KOCAELI UNIVERSITY Institute Of Natural And Applied Sciences Department Of Geodesy And Geoinformatic Engineering Homework Of Spatial Analysis Utilizing of GIS Least Cost Path Route Using GIS Supervisor : Accos.Prof Ozan ARSLAN Prepared by : 135114002 Mohammad Reza Shirzad
1. Overview of Least Cost Path Analysis Least cost path analysis is a distance analysis tool within GIS that uses the least cost path or the path between two locations that costs the least to those travelling along it to determine the most cost-effective route between a source and destination. Cost can be a function of time, distance or other criteria that is defined by the user. When using least cost path analysis in GIS, the eight neighbors of a raster cell are evaluated and the generated path moves to the cells with the smallest accumulated or cost value (“Distance Analysis Using ArcGIS”). This process is repeated multiple times until the source and destination are connected. The completed path is the smallest sum of raster cell values between the two points and it has the lowest cost. Least cost path analysis is an important GIS tool to understand because it has many different applications – all of which can help businesses, city planners and other users save time and money.
Requirements for Least Cost Path Analysis Prior to conducting a least cost path analysis there are some requirements that all users must have and understand. These are a source raster, a cost raster, cost distance measures and an algorithm for deriving the least cost path (Chang). A source raster is a raster that “defines the source to which the least-cost path from each cell is calculated” (Chang, pg. 380). This means that the source raster is the raster that defines where the least cost path is going and the source cell is the end of that path. In the source raster only the source cell has a value and all others are given a “no data” value. The cost raster is the raster that defines the cost or other impedance (defined by the user) to move through each raster cell. A cost raster has three characteristics. The first is that the cost of each cell is the sum of the different costs while the second is that the cost can represent either the actual or the relative cost (Chang). The third characteristic is that the cost factors can be weighted depending on the importance of each factor (Chang). For example if travel time is considered a more important factor than the monetary cost, it can be it can be weighted more in the analysis.
The cost distance measure portion of least cost path analysis is centered on the node-link raster cell representation (Chang). In this representation a node is the center of the raster cell and the link connects the node to its neighboring cells. A lateral link is also included to connect a cell to one of its four nearest neighbors and a diagonal link connects the cell to the corner neighbors (Chang). The cost distance is the cost that it takes to travel from the node to these links and the least cost path is based on these costs. Finally, least cost path analysis requires an algorithm for deriving a least cost path. An algorithm is an important component of least cost path analysis because accumulative cost between two cells can be calculated by adding the costs of connecting the two cells but least accumulative costs are challenging because there are many different ways to connect two cells and they do not have to be immediate neighbors (Chang). Least cost paths are derived after all possible paths are evaluated, thus an algorithm makes these complex and time consuming calculations much easier.
Creating a Least Cost Path Analysis Once the four requirements for conducting a least cost path analysis are met it is important to consider and correctly weight the rasters making up the cost raster to create an effective least cost path analysis (“Creating the Least Cost Path.”). The cost is the factor that is being tested in these analyses and they can be a function of time, distance and/or other criteria that the user defines and deems most important. How the cost rasters are weighted depends on the project application and the desired results. Take for example a least cost path analysis examining routes between two campgrounds. The time it takes to travel between them and the monetary cost of fuel are the two are the important costs being tested. The user needs to determine which of these is most important and weight them accordingly. Weighted distance analysis (the determination of the best path between the two campgrounds considering time and fuel usage based on terrain, etc.) is one tool that can be used to help weight the two factors (“Distance Analysis Using ArcGIS”). Once correctly weighted a least cost path analysis between the two is generated based on factors such as speed limits and terrain among others.
When creating a least cost path analysis there is a simple workflow to follow to ensure that the analysis is complete. The ESRI Virtual Campus course, “Distance Analysis Using ArcGIS,” outlines this workflow nicely. The first step in the least cost path analysis workflow is to reclassify the value ranges of the rasters to make sure that they use a common weighting scale. The next step is to combine the reclassified rasters and create a total cost surface. This step is followed by performing a cost weighted analysis to create cost the distance and cost direction rasters that are required to complete the analysis. The final step in conducting a least cost path analysis is to use the cost distance and cost direction rasters as the main inputs in the cost path tool. This tool then determines the final least cost path from the source to the destination.
Applications/Examples of Least Cost Path Analysis Least cost path analysis has a number of different GIS applications. It is often used in planning infrastructure such as roads, pipelines, canals, and power transmission lines as well as for recreational uses such as the development of a hiking trail system in a national park (Chang). Least cost path analysis can also be used by ecologists to monitor wildlife movements to combat environmental issues like habitat fragmentation. Economic and business geographers as well as those making tourist maps and guides can use least cost path analysis to determine the best and most cost-effective routes between places on delivery routes, national monuments or other destinations.
Spatial Analysis Functional Categories : Spatial Analysis of GIS has several functional categories such as flowing which each of them also has variation of functional categories would utilize for different purposes.
