Latest Contributions by SarmisthaChatterjee

Suitability modeling is one of the most common applications for a GIS analyst. It can be used to determine the best location to build a shopping center, housing development, or ski resort. You can also use it to find the best areas for parks, endangered wildlife habitat, or flood control. Suitability modeling is one of the first applications you are taught when learning GIS. It is the application that made you fall in love with GIS. Traditionally, creating a suitability model is a linear process. You needed to understand the steps in the workflow in which you independently specify the parameters for each criterion with little knowledge of their effect on the output. You had to trust the process when creating the output. The new Suitability Modeler is an interactive, exploratory environment for creating and evaluating a suitability model. It is available now for ArcGIS Pro 2.6 with a Spatial Analyst extension license. The basic premise underlying the Suitability Modeler is that a model is a dynamic process. Constructing a suitability model is a nonlinear, iterative process. At each step of the workflow, analytical feedback is provided through interacting maps, plots, and panes. This allows you to make informed decisions on the parameters you specify and easily see how they will affect the final output. The Modeler allows seamless back and forth movement between each stage of the model development. What you learn in one stage of the process may influence the decisions you make in another. Because of the exploratory and interactive nature of the Suitability Modeler, you can justify each step of your modeling process. As a result, you will be more confident in the model output leading to better decisions. The best way to understand the Suitability Modeler is to see it in action. The following video was presented at the Plenary of the 2020 Esri User Conference. To learn more about the Suitability Modeler, see What is the Suitability Modeler? To understand the general steps of a suitability model, see The general suitability modeling workflow. The content of the blog The new Suitability Modeler: Changing the way suitability models are created is written by Kevin Johnston. Kevin has been a Product Engineer on the Spatial Analyst development team for over 29 years. He has degrees in Landscape Architect from Harvard and in environmental modeling from Yale. At Esri, Kevin’s current focus is developing suitability and connectivity tools. He hopes the tools that he works on can help users make more informed decisions. ... View more

We are excited to share with you some of the important improvements that we did in the Zonal toolset for ArcGIS Pro 2.6 and ArcGIS Image Server 10.8.1 to make your workflows more intuitive and powerful. We have significantly improved and enhanced the geoprocessing tools, the corresponding raster functions, the ArcGIS API for Python and the ArcGIS REST API. New enhancement in zonal functionalities now included new capabilities, options, and ways of accessing the tools. Read on to learn more about it. Process overlapping polygon zones The Tabulate Area tool in ArcGIS Pro 2.6 Spatial Analyst extension now process overlapping zones and calculates area for each individual feature. Calculate percentile The zonal statistics tools and the function have an additional statistics type, percentile, to compute extreme events. Using the Zonal Statistics as Table tool, you can also calculate multiple percentile values at one go, as shown below:   This capability can be accessed from: ArcGIS Pro 2.6 Spatial Analyst and Image Analyst extensions: - Zonal Statistics as geoprocessing tool and function - Zonal Statistics as Table as geoprocessing tool ArcGIS Image Server 10.8.1: - Summarize Raster Within as Map Viewer tool, Portal tool in ArcGIS Pro, ArcGIS API for Python and ArcGIS REST API - Zonal Statistics as Map Viewer function and ArcGIS API for Python - Zonal Statistics As Table as Portal tool in ArcGIS Pro and ArcGIS REST API Process multidimensional zone and value rasters The zonal statistics tools and function can now process both multidimensional zone and value rasters. This capability is enabled by checking the parameter Process as multidimensional, as shown in the example below. These tools are also available from the Multidimensional ribbon, which was introduced in ArcGIS Pro 2.5. The zonal operation occurs slice-by-slice between the slices of the zone raster and the slices of the current variable from the value raster. See How the zonal statistics tools work to learn more about the multidimensional processing in these tools. Multidimensional zone and value input rasters with the resulting zonal statistics raster are shown. This capability can be accessed from: ArcGIS Pro 2.6 Spatial Analyst and Image Analyst extensions: - Zonal Statistics as geoprocessing tool and function - Zonal Statistics as Table as geoprocessing tool ArcGIS Image Server 10.8.1: - Summarize Raster Within as Map Viewer