spatial analyst – identifying the best paths with cost ... · two step process for performing...
TRANSCRIPT
Spatial Analyst – Identifying the Best
Paths with Cost Distance AnalysisKevin M. Johnston
Elizabeth Graham
Cost distance analysis - Outline
• What is cost distance analysis
• Creating a cost surface
• Cost Analysis 1
- Cost Connectivity
• Demo
• Cost Analysis 2
- Two step process – Cost Distance and Cost Path
• Demo
• Source characteristics and Network Analyst
• Demo
• Adding complexity and corridor analysis
What is cost distance analysis
• One of the most common applications in Spatial Analyst
• Euclidean distance – as the crow flies
• Cost distance – as the phenomenon moves across the landscape
• Euclidean is how far while cost analysis is a total amount
• Cost can be:
- Preference
- Energy expended
- Time
- Dollars
- Risk
Problems addressed by cost distance analysis
• Constructing a road to a proposed shopping center
• Conserving wildlife corridors between habitat patches
• Supplying and reinforcing military troops in a deployment
• Providing movement paths for fire fighters between posts
• Locating a pipeline to connect energy fields to a refinery
• Siting electrical lines
Why do patches need to be connected?
• Fragmentation Metapopulation
- Logging Roads
- Supply routes for military locations
- Fire fighting routes
Cost distance analysis - Outline
• What is cost distance analysis
• Creating a cost surface
• Cost Analysis 1
- Cost Connectivity
• Demo
• Cost Analysis 2
- Two step process – Cost Distance and Cost Path
• Demo
• Source characteristics and Network Analyst
• Demo
• Adding complexity and corridor analysis
The problem
• Have series of locations
- Habitat patches
- Firefighting headquarters
- Military installations
• Suitability model using Locate
Regions tool or known locations
• Need to connect them the most
effective way possible
• Cost distance analysis
Need a cost surface for movement
• Create a surface identifying the cost
to move through each cell location
• Similar to creating a suitability model
• Cost per map unit to move through
the cell
• The lower the cost the better
• Diagonal movement accounted for
How to create cost surface
• Define the problem (which includes creating submodels)
• Identify and derive the criteria
• Transform values to a common scale
• Weight the criteria relative to one another and combine
• Analyze the results
How to create cost surface
• Define the problem (which includes creating submodels)
• Identify and derive the criteria
• Transform values to a common scale
• Weight the criteria relative to one another and combine.
• Analyze the results
The input to cost distance analysis
• Define the problem (which includes creating submodels)
• Identify and derive the criteria
• Transform values to a common scale
• Weight the criteria relative to one another and combine.
• Analyze the results
Cost analysis 1 – Cost Connectivity
• Input regions
• Input cost
• Create a network of paths
- Output optimum paths
- Optional neighboring paths
Cost Connectivity – Optimum network
• Cost allocation identifies regions to
connect
• Cost path connects neighbors
- Base on cost not Euclidean distance
• Convert to graph theory
- Nodes – the regions
- Edges – the paths
- Weights – accumulative cost
• Maps back to paths
C
A
B
Cost Connectivity – Optimum network
DemoCreating the optimum network
Creating a cost surface
Cost Connectivity
Cost distance analysis - Outline
• What is cost distance analysis
• Creating a cost surface
• Cost Analysis 1
- Cost Connectivity
• Demo
• Cost Analysis 2
- Two step process – Cost Distance and Cost Path
• Demo
• Source characteristics and Network Analyst
• Demo
• Adding complexity and corridor analysis
Two step process for performing cost distance analysis
