababa city boundary. Assume that the street network includes Finfine special zone. Clipping would be useful in order to permanently extract the street features matching the Spatial Analysis extent and Modelling by Tadele Feyssa, Wollega University of the city boundary.

Split is used to divide an input layer into two or more independent layers:  based on geographically corresponding features in a split layer Input output layer The Split Features daStpaatiasl Aenatly sims andu Mosdetlli ngb bye Ta dpele oFeylsysa,g Woollegna s. 43 University

Extracts features from an input feature class or input feature layer, typically using a select or Structured Query Language (SQL) expression and stores them in an output feature class. Eg. Urban planner might wish to look at only double-line streets in the particular municipality of interest. In this case, he or she would execute a selection query to extract only those desired features to a new layer 44 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

CLASSIFICATION Classification is the procedure of identifying a set of features as belonging to a group and defining patterns.  Some form of classification function is provided in every GIS. Classification is important because it defines patterns.  One of the important functions of a GIS is to assist in recognizing new patterns. Classification is done using single data layers, as well as with multiple data laSpaytiale Anralyssis anad Msode llipng bay Tardetle Foeysfsa , Waollnega overlay operation. 45 University

When you perform a classification, you group similar features into classes by assigning the same symbol to each member of the class. Aggregating features into classes allows you to spot patterns in the data more easily. The definition of a class range determines which features fall into that class and affect the appearance of the map. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 46 University

By altering the class breaks (the boundary between classes), you can create very different-looking maps. Classes can be created manually, or you can use a standard classification scheme. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 47 University

TOPOLOGY  In geodatabases, topology is the arrangement that defines how point, line, and polygon features share coincident geometry. For example, street centerlines and census blocks share common geometry, and adjacent soil polygons share their common boundaries. Topology is the science and mathematics of relationships used to validate the geometry of vector entities, and for operations such as network tracinSpagtial Aanalynsis dand Mtodeellinsg bty Tsad eloe Fefys sap, Woollelgay gon adjacency 48 University

Topological or topology-based data are useful for detecting and correcting digitizing errors (e.g. two lines in a roads vector layer that do not meet perfectly at an intersection).  Topological errors violate the topological relationships that are either required by a GIS package or defined by the user Topology is necessary for carrying out some types of spatial analysis, such as network analysis. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 49 University

Topological Errors There are different types of topological errors and they can be grouped according to whether the vector feature types are polygons or polylines. Topological errors with polygon features can include unclosed polygons, gaps between polygon borders or overlapping polygon borders. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 50 University

Topological Errors A common topological error with polyline features is that they do not meet perfectly at a point (node). This type of error is called an undershoot if a gap exists between the lines, and an overshoot if a line extend beyond the line The result of overshoot and undershoot errors are so-called 'dangling nodes' at the end of the lines. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 51 University

Dangling nodes are acceptable in special cases, for example if they are attached to dead-end streets Arc-node data model Arc: a series of points that start and end at a node Node: an intersection point where two or more arcs meet Nodes that are close together are snapped. Slivers due to double digitizing and overlay are eliminated. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 52 University

Topological errors Undershoots (1) occur when digitized vector lines that should connect to each other don't quite touch. Overshoots (2) happen if a line ends beyond the line it should connect to. Slivers (3) occur when the vertices of two polygons do not match up on their borders. 53 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

Slivers Sliver A common error in overlaying polygon layers is Silvers Silvers are very small polygons along correlated or shared boundary lines Spatial Analysis and Modelling by Tadele Feyssa, Wollega 54 University

Unsnapped node Spatial Analysis and Modelling by Tadele Feyssa, Wollega 55 University

OVERLAY ANALYSIS Overlay is one of the most common and powerful GIS functions.  It investigates the spatial association of features by “vertically stacking” feature layers to investigate geographic patterns and determine locations that meet specific criteria. An overlay operation combines the geometries and attributes of two feature layers to create the output Feature layers to be overlaid must be spatially registered and based on the same cSopatioal Arnadlysiis nanda Modteelling bsy Taydelse Fetyessa,m Wollesga 56 University

Spatial Analysis and Modelling by Tadele Feyssa, Wollega 57 University

Feature type and overlay There are two group of overlay operations The first group uses two polygon layers as input The second group uses one polygon layer and other layer which may contain points or lines Spatial Analysis and Modelling by Tadele Feyssa, Wollega 58 University

Overlay operation can be classified as: Point-in-polygon overlay Line-in-polygon overlay Polygon-on-polygon overlay Spatial Analysis and Modelling by Tadele Feyssa, Wollega 59 University

Point-in-polygon overlay Line-in-polygon overlay Spatial Analysis and Modelling by Tadele Feyssa, Wollega 60 University

Polygon-on-polygon overlay Spatial Analysis and Modelling by Tadele Feyssa, Wollega 61 University

Overlay Methods Overlay methods are based on the Boolean connectors AND, OR and XOR  Intersect uses the AND connector Union uses the OR connector. Differences uses XOR connector Union preserves all features from the inputs The area extent of the output combines the area extents of both input layers Spatial Analysis and Modelling by Tadele Feyssa, Wollega 62 University

AND Spatial Analysis and Modelling by Tadele Feyssa, Wollega 63 University

Union requires that both input layers be polygon layers  Intersect preserves only those features that fall within the area extent common to the inputs The input layers may contain different feature types although in most cases one of them is a point, line or polygon Spatial Analysis and Modelling by Tadele Feyssa, Wollega 64 University