tool, Portal tool in ArcGIS Pro, ArcGIS API for Python and ArcGIS REST API - Zonal Statistics as Map Viewer function and ArcGIS API for Python - Zonal Statistics As Table as Portal tool in ArcGIS Pro and ArcGIS REST API See what else is new with multidimensional raster analysis from this blog post: Let’s do data science III: Multidimensional Raster Analysis in ArcGIS Pro New output table schema A new table output schema is available for the Tabulate Area and Zonal Histogram tools with ArcGIS Pro 2.6 Spatial Analyst extension, which facilitates further query and analysis on the output tables. In the Tabulate Area tool, you can now choose between representing the input classes as rows or as fields in the output table with the Classes as rows in output table parameter. Similarly, to improve analysis workflows, in the Zonal Histogram tool, you can represent the input zones either as rows or as fields, using the new Zones as rows in output table parameter. Create histogram  The Zonal Histogram tool in ArcGIS Pro 2.6 Spatial Analyst extension can now create histogram graphs. Just note that the zonal histogram graph is not generated by default. In order to create the graph, first check the Zones as rows in output table parameter and specify an output graph name. Also, do not forget to check out the What’s New for Spatial Analyst in ArcGIS Pro 2.6 to know about everything that is new with the Spatial Analyst tools. What else is new in ArcGIS Image Server? The Zonal Statistics As Table tool is now available in ArcGIS Image Server as a Raster Analysis portal tool and as an ArcGIS REST API task to calculate the values of a raster within the zones of another dataset and report the results to a table. To learn more about what’s changed in ArcGIS 10.8.1, see What’s new in ArcGIS Image Server. ... View more

The 2020 Esri User Conference (July 13-16) is almost here, and we are going virtual! So, if you haven’t already, register to join the world’s largest, virtual GIS conference and be able to access the plenary, technical sessions, and many other presentations. We here on the Spatial Analyst team are excited to show you what we’ve been working on since last summer.  If you do any kind of raster or imagery analysis, or work with multidimensional data, look at the guide below to plan out your time with us. Streaming Live Plenary Session Join us for three inspiring days of thought-provoking Plenary Session content. Don’t miss out on the brand-new Interactive Suitability Modeler, which will be covered in the Monday, July 13, 10:00 am – 12:00 pm PDT timeslot. Streaming Live Technical Sessions Take advantage of this unique learning experiences and check out the following technical workshops with live Q&A: Distance Analysis: Identifying Optimal Paths Using Rasters Tuesday, July 14, 7:30 am – 8:30 am PDT Prediction, Trend, and Change Analysis of Multidimensional Raster Data Tuesday, July 14, 1:40 pm – 2:40 pm PDT Spatial Analysis and Data Science: Q&A with the Product Team Wednesday, July 15, 2:50 pm – 3:50 pm PDT You can find out more about these and all other sessions from the full schedule. You can also filter the schedule based on your interest or level to find out the sessions you would like to attend. If you are interested in sessions related to Spatial Analysis and Data Science, don’t forget to check out this blog. On-Demand Sessions The following on-demand sessions can be accessed starting on July 13 th  for all registered attendees. You can view the recordings at your convenience. Spatial Analyst: An Introduction (ID 11020) – Technical Workshop The New Interactive Suitability Modeler (ID 11053) – Demo Finding the Best Locations Using Suitability Modeling (ID 11023) – Technical Workshop Doing More with the New and Improved Distance Tools in Spatial Analyst (ID 11039) – Demo Interpolating Surfaces in ArcGIS (ID 11027) – Technical Workshop Hydrological Analysis Tools in ArcGIS (ID 11032) – Technical Workshop GIS Techniques for Floodplain Delineation (ID 11050) – Demo Working with Multidimensional Data in ArcGIS (ID 11036) – Technical Workshop ArcGIS Image Server: Raster Analytics (ID 10393) – Technical Workshop ArcPy: Working with Raster Data (ID 11192) – Technical Workshop Custom Analysis Using Raster Cell Iterator in ArcPy (ID 11041) – Demo As you are attending the sessions, make sure to give us your feedback, so that we can improve them for the next round. We would also like to hear from you while you are attending the conference, so visit us virtually at the Esri UC 2020 expo in the Spatial Analysis and Data Science showcase to learn more about the ongoing projects, or if you have questions. You can also follow our Spatial Analyst Resources blog page to get updates throughout the year. Download the Spatial Analyst UC 2020 Workshops and Demos List. Enjoy the conference! ... View more