• Cost Distance tool
- Input
- Sources – starting point
- Cost surface – cost per map unit for travel
- Output
- Cost distance – total accumulative least-cost for
each cell to reach a source
- Back link – direction to move from each cell to
reach a source
- Cost allocation – for each cell, which is the least-
cost source
• Cost Path tool
- Input
- Destination – ending point
- Cost distance and Back link output rasters from
Cost Distance tool
- Output
- Least-cost paths – the least-cost paths
Use cases for two-step process
• Know the specific start and end locations
• Have two locations to connect
• Add a path to the Cost Connectivity
network
• Connect location to several other locations
Step 1: How to perform the cost distance analysisThe Cost Distance tool
• Sources
• Cost surface
• Output distance raster – for each cell, the
lowest total accumulative cost to reach a source
Step 1: How to perform the cost distance analysisThe Cost Distance tool (continued)
• Back link
• Cost allocation
Step 2: How to create the least-cost pathThe Cost Path tool
• Destination and the cost distance and back link output from
the Cost Distance tool
• Creates the least cost path from the destination to the sources
- Best single path
- Each zone
- Each cell
DemoCreating the least-cost path
Cost Distance
Cost Path
Cost distance analysis - Outline
• What is cost distance analysis
• Creating a cost surface
• Cost Analysis 1
- Cost Connectivity
• Demo
• Cost Analysis 2
- Two step process – Cost Distance and Cost Path
• Demo
• Source characteristics and Network Analyst
• Demo
• Adding complexity and corridor analysis
How cost distance analysis works
accum_cost = a1 + (cost2 + cost3)/2
Where
a1—The accumulative cost from cell 1 to cell 2
cost2—The cost of travel for cell 2
cost3—The cost of travel for cell 3
accum_cost—The accumulative cost to move into cell 3 from cell 1
Source characteristics
• Multiplier
- Different modes of travel from each source - an ATV versus walking
- Different magnitudes at each source – number of firefighters at each headquarter
• Start cost
- Time it takes to prepare before leaving the source
a1 = starting_cost + (((cost1 + cost2) / 2) * cost_multiplier)
• Resistance rate
- A hiker getting tired
• Capacity
- Identify potential locations for refueling stations for military tanks
accum_cost = (a1 * (1 + resistance_rate)) + ((((cost2 * HF(2)) + (cost3 * HF(3)))/2) *
Surface_distance(23) * VF(23) * cost_multiplier)
Source characteristics (cont.)
• Travel direction (From source and To source)
- Bobcat, for security, prefer locations away from roads
- Bobcat prefer locations that are easiest to access streams
Travel from source
a5 = c1 + c2 (1+r) + c3 (1+r)2 + c4(1+r)3 + c5(1+r)4
Where
a5—The least accumulative cost for the first five cells
ci—The cell identifier
r—The resistance rate
Travel to source
a5 = c1 (1+r)4 + c2 (1+r)3 + c3 (1+r)2 + c4 (1+r) + c5
Cost Connectivity – Neighbor paths and Network Analyst
DemoControlling the mover
Source Characteristics
Network Analyst
Cost distance analysis - Outline
• What is cost distance analysis
• Creating a cost surface
• Cost Analysis 1
- Cost Connectivity
• Demo
• Cost Analysis 2
- Two step process – Cost Distance and Cost Path
• Demo
• Source characteristics and Network Analyst
• Demo
• Adding complexity and corridor analysis
Adding complexity Adding surface distance with the Path Distance tool
• Actual distance traveled
• Endure the cost longer because going uphill
or downhill
• Surface raster
• The Path Distance tool
Adding complexityAdding directionality with the Path Distance tool
• Cost adjustment to overcome going
uphill and downhill
• Vertical factor
- Surface raster endure cost longer
- Vertical factor the additional cost to over