OR Spatial Analysis and Modelling by Tadele Feyssa, Wollega 65 University

XOR Symmetrical difference preserves features that fall within the area extent that is common only to one of the inputs.  In other words symmetrical difference is opposite to intersect in terms of the outputs area extent Symmetrical difference requires polygons for both inputs Spatial Analysis and Modelling by Tadele Feyssa, Wollega 66 University

Identity preserves only features that fall within the area extent of the layer defined as the input layer the other layer is call the identity of layer The input layer may contain points, lines or polygon and the identity layer is a polygon layer Spatial Analysis and Modelling by Tadele Feyssa, Wollega 67 University

Application of Overlay An overlay operation combines features and attributes from the input layers The overlay output is useful for query and modelling purposes. For example a company who is looking a parcel that is zoned a commercial area, not subject for flooding and not more than a mile from heavy duty road may use overlay method to identify the area Spatial Analysis and Modelling by Tadele Feyssa, Wollega 68 University

NEIGHBORHOOD FUNCTIONS Neighborhood operations consider the characteristics of neighboring areas around a specific location. The principle here is to find out the characteristics of the vicinity, here called neighborhood, of a location. After all, many suitability questions, for instance, depend not only on what is at the location, but also on what is near the location.  Thus, the GIS must allow us ‘to look around locally’. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 69 University

To perform neighbourhood analysis, we must  1. state which target locations are of interest to us, and what is their spatial extent, 2. define how to determine the neighbourhood for each target,  3. define which characteristic(s) must be computed for each neighbourhood.  These functions either modify existing features or create new feature layers, which are influenced, to some degree, by the distance from existing features Spatial Analysis and Modelling by Tadele Feyssa, Wollega 70 University

For example, our target is Nekemte Hospital. Its neighborhood can be defined as • an area within 2 km distance, as the crow flies, or • an area within 2 km travel distance, or • all roads within 500 m travel distance, or • all other clinics within 15 minutes travel time, or • all residential areas, for which the clinic is the closest clinic Spatial Analysis and Modelling by Tadele Feyssa, Wollega 71 University

Proximity computation  In proximity computations, we use geometric distance to define the neighborhood of one or more target locations. All GIS programs provide some neighborhood analyses, which include buffering, interpolation, Theissen polygons, and various topographic functions.  The most common and useful technique is buffer zone generation. Another technique based on geometric distance that is Thiessen polygon gSpeatianl Anealysris aandt Mioodellinng b.y Tadele Feyssa, Wollega 72 University

Buffering Buffering works based on proximity concept Feature for buffering may be points, lines or polygons Buffering around point create a circle Around lines a series of elongated buffer zones around each line segment A buffer around a polygon creates an extended area from the polygon boundaries Spatial Analysis and Modelling by Tadele Feyssa, Wollega 73 University

Buffering around point Buffering Around lines Buffering around a polygon  Buffering uses distance measurements from selected features  We must know the measurement unit of features we are dealing with Spatial Analysis and Modelling by Tadele Feyssa, Wollega 74 University

Application of Buffering Buffering creates a buffer zone data set A buffer zone often treated as a protection zone and is used for planning and regulatory purposes A city may require a buffer zone of 500m for alcohol trading from school A 30m buffer zone along river bank may needed to protect a river Spatial Analysis and Modelling by Tadele Feyssa, Wollega 75 University

A buffer zone may be treated as a neutral zone and as a tool for conflict resolution Buffering zone also used for identifying suitable sites for different purposes Buffering also can be applied for sampling methods. EG A stream network can be buffered at regular distances to analyse vegetation variations as one moves away from the stream Spatial Analysis and Modelling by Tadele Feyssa, Wollega 76 University

Thiessen Polygons (Analysis) It Creates Thiessen polygons from point input features. Each Thiessen polygon contains only a single point input feature. Any location within a Thiessen polygon is closer to its associated point than to any other point input feature. 77 This tool is used to divide the area covered by the point input features into Thiessen or proximal zones. These zones represent full areas where any location within the zone is closer to its associated input point than to any other Spatial Analysis and Modelling by Tadele Feyssa, Wollega input point University

NETWORK ANALYSIS Network is any system of interconnected linear features A network is a system of interconnected elements, such as edges (lines) and connecting junctions (points), that represent possible routes from one location to another Spatial Analysis and Modelling by Tadele Feyssa, Wollega 78 University

What is network analysis? Solving problems involving networks Its goal is efficiency – Saving time and money. Tools like • Network data (connectivity is needed) • Network analysis software – A GIS is also required to network analysis Spatial Analysis and Modelling by Tadele Feyssa, Wollega 79 University

Network Analysis Network analyses involve analyzing the flow of networks— a connected set of lines and point nodes. These linear networks most often represent features such as rivers, transportation corridors (roads, railroads, and even flight paths), and utilities (electric, telephone, television, sewer, water, gas). Point nodes usually represent pickup or destination sites, clients, transformers, valves, and intersections. People, water, consumer packages, kilowatts, and many other resources flow to and from nodes along linear features. Each linear feature affects the resource flow. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 80 University

 For example, a street segment might only provide flow in one direction (a one-way street) and at a certain speed.  Network analysis tools help you analyze the “cost” of moving through the network.  “cost” can be money, time, distance, or effort. The three major types of network analyses include route selection (optimal path or shortest path), resource allocation, and network modeling. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 81 University