The Spatial Analyst team has made some important improvements in the Distance toolset for ArcGIS Pro 2.5. We have significantly improved the precision of the geoprocessing tools in the Distance toolset and the corresponding raster functions. We have also provided additional capabilities which let you answer some new “how much/how many/how far” questions. These improvements allows us to remove the strong distinction between the “Euclidean” and “Cost” Distance tools. The number of tools has been reduced from 17 to 6. The new user experience is now simplified to: 1. Enter Sources 2. Do you have barriers? 3. Do you have a surface? 4. Do you have a cost surface? 5. Do you need to consider direction of motion and other source characteristics? The new toolset organization is shown in Figure 1 below. The new tools are: Distance Accumulation Distance Allocation Optimal Path As Line Optimal Path As Raster Optimal Region Connections Figure 1 - Organization of the Distance toolset at ArcGIS Pro 2.5 The original Distance tools that you are familiar with are available in the  Legacy sub-toolset. The Spatial Analyst team recommends that you move your Euclidean and Cost Distance analysis work to the new tools. Why should I use the new tools? At ArcGIS Pro 2.5, we’ve uniformly adopted a new algorithm [4] for all distance mapping tools. This algorithm removes the distortion in outputs caused by using a network model of cell connectivity, as shown in Figure 2 on the left below. This has the following benefits: Cost distance is now measured the same way in all directions. An important special case is cost distance with a constant cost surface now produces the same output as the Euclidean distance tools. Surface distance over a digital elevation model can now be precisely computed. Paths around barriers are now followed precisely, as shown in Figure 3. Figure 2- Output of legacy Cost Distance tool using a constant cost input with barriers encoded as ndoata, along with output of legacy Cost Path As Polyline tool. Figure 3 – Output of new Distance Accumulation tool with barrier inputs, along with output of new Optimal Path As Line tool. At ArcGIS Pro 2.4, we introduced this improvement in a limited context: Euclidean distance with barriers. ArcGIS Pro 2.5 generalizes and extends this work. What else is new? Along with this fundamental change to the behavior of the cost distance algorithm, several additional capabilities have been added. You can use geodesic distance with the Distance Accumulation, Distance Allocation, and Optimal Region Connections  tools. Figure 4- Geodesic flight paths from some Northern European airports to LAX, but avoiding paths over southern Greenland. As a side effect of the improved algorithm, you can now create buffer regions whose distance is measured over the surface of a digital elevation model. This isn’t really a separate capability, but it’s important enough to mention explicitly. In the example below, the study area is Lake Wakatipu near Queenstown, New Zealand – a high relief area. Notice how, in such areas, the output of the Distance Accumulation tool (gray gradient) with a maximum accumulation cutoff of 1500m “pulls away” from the output of the vector Buffer tool that used a buffer distance of 1500m. This surface-aware distance computation can be done in either planar or geodesic mode. Figure 5 – Comparison of planar (blue) and surface distance (gray gradient) buffers around Lake Wakatipu in the Queenstown area of New Zealand. Both buffers extend to a distance of 1500m, but the gray gradient output measures distance over the surface of a DEM instead of distance in the plane of the data’s projected coordinate system. Using the new Optimal Path as Raster tool, you can count how many least cost paths pass through a cell en route from some destinations to a source. Figure 6 – Optimal Path As Raster output from ridgeline hiking trail in SW to access road in NE. Green is less intense usage, red is more intense. Using the new source_location output from Distance Accumulation and Distance Allocation, you can do intensity mapping of the boundary of sources, showing where most cost paths in the study area would end up, without plotting all cost paths. The RasterCellIterator python object, also new at ArcGIS Pro 2.5, makes this type of intensity calculation very easy to perform. See A quick tour of using Raster Cell Iterator to know more about it. Figure 7 – Lake Wakatipu, New Zealand. Hot spots (red and blue fuzzy dots), computed from Distance Accumulation output, show locations of most intense drainage into lake from surrounding watershed. Which geoprocessing tools and raster functions should I start using? For each legacy distance tool that you are currently using, table 1 shows you which new tool to switch to. Table 1 – Upgrade guide for distance mapping tools If you used Then switch to Cost Distance Distance Accumulation Cost Backlink Distance Accumulation with back direction output Cost Allocation Distance Allocation Euclidean Distance Distance Accumulation Euclidean Allocation Distance Allocation Euclidean Direction Distance Accumulation with source direction output Path Distance Distance Accumulation Path