Conditional
Density
Distance
Zonal
Extraction
Surface
Generalization
Solar Radiation
Groundwater
Hydrology
Reclass
Spatial analysis
Raster Creation
Interpolation
Overlay
Local Map Algebra
Neighborhood Math Trigonometric
Math Bitwise Math General
Math Logical Multivariate
2. Identify the project objectives : In this project we will use Spatial Analysis to find the Least Cost Path Route between points by the name of “Cerro Pelado” for the origin and “Chumicoso” for the destination in Panama .
Figure 1
The criteria that is used are : Slopes: Higher slopes are most costly than lower slopes. Land use: It is considered that intervened land uses are less costly than intervened areas for examples farm land will be less costly than forest . Actually these criteria will given by related agencies on the information sheets to us.
3. Data requirements and Creating Database: For applying this project we need some data such as flowing: 1.Origin:(Cerro Pelado) that is Feature class representing the origin point 2.Destination:(Chumicoso)that is Feature class representing the destination point 3.Town: which is raster feature 4.Rivers: which is feature class representing the rivers 5.Streets: which is feature class shows the exciting street lines of the city 6.San flax district: which is Raster data representing the San flax district area (yellow color) 7.Land use: which is the raster dataset represent the categories of the land use in this project. 8.DEM: which is the raster dataset representing the Elevations of the study area
Figure 2
Creating a new model: If we intend to run a sequence of tools, experiment with parameter values to achieve the desired result, or to package our methodology so others can apply it to their data, we should build a model. A model is built by stringing tools together inside a Model Builder window.
Adding data to model builder : At first we make a slope surface using the DEM data to be applicable for using for reclassifying as at first we had mentioned that slope is one of the criteria of the project. For producing a slope surface we use Surface tools from spatial analysis tools.
Surface : With the Surface tools, we can quantify and visualize a terrain landform represented by a digital elevation model. Starting with a raster elevation surface as input, with these tools, we can gain information by producing a new dataset. We can derive patterns that were not readily apparent in the original surface, such as contours, angle of slope and etc.
The reclassification tools reclassify or change cell values to alternative values using a variety of methods.
Reclass : The Reclass tools provide a variety of methods that allow us to reclassify or change input cell values to alternative values. For instance : Replace values based on new information. Group certain values together. Reclassify values to a common scale (for example, for use in a suitability analysis or for creating a cost raster for use in the Cost Distance function). Set specific values to NoData or set NoData cells to a value We can reclass one value at a time or groups of values at once using alternative fields; based on a criteria, such as specified intervals.
Reclassifying the DEM slope as it is showed below, it reclassified from 5 class to 10 class that the smaller they are the smaller cost will be and diversely from end.
Weighing the data: In this step as we mentioned before, to weighting the data influencing, we will use weight overlay tools to performing this process .
Overlay tools : Overlay analysis tools allow us to apply weights to several inputs and combine them into a single output. The most common application for Overlay tools is suitability modeling. The following lists the general steps to perform overlay analysis: Define the problem. Break the problem into submodels. Determine significant layers. Reclassify or transform the data within a layer. Weight the input layers. Add or combine the layers.
So we give weight to slopes and Land use and define the percentage of their influencing that will counted for cost as following :
Calculate the cost to destination : In this step to calculate the cost from any destination it is used cost distance tools.
Distance Modeling : Determine the least expensive method for a new road, flight pattern, shipping route, or any factor that is effected by time and coast. By mapping distance, the analyst can find out information such as the nearest hospital from certain areas for an emergency helicopter or all fire hydrants within 500 meters of a burning building. Alternatively, the analyst can find the shortest (or least cost ) path from a location to another based on some cost factor.
This action performs such as way that calculate the cost from any destination need a cost data that it gained from last term so out put data will use for next step to calculate the least cost path.
Least cost path : Finally in order to calculate the least cost path it is used from cost path tools and adding data such as origin and cost distance which had gained recently.
4.Result: As the result it is showed the suggested least cost path route between Cerro Plado and Chumicoso .
Conclusions Spatial analysis would be done by spatial analysis tool box separately but it is so better if we use from a model builder to do a spatial analysis such as way that model builder is an application you use to create, edit, and manage models. The main advantage of using the model builder for GIS work is that we can save our GIS process and rerun the model at any time. This is particularly good when we need to go back and make adjustments to our process/analysis. Rather than redoing the entire analysis, we can simply change 1 parameter and rerun the model to produce new results. Whatever its use, least cost path analysis is an important tool in GIS because it has the ability to help businesses, city planners and other users save time and money.
References: Chang, Kang-tsung. (2012). Introduction to Geographic Information
Systems. McGraw-Hill: New York, 6th Edition. ESRI. (n.d.). “Creating the Least Cost Path.” ArcGIS Resources. Retrieved from: http://resources.arcgis.com/en/help/main/10.1/index.html#//009z0000 0021000000[S1] (3 April 2014). ESRI. (n.d.). “Distance Analysis Using ArcGIS.” ESRI Virtual Campus. Personal Notes. (Course Taken 2 April 2014). ArcGis Spatial Analysis: Advanced GIS spatial Analysis Using Raster and Vector data.An Essri “white paper” December 2001. Creating a least Cost path Route Using ArcGIS 10 By : Luis Carlos Berrocal.
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