• Route Selection attempts to identify the least “cost” route. • You might want to find the shortest path between your home and a weekend destination • In any route selection routine, two or more nodes, including an origin and a destination point, must be identified and be able to be visited on the network. • Sometimes there are a large number of possible routes. • It is the job of the network analysis algorithm to determine the least cost routeS.patial Analysis and Modelling by Tadele Feyssa, Wollega 82 University

• • Resource Allocation, the second major type of network analysis, involves the distribution of a network to nodes. • To do this, you define one or more allocation nodes on the network. • Territories of linear features, like streets, are defined around each of these allocation nodes. • The linear features are usually assigned to the nearest node, where distance is measured in time, length, money, or effort. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 83 University

What do the tools do? GIS allows you to solve common network problems, such as finding the best route across a city, finding the closest emergency vehicle or facility, identifying a service area around a location, servicing a set of orders with a fleet of vehicles, or choosing the best facilities to open or close

What do the tools do? Direct path analysis – eg finding the shortest path between your office and home Optimum routing - helping a pizza deliveryman visit numerous houses in the most time – efficient manner, that include length of the lines, their capacity, maximum travel rate and time Closest facility analysis – eg finding the closest hospital to an automobile accident Drive time analysis- Helping a store to determine how many customers are within 5 driving miles Driving directions- the systems of computation also allow deriving directions

Route Closest Facility Service Area ArcGIS Network Analyst Extension Solving transportation problems Vehicle Routing Problem Location-Allocation Origin-Destination Cost Matrix

Route  Network Analyst can find the best way to get from one location to another or to visit several locations What's the best route?  Whether finding a simple route between two locations or one that visits several locations, people usually try to take the best route. But "best route" can mean different things in different situations.  The best route can be the quickest, shortest, or most scenic route, depending on the impedance chosen. If the impedance is time, then the best route is the quickest route. Hence, the best route can be defined as the route that has the lowest impedance, where the impedance is chosen by the user. 87 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

eg Routing from Wollega University to Nekemte Bus Station Shortest route analysis by considering different origins and destinations 88 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

Closest facility The closest facility solver finds the cost of travelling between incidents (i.e. specified points/ locations) and facilities and determines which are nearest to the other Finding the closest hospital to an accident, the closest police cars to a crime scene, and the closest store to a customer's address are all examples of closest facility problems Spatial Analysis and Modelling by Tadele Feyssa, Wollega 89 University

The closest facility problem to search for 5 schools within a 10 minute drive from Nekemte First Square and Chalalaki. Any schools that take longer than 10 minutes to reach are not included in the results. This can be visualized in the following figure 90 School closest facility mapping Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

Service Area Analysis  The service area solver generates polygons or lines that cover all edges within a given distance, travel time or other impedance unit from the predefined facility/facilities  With the ArcGIS Network Analyst extension, you can find service areas around any location on a network.  A network service area is a region that encompasses all accessible streets  For instance, the 5-minute service area for a point on a network includes all the streets that can be reached within five minutes from that point.  Service areas created by Network Analyst also help evaluate accessibility. Concentric service areas show how accessibility varies with impedance. Once service areas are created, you can use them to identify how much land, how many people, or how much of anything else is within the neighborhood or Spatial Analysis and Modelling by Tadele Feyssa, Wollega 91 University

One of many uses of GIS analysis tools is to build models. What is a model? A model is a simplified representation of a phenomena or a system. A map is a model.  So are the vector and raster data models for representing spatial features. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 94 University

A model helps us better understand a phenomenon or a system by retaining the significant features and relationships of reality. Often used to identify locations that meet specific criteria  Can be used to infer an unknown quality or quantity using relationships with known or measurable quantities or qualities Can be used to generate new data Spatial Analysis and Modelling by Tadele Feyssa, Wollega 95 University

Classification of GIS Models Descriptive or Prescriptive Deterministic or Stochastic Static or Dynamic Deductive or Inductive Spatial Analysis and Modelling by Tadele Feyssa, Wollega 96 University

A model may be descriptive or prescriptive. Descriptive model describes the existing conditions of spatial data  Prescriptive model offers a prediction of what the conditions could be or should be. Eg If we use maps as analogies, a vegetation map would represent a descriptive model and a potential natural vegetation map, a prescriptive model. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 97 University

The vegetation map shows existing vegetation, whereas the potential natural vegetation map predicts the vegetation that could occupy a site without disturbance or climate change Spatial Analysis and Modelling by Tadele Feyssa, Wollega 98 University

Both deterministic and stochastic models are mathematical models represented by equations with parameters and variables. A stochastic model considers the presence of some randomness in one or more of its parameters or variables, but a deterministic model does not Spatial Analysis and Modelling by Tadele Feyssa, Wollega 99 University

 A dynamic model emphasizes: the changes of spatial data and the interactions between variables, Whereas a static model deals with the state of spatial data  at a given time Many environmental models such as groundwater pollution and soil water distribution are best studied as dynamic models Spatial Analysis and Modelling by Tadele Feyssa, Wollega 100 University

A deductive model represents the conclusion derived from a set of premises. These premises are often based on scientific theories or physical laws An inductive model represents the conclusion derived from empirical data and observations. Eg To assess the potential for a landslide one can use a deductive model based on laws in physics or use an inductive model based on recorded data from past landslides Spatial Analysis and Modelling by Tadele Feyssa, Wollega 101 University