Backlink Distance Accumulation with back direction output Path Allocation Distance Allocation Cost Path Optimal Path As Raster Cost Path As Polyline Optimal Path As Line Cost Connectivity Optimal Region Connections For new workflows, the back direction raster replaces the back link raster. The Optimal Path as Raster/Line tools no longer accept a backlink raster as input (they will still accept a flow direction raster). If you were using the backlink raster in a workflow that involves something other than the Cost Path or Cost Path As Polyline tools, then the Spatial Analyst team would like to hear from you. The Euclidean distance blog mentioned above discusses the backdirection raster in more detail.If you used the  maximum_distance  parameter in the legacy cost tools, you should now use the Maximum Accumulation parameter in the “Characteristics of the sources” parameter group. These are the new raster functions. Distance Accumulation Distance Allocation The functions have the same parameters as the tools, with an important difference: the functions optionally emit multi-band outputs. This avoids having to run these (possibly expensive) global functions twice to get closely related outputs (like accumulated cost surface and back direction raster). Also, the Least Cost Path raster function has been updated to use the Distance Accumulation tool behind the scenes. Finally, although not directly related to these enhancements, your advanced use of the Distance toolset needs to consider if direction of motion between sources and destinations is important. If so, please check out this blog on Creating accumulative cost surfaces using directional data. References 1) Goodchild, M. F. (1977). An Evaluation of Lattice Solutions to the Problem of Corridor Location. Environment and Planning A. 9. 727-738. 10.1068/a090727. 2) Sethian, J. A. (1997). Tracking Interfaces with Level Sets: An “act of violence” helps solve evolving interface problems in geometry, fluid mechanics, robotic navigation and materials sciences. American Scientist Vol. 85, No. 3 (MAY-JUNE 1997), pp. 254-263 3) Sethian, J. A. (1999). Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science (2nd edition). Cambridge University Press. 4) Zhao, H. (2005). A fast sweeping method for eikonal equations. Mathematics of computation, 74(250), 603-627. The original blog post has been written by James TenBrink, and is available here, What’s New in the Spatial Analyst Distance Toolset in Pro 2.5. James TenBrink is a senior software engineer in the raster analysis group at Esri. He has a Bachelor of Science degree in Computer Science from Hope College in Holland, Michigan, and a Master of Science degree in Computer Science from Rensselaer Polytechnic Institute in Troy, New York. He has been with Esri 28 years and has had an office in almost every building, on almost every floor, of the Esri campus. ... View more

The following question was recently posted to GeoNet: “I want to add wind direction and strength to a cost distance surface. How do I create a raster surface which incorporates these two factors…?“ In this blog post, the spatial analyst team responds to this user’s question. The Path Distance tool in the Spatial Analyst toolbox can do part of this. It can be used to create an accumulative cost surface from costs that are determined by the direction of the wind. Unfortunately, Esri doesn’t yet have a way to make cost of motion vary based on both the direction and speed of the wind. Also, when using Path Distance, it is extremely important to know in advance whether you intend to move from source cells to other cells, or vice versa. You will see that changing this intent changes the result dramatically. If you decide to use Path Distance to incorporate only wind direction into your accumulative cost surface, here is what we recommend: You can encode wind direction azimuths (clockwise 0-360 from north, both 0 and 360 are treated as “north”) in the input horizontal raster. In figure 1 below, a 10 x 10 horizontal raster is shown. It has a constant cell value of 90 and represents wind blowing to the east with some uniform speed over the study area. You should not use wind speed as the friction cost surface, because that cost is paid regardless of which direction you travel through a cell. In fact, Path Distance doesn’t need a cost friction surface at all (nor do the other Path Distance tools: allocation, backlink). Figure 1-A 10 x 10 horizontal direction raster used as input to Path Distance. Each cell has a value of 90 (wind blowing from west to east). Cell centers are shown in yellow, along with a vector field renderer (blue arrows). Case 1: Motion from source Let’s first assume that: You are sitting on the single source point (the selected blue point in the center of the raster in figure 2). That its easiest to move with the wind and impossible to move either against it or at right angles to it. That you want to calculate accumulated cost from your source cell to every other cell in the study