come the slope
Adding complexityAdding directionality with the Path Distance tool
• Horizontal factor
- Additional cost to overcome a horizontal factor
such as wind
Accum_cost_distance = a1 + (((Cost_Surface(b) * Horizontal_factor(b)) +
(Cost_surface(c) * Horizontal_factor(c)))/2) * Surface_distance(bc) * Vertical_factor(bc)
Creating a cost corridorThe Corridor tool
• Cost distance from source one
• Cost distance from source two
• Combine in the corridor tool
• Extract by Attribute tool to identify acceptable
threshold
Additional resource
• Two case studies in the Find locations section of the case studies in the online help
• Cost distance analysis
http://desktop.arcgis.com/en/analytics/case-studies/understanding-cost-distance-
analysis.htm
• Case study with 4 lessons with data (ArcGIS desktop and Pro
- Lesson 1: Creating a cost surface
- Lesson 2: Creating an optimal connectivity network
- Lesson 3: Creating a least cost path
- Lesson 4: Creating a corridor
Additional resource
• Suitability modeling:
http://desktop.arcgis.com/en/analytics/case-studies/understanding-the-suitability-modeling-
workflow.htm
• Case study and 4 lessons with data
- Lesson 1: Exploring and deriving data
- Lesson 2: Transforming data onto a common scale
- Lesson 3: Weighting and Combining Data
- Lesson 4: Locating and connecting regions
DemoDirectionality
Path Distance
Case studies and lessons
Cost distance analysis
Suitability modeling
Conclusion
• Defining the cost units can be difficult
• Can create the optimum network of paths – Cost Connectivity
- Compatible with Network Analyst
• Two step process – Cost Distance and Cost Path
- When source and destination known
• Source Characteristics
- Difference modes of travel from a source
- Starting costs
- Dynamic adjustment – getting tired
• Directionality – Path Distance
• Creates the optimum least-cost solution
- Assumes memory
- Has visited all locations
Acknowledgements: The Vermont Center for Geographic Information for the use of their data in this presentation
Other Spatial Analyst sessions
• Spatial Analyst: An Introduction
- Tues 10:15 – 11:30
- Wed 10:15 – 11:30
• Finding the Best Locations Using Suitability Modeling
- Tues 1:30 – 2:45
- Thurs 8:30 – 9:45
• Identifying the Best Paths with Cost Distance
- Tues 3:15 – 4:30
- Wed 1:30 – 2:45
• Suitability Modeling and Cost Distance Analysis Integrated Workflow (Demo Theater)
- Wed 4:30 – 5:15
• Python: Raster Analysis
- Tues 8:30 – 9:45
• Getting Started With Map Algebra Using the Raster Calculator and Python (Demo
Theater)
- Thurs 9:30 – 10:159
Other Spatial Analyst sessions
• Modeling Renewable Energy Potential Using ArcGIS (Demo Theater)
- Tues 1:30 – 2:15
• Creating Watersheds and Stream Networks
- Wed 10:00 – 10:30
• Hydrologic and Hydraulic Modeling
- Wed 3:15 – 4:30
- Thurs 1:30 – 2:45
• GIS Techniques for Floodplain Delineation (Demo Theater)
- Tues 12:30 – 1:15
• Creating a Hydrologically Conditioned DEM (Demo Theater)
- Tues 10:30 – 11:15
• Creating Surfaces from Various Data Sources
- Tues 3:15 – 4:30
- Thurs 3:15 – 4:30
• Choosing the Best Kriging Model for Your Data (Demo Theater)
- Wed 11:30 – 12:15
Other Spatial Analyst sessions
• Surface Interpolation in ArcGIS (Demo Theater)
- Thurs 10:30 – 11:15
• Creating Watersheds and Stream Networks (Demo Theater)
- Wed 10:00 – 10:30
• Working with Elevation Services (Demo Theater)
- Tues 10:30 – 11:15
- Wed 9:30 – 10:15
• Building Python Raster Functions (Demo Theater)
- Tues 10:30 – 11:15
• Raster Analytics in Image Server: An Introduction
- Wed 3:15 – 4:30
• Raster Classification with ArcGIS Desktop (Demo Theater)
- Thurs 9:30 – 10:15
• Raster Function Processing (Demo Theater)
- Thurs 10:30 – 11:15
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