The development of a model follows a series of steps. 1st step is to define the goals of the model This is similar to defining a research problem  What is the phenomenon to be modeled? Why is the model necessary? What spatial and time scales are appropriate for the model?  One can use a conceptual or schematic model to show the essential structure of the model Spatial Analysis and Modelling by Tadele Feyssa, Wollega 102 University

2nd step is to break down the model into elements and to define the properties of each element and the interactions between the elements A flowchart may used as a useful tool for linking the elements Also at this step, one will gather mathematical equations of the model and use tools in a GIS to carry out the computation Spatial Analysis and Modelling by Tadele Feyssa, Wollega 103 University

3rd step is the implementation and calibration of the model Data are needed for running and calibrating the model 4th Validate the model Spatial Analysis and Modelling by Tadele Feyssa, Wollega 104 University

GIS can assist the modeling process in several ways. First, a GIS can be used a tool that can process, display, and integrate different data sources including maps, digital elevation models (DEMs), GPS (global positioning system) data, images, and tables These data are needed for the implementation, calibration, and validation of a model. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 105 University

A GIS can function as a database management tool and, at the same time, is useful for modeling-related tasks such as exploratory data analysis and data visualization. Second, models built with a GIS can be vector-based or raster-based The choice depends on the nature of the model, data sources, and the computing algorithm Spatial Analysis and Modelling by Tadele Feyssa, Wollega 106 University

Third, the distinction between raster-based and vector-based models does not preclude GIS users from integrating both types of data in the modeling process. Fourth, the process of modeling may take place in a GIS or require the linking of a GIS to other computer programs. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 107 University

There are three scenarios for linking a GIS to other computer programs A loose coupling involves transfer of data files between the GIS and other programs through import and export A tight coupling gives the GIS and other programs a common user interface. eg, the GIS can have a menu selection to run a simulation program on soil erosion. An embedded system packages the GIS and other programs with shared memory and a common interface Spatial Analysis and Modelling by Tadele Feyssa, Wollega 108 University

This Chapter includes    Spatial Analysis and Modelling by Tadele Feyssa, Wollega 109 University

Binary model uses logical expressions to select spatial features from composite feature layers from a composite map or multiple grids The output of binary model is in binary format: 1 ( ) for spatial features that meet the selection criteria and  0 ( ) for features that do not Binary model can be the extension of data query Spatial Analysis and Modelling by Tadele Feyssa, Wollega 110 University

Site selection is the most common application of the binary model A siting analysis determines if a unit area meets a set of selection criteria for locating a certain activities Two approaches may be used to run a siting analysis One is to evaluate the preselected sites The second is to evaluate all potential areas Spatial Analysis and Modelling by Tadele Feyssa, Wollega 111 University

For example a municipality wants to select potential industrial sites that meets the following criteria  At least 5 acres in size Commercial zone Not subject to flooding Not more than 1 mile from a heavy-duty road Less than 10 percent slope Operationally the task involves the following Spatial Analysis and Modelling by Tadele Feyssa, Wollega 112 University

Operationally, the task involves the following from steps Gather all layers (land use, flood potential, road and slope) relevant to the selection criteria Select heavy duty roads from the road layer and create a 1 mile buffer zone around them  Intersect the road buffer zone layer and other layers Select sites which are equal to or larger than 5 acres Spatial Analysis and Modelling by Tadele Feyssa, Wollega 113 University

Application of Binary models Change detection is a simple application of the binary mode By overlaying two maps representing land covers at two different points in time, one can query the attribute data of the composite map to find, for example, where forested land has been converted to housing development Spatial Analysis and Modelling by Tadele Feyssa, Wollega 114 University

Application of Binary models Siting analysis is probably the most common application of the binary model A siting analysis determines if a unit area (a polygon or a cell) meets a set of selection criteria for locating for example a landfill There are at least two approaches to conducting a siting analysis. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 115 University

On evaluates a set of nominated or preselected sites And the other evaluates all potential sites Although the two approaches may use different sets of selection criteria, they follow the same approach for evaluation Spatial Analysis and Modelling by Tadele Feyssa, Wollega 116 University

1 2 3 1 2 3 4 ID Suit 1 3 2 1 3 2 ID Type 1 2 3 21 18 6 1 2 3 4 5 6 7 ID 1 2 3 4 2 18 5 6 7 Suit Type 3 3 1 2 2 1 21 18 18 21 6 6 Suit = 2 AND Type = 18 + + An illustration of a vector-based binary model. The two maps at the top are overlaid so that their spatial features and their attributes of Suit and Type are combined. A logical expression, Suit = 2 AND Type = 18, results in the selection of polygon 4 in the output. Spatial Analysis and Modelling by Tadele Feyssa, 117 Wollega University

1 1 1 4 3 2 4 4 3 3 3 4 4 4 4 4 1 1 1 3 3 2 2 3 3 3 4 4 3 3 4 4 Grid 1 Grid 2 ([Grid1] = 3) AND ([Grid2] = 3) = An illustration of a raster-based binary model. A query statement, ([Grid1] = 3) AND ([Grid2] = 3), results in the selection of 3 cells in the output. Spatial Analysis and Modelling by Tadele Feyssa, 118 Wollega University