area. For this case, you could use a horizontal_factor parameter value of Horizontal Forward Function AND (this is important) a source_direction parameter value of “FROM_SOURCE”. The result is shown in figure 2. You can see that your accumulated cost surface is only defined in areas that can be reached travelling with (or somewhat with) the wind. Figure 2- The results of using Path Distance with the horizontal raster from figure 1 and the horizontal forward function. The single source is the selected point in the center and we want to know how expensive it is to reach other cells in the study area. The accumulated cost surface shows that we are only allowed to move with the wind, and its easiest to move directly east. For this result, we use the horizontal forward function AND a source_direction parameter value of motion from source. Lets go into a bit of detail on how Path Distance turns wind direction into a travel cost (also called a weight). We zoom in on the single source cell and its 8 neighbors: Figure 3 - Detail of source cell (marked with an S) and its 8 neighbors. Each cell has a wind direction. From that source, we can move in 8 directions to reach a neighbor cell and we need to figure out what the step cost for that move will be. Figure 4 - You can move 8 ways from the source cell to its neighbors. Each choice makes a different angle relative to each cell's wind direction. In our case, the only thing that affects the step cost is wind direction. Each of those 8 step directions makes some angle with the preferred moving direction, which in this case is the wind direction. HRMA calculation We call this angle the horizontal relative moving angle (HRMA) because it is your direction of motion relative to an azimuth value stored in your horizontal raster. Each horizontal factor function turns this HRMA into a weight. In this case, we’ve used the Horizontal Forward Function. See this help topic for more details on HRMA. The HRMA and horizontal factor function are evaluated twice for each of the 8 possible steps: once for the wind direction at the source cell and once for the direction at the neighbor cell. The two results are then averaged to get the horizontal weight to be used when calculating the cost of a step into the neighbor cell. Case 2: Motion To Source Now let’s assume that the single source point represents a location that you want to reach from other points in the study area. In this case, all inputs to Path Distance remain the same, except you now choose a source _direction parameter value of “TO_SOURCE”. Figure 5 shows the result. As you can see, the results are very different. Figure 5 -The result of using Path Distance to find accumulated cost of travelling to the single source point from other cells in the study area: source_direction is set to “ motion to source”. All other inputs are the same as for figure 2. Why are these results so different? In both cases, the accumulative cost surface is grown outward starting from the source and visiting all other cells; but in the TO_SOURCE case, the calculation of the HRMA is different, so the output of the horizontal factor function applied to that HRMA will also be different. In the TO_SOURCE case the HRMA is the geometric supplement of the HRMA calculated in the FROM_SOURCE case, as shown in figure 6. HRMA value changes based on direction of motion To summarize: Path distance can adjust the cost of motion through a cell based on input data that describes, for each cell, the easiest direction to move through the cell. This calculation can produce very different results, based on whether you want to move from a source to other cells, or from other cells back to the source. We haven’t explicitly talked about the vertical raster and vertical factor functions, but the reasoning process is basically the same as described above. However, instead of accounting for horizontal influences, the vertical factor determines the cost to overcome the changes in elevations (the slopes) between the cells. The original blog content has been written by James TenBrink and Kevin Johnston and is available here: https://www.esri.com/arcgis-blog/products/arcgis-pro/analytics/creating-accumulative-cost-surfaces-using-directional-data/  James TenBrink is a senior software engineer in the raster analysis group at esri. He has a Bachelor of Science degree in Computer Science from Hope College in Holland, Michigan, and a Master of Science degree in Computer Science from Rensselaer Polytechnic Institute in Troy, New York. He has been with esri 28 years and has had an office in almost every building, on almost every floor, of the esri campus. Kevin Johnston has been a Product Engineer on the Spatial Analyst development team for over 29 years. He has degrees in Landscape Architect from Harvard and in environmental modeling from Yale. At Esri, Kevin’s current focus is developing suitability and connectivity tools. He hopes the tools that he works on can help users make more informed decisions.   ... 