An index model calculates the index value for each unit area and produces a ranked map based on the index values. An index model is similar to a binary model in that both involve multi-criteria evaluation and both depend on overlay operations for data processing But an index model produces for each unit area an index value rather than a simple yes or no Spatial Analysis and Modelling by Tadele Feyssa, Wollega 119 University

The primary consideration in developing an index model, either vector-based or raster, is the method for computing the index value The weighted linear combination method is probably the most common method for computing the index value Spatial Analysis and Modelling by Tadele Feyssa, Wollega 120 University

To build an index model with the selection criteria of slope, aspect, and elevation, the weighted linear combination method involves evaluation at three levels. The first level of evaluation determines the criterion weights (e.g., Ws for slope) The second level of evaluation determines standardized values for each criterion (e g, sl, s2, and s3 for slope) The third level of evaluation determines the index (aggregate) value for each unit area Spatial Analysis and Modelling by Tadele Feyssa, Wollega 121 University

Weighted linear combination is a common method for computing the index value For example combination of slope aspect and elevation Weighted linear combination involves evaluation at three levels Spatial Analysis and Modelling by Tadele Feyssa, Wollega 122 University

To build an index model with the selection criteria of slope, aspect, and elevation, the weighted linear combination method involves evaluation at three levels. The first level of evaluation determines the criterion weights (e.g., Ws for slope). The second level of evaluation determines standardized values for each criterion (e.g., s1, s2, and s3 for slope). The third level of evaluation determines Spatial Analysis anthd eM ionddelelixn g( abyg gTardeeglae tFee)y svsaa,l ue for each unit area. 123 Wollega University

1 2 3 1 2 3 0.64 0.46 0.14 0.26 0.44 0.68 0.56 1 2 3 4 5 6 7 ID Suit 1.0 1.0 0.2 0.5 0.5 0.5 (S_V * 0.4) + (T_V * 0.6) + + 1 2 3 3 1 2 S_V 1.0 0.2 0.52 ID Type 1 2 3 21 18 6 T_V 0.4 0.1 0.83 ID 1 2 3 4 5 6 7 S_V 0.2 T_V 0.4 0.1 0.1 0.1 0.4 0.8 0.8 An illustration of a vector-based index model. First, the Suit and Type values of the two input maps are standardized from 0.0 to 1.0. Second, the two maps are overlaid. Third, a weight of 0.4 is assigned to the map with Suit and a weight of 0.6 to the map with Type. Finally, the index values are calculated for each polygon in the output by summing the weighted values. For example, Polygon 4 has an index value of 0.26 (0.5 *0.4+ 0.1*0.6). Spatial Analysis and Modelling by Tadele Feyssa, 124 Wollega University

7 21 32 49 1 3 5 2 45 57 63 31 0.2 0.6 0.8 1.0 0.2 0.6 1.0 0.4 0.6 0.8 1.0 0.4 x 0.6 x 0.2 x 0.2 0.12 0.36 0.48 0.60 0.04 0.12 0.20 0.08 0.12 0.16 0.20 0.08 0.28 0.64 0.88 0.76 Input grids Standardize cell values into 0.0-1.0 scale Multiply by criterion weights Calculate index values by summing weighted criterion values An illustration of a raster-based index model. First, the cell values of each input grid are converted into the standardized scale of 0.0 to 1.0. Second, the index values in the output grid are calculated by summing the products of each grid multiplied by its assigned weight. For example, the index value of 0.28 is calculated from: 0.2*0.6 + 0.2*0.2 + 0.6*0.2, or 0.12 + 0.04 + 0.12. Spatial Analysis and Modelling by Tadele Feyssa, 125 Wollega University

 First the relative importance of each criterion or factor is evaluated against the other criteria  In most cases we use expert-derived paired comparison for evaluating criteria This method involves performing ratio estimates for each pair of criteria For instance if criterion A is considered to be three times more important than criterion B, then 3 is recorded for A/B and 1/3 for B/A Spatial Analysis and Modelling by Tadele Feyssa, Wollega 126 University

Application of the Index model Index model are commonly used for suitability analysis and vulnerability analysis A suitability analysis ranks areas for their appropriateness for a particular use A vulnerability analysis assess areas for their susceptibility to a hazard or a disaster Both analysis requires careful consideration of criteria and criterion weights Spatial Analysis and Modelling by Tadele Feyssa, Wollega 127 University

128 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

3. Regression Model Regression model relates a dependent variable to a number of independent (explanatory) variables in an equation which can then be used for prediction or estimation Regression model can use an overlay operation in GIS to combine variables needed for the analysis There are two types of regression model; linear and logistic regression Spatial Analysis and Modelling by Tadele Feyssa, Wollega 129 University

Regression Model… Some GIS packages are capable of performing linear or logistic regression analysis  Both Arclnfo Workstation and IDRISI have commands to build raster-based linear or logistic models  In fact it is not powerful like statistical analysis packages Spatial Analysis and Modelling by Tadele Feyssa, Wollega 130 University

Linear Regression Model A multiple linear regression model is defined by Y= a+b1Xi +b2x2+……bnxn Where y is the dependent variable, Xi is the independent variable I and b1,….. bn are the regression coefficients a is the intercept The primary purpose of linear regression is to predict values of y from values of Xi Spatial Analysis and Modelling by Tadele Feyssa, Wollega 131 University

regression y α β x β x ... β x 1 1 2 2 i i      Dependent Independent variables Predicted Predictor variables Response variable Explanatory variables Outcome variable Covariables 132