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Currently, Euclidean Distance Mapping geoprocessing tools can be used to assign distance properties to raster cells. Example applications include distance from runways used as part of an airport noise model, or distance from streams used as a criterion layer in a habitat suitability model. From ArcGIS Pro 2.4 and ArcMap 10.7.1, you can now  use the Euclidean distance tool to consider an important use case: generating precise shortest paths around barriers. You can also use barrier-aware distance and allocation maps  to replace their corresponding cost distance versions as input layers in suitability models. Consider these related use cases posted to geonet: “Aim: To determine the shortest path from a nest to marine foraging locations for an individual bird. The bird is restricted to traveling along water only (i.e., freshwater and ocean).” [1] “I have been running some least cost path analyses on manatee GPS telemetry data using ArcGIS 9.3.1.  The goal of this analysis is to create the shortest path between points without the path crossing land.” [2] Prior to ArcGIS Pro 2.4/ArcMap 10.7.1, the only way to plot shortest paths around barriers was to use the Cost Distance (CD) tool using a constant cost surface with a small, positive, non-zero cost for the areas where movement was allowed and either a high cost or nodata where movement was prohibited. As the geonet posters point out, the cost paths obtained from this approach are unsatisfactory. To drive this point home, lets recreate the seabird flight paths (using completely fake nesting and feeding site data) using the CD + constant cost surface approach. The study area is Vancouver, British Columbia. Figure 1: Sea bird over-water flight paths created using the CostDistance + constant cost surface approach. The Cost Distance sources are the feeding sites in the northwest corner and the Cost Path As Polyline destinations are the nesting locations in the southeast corner. Goodchild [3] discusses why these paths deviate. The underlying cost distance algorithm is based on an 8-way connected graph, which restricts the directions that can be considered, and the absence of meaningful variation in the cost surface (other than barriers) means that the CostPath/CostPath As Polyline operation doesn’t know which of many equal length routes to choose from. At 2.4/10.7.1, we can do better, as shown below in figure 2 in green, using enhancements to the Euclidean Distance (ED) and Cost Path As Polyline GP tools. Figure 2: Sea bird over-water flight paths (in green) created using barrier-aware Euclidean Distance and Cost Path As Polyline tools. Figure 3: Detail showing Euclidean distance (in green) and Cost Distance (in blue) flight paths. The CD paths in this example are only about 1% longer than the ED paths, but their deviations from the geometric shortest paths are significant. There is also a geodesic option for ED, but we haven’t used it in this example because we wanted to compare results with CD. Creating Shortest Paths With Barriers To generate these improved paths, use the following workflow: 1. Create a barrier dataset. It can be any kind of feature class or a raster. In the example above, the barrier dataset is a polyline feature class representing coastlines. If it’s a raster, DATA cells act as barriers. 2. Use the Euclidean Distance tool instead of the Cost Distance tool. Specify values for the new optional input parameter “Input raster or feature barrier data” and the new optional output parameter “Out back direction raster”, as shown below: Figure 4: Euclidean distance tool user interface 3. Use the Cost Path As Polyline tool to create the paths from the destinations (Nesting sites) back to the sources (Feeding Sites). This tool has been enhanced to accept the new back direction output from Euclidean distance: Figure 5: Cost Path As Polyline tool user interface Simple! The Back Direction Raster The new back direction raster, created by the Euclidean toolset, encodes a floating point azimuth value in the range (0.0, 360.0] degrees, with zero itself reserved for source cell locations. This azimuth points in the direction of the shortest path as it passes through the cell. This is different than the “Output direction raster” parameter, which uses the 1-360 azimuth value (rounded to an integer) to point directly at the closest source cell (the cell where the shortest path will eventually end). You can always ask for the back direction raster to be generated whether or not barriers are specified. This provides a convenient way to use Cost Path As Polyline tool to identify which source cell is closest to a given destination cell (it’s the cell on which the polyline ends) even if you’re not interested in barriers or specific pathways. Locating this cell was difficult in earlier versions of the Euclidean toolset: For planar Euclidean distance, you could have done some math with the distance and direction outputs, but that wouldn’t work for geodesic output. Barrier-aware Distance and Allocation Outputs So far, we’ve emphasized the use of the new functionality in generating better shortest paths. For this application, the most important output was the new back direction raster, which gave Cost Path as Polyline the information it needed to plot those paths step-by-step. The distance and allocation maps may also be directly useful. As an example, compare the euclidean and cost distance versions of the barrier-aware distance map in figures 6 and 7 below. The euclidean version avoids the distortions present in the cost distance version. Figure 6: Barrier-aware Euclidean distance map used to compute the sea bird over-water flight paths. Figure 7: Cost distance map produced from encoding coastline as nodata into a constant cost surface Use of Distance and Allocation Outputs in Suitability Models Let’s repeat that the difference between CostDistance and Euclidean Distance with barriers is that the former performs its computation using a network distance metric and the latter uses a euclidean distance metric. This is something to be aware of when using the outputs of cost distance as layers in a suitability model. It is probably the case that some other layers in that model assume a Euclidean distance metric, so the mixture of distance metrics in a single suitability model will introduce some amount of error into the model results. For example, if both the Cost allocation and Euclidean allocation datasets shown in the figure below were used in the same model, then the classification of cells near an allocation boundary becomes more uncertain, as shown below. Figure 8: Comparison between cost allocation and barrier-aware euclidean allocation zones. Note the zig-zag distortion in the cost allocation boundary. The only reason to mix distance metrics like this is because, up until now, there hasn’t been a good alternative. Also, the distance distortion itself is bounded by a factor of about 1.1 ([3, Table 2], so that may be good enough for many applications. However, now we have a better alternative for the special case of barriers on a constant cost surface. For a distortion free solution for more general cost distance problems, we’ll have to wait for an upcoming ArcGIS platform release. Summary This blog has shown you how to generate shortest paths around barriers, using the versions of the Euclidean Distance and Cost Path as Polyline tools available in ArcGIS Pro 2.4 and ArcMap 10.7.1. Also, if you are using cost distance tools with a constant cost raster (containing some nodata cells) to generate inputs for a suitability model, you should consider using the barrier-aware euclidean tools instead. References [1] Sea bird geonet posting https://community.esri.com/thread/109625 [2] ManateeGISDB geonet posting https://community.esri.com/thread/97784-least-cost-path-anomalies [3] Description of length and direction distortions in current cost distance implementation Goodchild, M. F. (1977). An Evaluation of Lattice Solutions to the Problem of Corridor Location. Environment and Planning A. 9. 727-738. 10.1068/a090727. [4] Readable introduction to barrier-aware euclidean distance method Sethian, J. A. (1997). Tracking Interfaces with Level Sets: An “act of violence” helps solve evolving interface problems in geometry, fluid mechanics, robotic navigation and materials sciences.  American Scientist  Vol. 85, No. 3 (MAY-JUNE 1997), pp. 254-263 The original blog is available at https://www.esri.com/arcgis-blog/products/spatial-analyst/analytics/doing-more-with-euclidean-distance-barriers-and-paths/ The content of the blog has been prepared by James TenBrink, a senior software engineer in the raster analysis group at esri. He has a Bachelor of Science degree in Computer Science from Hope College in Holland, Michigan, and a Master of Science degree in Computer Science from Rensselaer Polytechnic Institute in Troy, New York. He has been with Esri 28 years and has had an office in almost every building, on almost every floor, of the Esri campus. ... View more

The 2019 Esri User Conference (July 8-12) is almost here. We here on the Raster Analysis team are busy practicing and polishing our presentations and are looking forward to showing you what we’ve been working on since last summer.  If you do any kind of raster or multidimensional analysis, please look at the guide below to help you plan out your time with us. Expo hall Visit the UC Expo to meet with us and with several amazing exhibitors. The UC Expo Hall has extended hours on Thursday this year, meaning more time is available for you to come by and get your questions answered. Tuesday, July 9 th          9:00 AM – 6:00 PM Wednesday, July 10 th   9:00 AM – 6:00 PM Thursday, July 11 th       9:00 AM – 4:00 PM Technical Workshops and Demos To help target your areas of interest, we’ve categorized workshops and demos relevant to raster analysis with Spatial Analyst into different groups.  