Logistic Regression Model  logistic regression is used when the dependent variable is categorical (e g., presence or absence) and the independent variables are categorical, numeric,or both. Although having the same form as linear regression, logistic regression uses the logit of y as the dependent variable logit(p)=ln(p/(1-p))=a+b1*x1+b2*x2+b3*x3 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University 133

Regression analysis applications Regression analysis can be used for a large variety of applications: Modeling traffic accidents as a function of speed, road conditions, weather, and so forth, to inform policy aimed at decreasing accidents. Modeling property loss from fire as a function of variables such as degree of fire department involvement, response time, or property values. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 134 University

There are three primary reasons you might want to use regression analysis: To model some phenomenon to better understand it. The basic objective is to measure the extent that changes in one or more variables jointly affect changes in another. Example: Understand the key characteristics of the habitat for some particular endangered species of bird (perhaps precipitation, food sources, vegetation, predators) to assist in designing legislation aimed at protecting that species. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 135 University

 It is mainly applied for bird habitat identification, rainfall-triggered land slide model, predicting grass land bird habitat attitude towards national park designation Spatial Analysis and Modelling by Tadele Feyssa, Wollega 136 University

4. Process Models A process model integrates existing knowledge about the environment process in the real world into: a set of relationships and equations for quantifying the processes A process model offers both a predictive capability and an explanation that is inherent in the proposed processes Therefore process models are by definition predictive and dynamic models 137 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

Environmental models are very complex and data intensive Environmental models are typically process models because they must deal with the interaction of many variables including physical variables such as climate, topography, vegetation, and soils as well as cultural variables such as land management Spatial Analysis and Modelling by Tadele Feyssa, Wollega 138 University

Revised Universal Soil Loss Equation (RUSLE) RUSLE is a model that is widely used to estimate average annual nonchannelized soil loss.  Soil erosion is an environmental Process that involves climate, soil properties, topography, soil surface conditions and human activities A well known model of soil erosion is the Revisited Universal Soil Loss Equation (RUSLE) RUSLE predicts the average soil loss carried by runoff from specific field slopes in specified cropping and management systems from range land Spatial Analysis and Modelling by Tadele Feyssa, Wollega 139 University

RUSLE is a multiplicative model with six factors A= R*K*L*S*C*P Where A is average soil loss R- is the rainfall fun off erosivity factor K is the soil erodibility factor L is the slope length factor S is the slope steepness factor C is crop management factor(land cover) and P = Support practice factor (conservation) Spatial Analysis and Modelling by Tadele Feyssa, Wollega 140 University

L and S can be combined into single topographic factor LS Slope length is defined as the horizontal distance from the point of origin of overland flow to the point where either the lope gradient decreases enough that deposition begins or the flow is concentrated in a defined channel RUSLE permits the estimation of long-term soil loss in a wide range of environmental settings.  RUSLE is the primary means for estimating soil loss on farm fields and rangelands 141 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

The RUSLE module not only allows the user to estimate average annual soil loss for existing conditions, it permits one to simulate how landuse change (C factor), climate change (R factor), and/or changes in conservation/management practices (P factor), will affect soil loss. Spatial Analysis and Modelling by Tadele Feyssa, Wollega University 142

Critical Rainfall Model Land slide is defined as a down slope movement of a mass of soil and a rock material A landslide hazard model measures the potential of landslide occurrence within the given area GIS has been employed in the past decades for development of landslide models 143 Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

 There are two types of landslide models; Physically based and statistical based models  Regression model is an example of statistical based land slide model  Critical Rainfall model is an example of physically based landslide model  The infinite slope model defines slope stability as the ratio of the available shear strength (Stabilizing forces) including soil and root cohesion to the shear of stress (destabilizing forces). Spatial Analysis and Modelling by Tadele Feyssa, Wollega 144 University

 The Critical Rainfall model combines the infinite slope model with a steady hydrologic model to predict the critical rainfall Qcr that can cause landslide Qcr can be calculated  Qcr = T sinѲ (a/b) (ps/pw)(1- sinѲ-C/cosѲtanØ)  T is saturated soil transmissivity  Ѳ is local slope angel  a is the upslope contributing drainage area  b is the unit contour length (the raster resolution)  Ps is wet soli density  Ø is the internal friction angle of the soil  Pw is the density of water  C is combined cohesion Spatial Analysis and Modelling by Tadele Feyssa, Wollega 145 University

 Critical rainfall model is regularly used for predicting shallow landslides triggered by rainfall events Spatial Analysis and Modelling by Tadele Feyssa, Wollega 146 University

Digital Elevation Model (DEM) The land surface has been the object of mapping, modelling and analysis for several years Map makers devised many techniques for terrain mapping; such as contouring, hill shading, 3D-view perspectives, etc. Geomorphologists have developed measures of the land surface which include slope, aspect and surface curvature Spatial Analysis and Modelling by Tadele Feyssa, Wollega 147 University

Terrain mapping is no more a subject of specialists. GIS has made it relatively easy to incorporate them in many application areas Slope and aspect play a regular role in hydrological modelling, snow cover evaluation, soil mapping land slide area delineation and soil erosion modelling Spatial Analysis and Modelling by Tadele Feyssa, Wollega 148 University

Most GIS packages treat elevation data (Z values) as attribute data at point or cell location However, in 3-D model an additional coordinate to x and y In raster data modelling the z value corresponds to the cell value  In vector data modelling the z value will be stored as attribute data Spatial Analysis and Modelling by Tadele Feyssa, Wollega 149 University

Digital elevation model and TIN  Digital elevation model is a data input for terrain mapping  The two most common data input for terrain mapping and analysis are DEM and TIN  Digital elevation model (DEM) is based on raster data analysis  Triangulated irregular network (TIN) is based on vector based data analysis  We can’t use TIN and DEM together but we can change TIN to DEM or DEM to TIN Spatial Analysis and Modelling by Tadele Feyssa, Wollega 150 University

DEM A DEM represents a regular array of elevation points A point based DEM must be converted before using this data for terrain mapping This conversion simply places each point in the DEM at the centre of a cell in elevation raster Therefore DEM and elevation raster can be used interchangeably Spatial Analysis and Modelling by Tadele Feyssa, Wollega 151 University

2301m Spatial Analysis and Modelling by Tadele Feyssa, Wollega 153 University

TIN A TIN approximates the land surface with a series of non-overlapping triangles Elevation values (Z values) along x, y, coordinates are stored at nodes that make up the triangles Unlike the DEM the TIN is based on an irregular distribution of elevation points Spatial Analysis and Modelling by Tadele Feyssa, Wollega 156 University

Terrain Mapping There are different kinds of terrain mapping Contouring is the most common for the terrain mapping it has contour interval and base contour The arrangement and pattern of contour lines reflect the topography  Contour lines don’t intersect one an other and will not stop in the middle of the map Spatial Analysis and Modelling by Tadele Feyssa, Wollega 157 University

Vertical Profiling A vertical profile shows changes in elevation along a line such as a road or a stream Spatial Analysis and Modelling by Tadele Feyssa, Wollega 159 University

Hill Shading Hill shading simulates how the terrain looks with interaction between sunlight and surface features A mountain slope directly facing incoming light will be very bright A slope opposite to it is will be dark. Four factors control the visual effects of hill shading Spatial Analysis and Modelling by Tadele Feyssa, Wollega 161 University

 The sun’s azimuth is the direction of the incoming light ranging from 0 (due north to 360 in a clockwise direction  Typically the default for the sun’s azimuth is 315 with  The sun’s altitude is the angle of the incoming light measured above the horizon between 0 and 90 degree Spatial Analysis and Modelling by Tadele Feyssa, Wollega 162 University

The other two factors are slope and aspect Slope ranges between 0 and 90 degrees Aspect ranges between 0 and 360 degrees Slope measures the rate of change of elevation at a surface location It can be expressed in percent or degree Aspect is the directional measures slope Spatial Analysis and Modelling by Tadele Feyssa, Wollega 163 University

Aspect starts with 0 degree at North pole and measures clockwise and ends with 360 degree Spatial Analysis and Modelling by Tadele Feyssa, Wollega 164 University

Viewshed Analysis Viewshed refers to the portion of the land surface that is visible from one or more viewpoints  The process for deriving viesheds is called viewshed or visibility analysis  A veiwshed analysis requires two input datasets  The first is usually point layer containing one or more view points such as a layer containing communication tower  The second input is DEM (an elevation raster or a TIN which represents the land surface Spatial Analysis and Modelling by Tadele Feyssa, Wollega 165 University

The line-of-sight operation is the basis for veiwshed analysis The line of sight also called sight-line, connects the viewpoint and the target GIS can display a sightline with symbols for the visible and invisible portions a long the sightline Spatial Analysis and Modelling by Tadele Feyssa, Wollega 166 University

Parameters of Viewshed Analysis A number of parameters can influence the result of a viewshed analysis  The first parameter is the viewpoint  After the view point is determined its elevation should be increased by the height of a physical structure  Fore example a forest lookout station is usually 15 to 20 meters high.  The height of the observation station can be added as an offset value to the elevation at the station Spatial Analysis and Modelling by Tadele Feyssa, Wollega 169 University

The second parameter is the viewing azimuth which sets horizontal angle limits of the view  Azimuth system for measuring direction is based on the 360 degrees found in a full circle. Viewing radius is the third parameter which sets the search distance for deriving visible areas. Other parameters include vertical viewing angle limits, the earth’s curvature, tree height and building height Spatial Analysis and Modelling by Tadele Feyssa, Wollega 170 University

Application areas of viewshed •Urban Planning •Cell phone tower placement •Location for wind turbines •Conservation projects Military purpose •Many more! Spatial Analysis and Modelling by Tadele Feyssa, Wollega 171 University

Watershed Analysis A watershed refers to an area defined by topographic divides that drains surface water to a common outlet A watershed is often used as a unit area for the management and planning of water and other resources Watershed analysis refers to the process of using DEMs and following water flow to delineate stream networks and watersheds Spatial Analysis and Modelling by Tadele Feyssa, Wollega 172 University

Traditionally watershed boundary can be drawn manually onto a topographic map The person who draws the boundaries uses topographic features on the map to determine where a divide is located Today computer based watershed analysis can do this job in a fraction of time Delineation of watershed can tale place at different spatial scales Spatial Analysis and Modelling by Tadele Feyssa, Wollega 173 University

 Delineation of watershed can be area based on point based  An area based method divides a study area into a series of watershed one for each stream section A point based method on the other hand derives a watershed for each select point  The select point may be an outlet a gauge station or a dam  Both methods follow a series of steps, starting with a filled DEM Spatial Analysis and Modelling by Tadele Feyssa, Wollega 175 University

A filled DEM is void of depressions or sinks A depression is a cell or cells surrounded by higher elevation values thus represent an area of internal drainage  Although some depressions are real most of them are the outcome of DEM imperfection  Therefore the Depression must be removed from a elevation raster A common method fro removing a depression is to increase the cell value to the lowest overflow point out of the sink Spatial Analysis and Modelling by Tadele Feyssa, Wollega 176 University

Treating depressions  routing through false depressions:  filling fill-in Spatial Analysis and Modelling by Tadele Feyssa, Wollega 181 University

Flow Direction A flow direction raster shows the direction water will flow out of each cell of a filled elevation raster Flow directions are commonly determined using single using single or multiple flow direction methods D8 is a popular single flow direction method  The D8 assigns a cell’s flow direction to the one of its 8 surrounding cells that has the steepest distance-weighted gradient Spatial Analysis and Modelling by Tadele Feyssa, Wollega 182 University

D8’ (8 direction) method , sets the flow direction toward the lowest of the eight neighboring cells, in relation to the centre cell. Therefore, flow is allowed in one of eight possible directions, assuming that water will travel along the steepest down slope path Based on the 3 x 3 cell neighborhood , flow would be directed from the centre cell (elevation of 8) to the southwest cell (elevation of 4). Spatial Analysis and Modelling by Tadele Feyssa, Wollega 183 University

 Multiple flow direction methods allow flow divergence or flow bifurcation  The flow direction of the centre cell can be determined by first calculating the distance between weighted gradient to each to each of its 8 neighbors.  For the immediate neigbours the gradient is calcualted by dividing the elveation difference between the center and the neighbour cell by 1  For the four corner neighbours, the gradient is calculated by dividing the elevation difference by 1.414  The result shows the steepest gradient and therefore the flow direction is from the centre to the steepest direction Spatial Analysis and Modelling by Tadele Feyssa, Wollega 185 University

Flow Direction for cell 1014 1011 1004 1019 1015 1007 1025 1021 1012 +1 +4 +11 -4 +8 -10 -6 +3 +0.71 +4 +7.78 -4 +8 -7.07 -6 +2.12 1 1 1.414 Cell Elevation Elevation difference Distance weight Distance weight Gradient Flow direction 32 64 128 16 1 8 4 2 Flow direction Number Spatial Analysis and Modelling by Tadele Feyssa, Wollega 186 University

Flow accumulation A flow accumulation raster tabulates for each cell number of cells that will flow to it. The tabulation is based on the flow direction raster With the appearance of spanning tree a flow accumulation raster records how many upstream cells will contribute drainage to each cell A flow accumulation raster can be interpreted in two ways. Spatial Analysis and Modelling by Tadele Feyssa, Wollega 188 University

 First, Cells having high accumulation values generally correspond to stream channels Whereas cell having an accumulation value of zero generally correspond to ridge lines Second if multiplied by the cell size, the accumulation value equals the drainage area Spatial Analysis and Modelling by Tadele Feyssa, Wollega 189 University

Stream network A stream network can be derived from a flow accumulation raster The derivation is based on the channel initiation threshold which represents the amount of discharge needed to maintain a channel head with contributing cells serving as a surrogate for discharge A threshold value of 500 for example means that each cell of the drainage network has a minimum of 500 contributing cells Spatial Analysis and Modelling by Tadele Feyssa, Wollega 191 University

Stream links After a stream network is derived from a flow accumulation raster, each section of the stream raster line is assigned a unique value and is associated with a flow direction A stream link raster therefore resembles a topology based stream layer The intersections or junctions are like nodes and the stream sections between junctions are like arcs reaches Spatial Analysis and Modelling by Tadele Feyssa, Wollega 192 University

Area wide watershed The final step is to delineate a watershed for each stream section This operation uses the flow direction raster and the stream link raster as the inputs A denser stream network (based on a smaller threshold value) will have more but smaller watersheds Spatial Analysis and Modelling by Tadele Feyssa, Wollega 193 University

Point Based watershed Sometimes we use point based for watershed analyis. The points are called pour points or outlets Delineation of water sheds based on individual pour points follow the same procedure for delineating area wide water sheds delineation The only difference to substitute a point raster for a stream link raster Spatial Analysis and Modelling by Tadele Feyssa, Wollega 195 University

 In a point raster a cell representing a pour point must be located over a cell that is part of the stream link  If a pour point is not located directly over a stream link, it will result in a small incomplete watershed for the outlet The relative location of the pour point to a stream network determines the size of a point based watershed Spatial Analysis and Modelling by Tadele Feyssa, Wollega 196 University

We use the snap pour point command to snap pour point to the cell the highest flow accumulation within a used defined search distance The snap pour point operation should be considered part of the data processing for delineating point based watersheds Spatial Analysis and Modelling by Tadele Feyssa, Wollega 197 University

Application of water shed analysis Water shed analysis is mainly used for natural resource management • Floodplain Management • Land Use Planning • Invasive Species • River Function • Water Supply Spatial Analysis and Modelling by Tadele Feyssa, Wollega 198 University

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Spatial analysis and modeling