In each group, you will find the general purpose of the sessions within the group, followed by the specific session titles in chronological order. Repeat presentations are also listed, in case you are not able to attend the first offering. Finally, remember to check out the Road Ahead sessions for a peek into the future. 1.     Raster Analysis The ArcGIS platform offers many capabilities for raster analysis, data management, and spatial modeling.  Attend the introductory sessions to get a broad overview of the capabilities for raster analysis.  If your interest is more for specific types of analysis, such as suitability modeling, cost distance modeling, or hydrological modeling, we offer many sessions more specifically focused on those areas. Raster Analysis Tuesday, July 9 10:00 AM Spatial Analyst: An Introduction SDCC – Room 30 B 10:00 AM Hydrologic Analysis and Applications in ArcGIS SDCC – Room 33 C 11:15 AM Working With Living Atlas Data: Habitat Modeling SDCC – Expo Demo Theater 11 1:00 PM Interpolating Surfaces in ArcGIS SDCC – Room 33 C 1:15 PM Map Algebra: Getting Started SDCC – Expo Demo Theater 10 1:15 PM Working With Living Atlas Data: Elevation Analysis SDCC – Expo Demo Theater 11 4:00 PM Finding the Best Locations Using Suitability Modeling SDCC – Room 33 C Wednesday, July 10 8:30 AM Cost Distance Analysis: Identifying the Best Path SDCC – Room 30 C 11:15 AM Surface Interpolation in ArcGIS SDCC – Expo Demo Theater 10 1:15 PM Bobcat Suitability Modeling SDCC – Expo Demo Theater 10 2:30 PM Spatial Analyst: An Introduction SDCC – Room 32 A/B Thursday, July 11 2:30 PM Finding the Best Locations Using Suitability Modeling SDCC – Room 15 A 4:00 PM Cost Distance Analysis: Identifying the Best Path SDCC – Room 17 A 4:00PM Interpolating Surfaces in ArcGIS SDCC – Room 15 A 4:00 PM Hydrologic Analysis and Applications in ArcGIS SDCC – Room 14 A Raster Data Management Tuesday, July 9 10:00 AM Basics of creating a Mosaic Dataset SDCC Demo Theater 2 Thursday, July 11 11:15 AM Managing and Serving Elevation and Lidar Data SDCC Demo Theater 2 2. Raster Analysis using Python The integration of Map Algebra with Python via the Spatial Analyst ArcPy module offers a broad range of capabilities for raster analysis, data management and automation workflows. With the arcgis.raster module in the ArcGIS Python API, you can perform raster analysis on very large raster datasets in the server environment. Various Python packages also let you extend your analytical power though custom tools, as well as machine and deep learning. Tuesday, July 9 1:15 PM ArcGIS API for Python: Integrating Machine Learning and Deep Learning SDCC – Expo Demo Theater 08 2:30 PM Python: Raster Analysis SDCC – Room 30 A Wednesday, July 10 10:00 AM ArcGIS API for Python: Imagery Analysis SDCC – Room 16 A 4:00 PM Python: Raster Analysis SDCC – Room 30 A 3.     Raster Analytics using ArcGIS Image Server The Raster Analytics capabilities offered by ArcGIS Image Server can be used to enable cloud computing, which applies distributed computing and distributed storage technology to analyze and process large raster data collections.  The following sessions will introduce the capabilities of this product, describe the architecture, detail the deployment options, and identify the performance benefits offered by distributed processing. Tuesday, July 9 8:30 AM ArcGIS Enterprise: Raster Analytics in Image Server SDCC – Room 33 A/B 1:00 PM ArcGIS Enterprise: Managing and Serving Imagery in the Cloud using ArcGIS Image Server SDCC – Room 15 B Wednesday, July 10 10:00 AM ArcGIS Enterprise: Sharing Imagery SDCC Demo Theater 2 12:30 PM Working with OGC WCS Services SDCC Standards & Interoperability Spotlight Theater 4:00 PM ArcGIS Enterprise: Managing and Serving Imagery in the Cloud using ArcGIS Image Server SDCC – Room 05 B Thursday, July 11 8:30 AM ArcGIS Enterprise: Deploying Distributed Raster Analytics SDCC – Room 05 A 2:30 PM ArcGIS Enterprise: Raster Analytics in Image Server SDCC – Room 08 4.     Scientific Multidimensional Data Analysis The ArcGIS platform has great capabilities for the ingesting, management, visualization, analysis, and sharing of scientific multidimensional data.  If you would like to use netCDF, HDF or GRIB data for doing spatial and temporal analysis, the following sessions will be of interest to you. Wednesday, July 10 2:30 PM Analyzing Multidimensional Scientific Data in ArcGIS SDCC – Room 33 C Thursday, July 11 10:00 AM Analyzing Multidimensional Scientific Data in ArcGIS SDCC – Room 10 10:00 AM Scientific and Multidimensional Raster Support in ArcGIS SDCC – Room 05 A 10:30 AM Working with Scientific Data in ArcGIS Platform SDCC – Expo – Open Platform: Standards & Interoperability Spotlight Theater 5.     The Road Ahead Come join us for the Road Ahead sessions to find out what enhancements have been made in spatial analytics and what we are planning for the upcoming year, especially in the areas of machine learning, big data and real time analysis. Tuesday, July 9 4:00 PM ArcGIS Enterprise: The Road Ahead SDCC – Ballroom 06 C 4:00 PM ArcGIS Pro: The Road Ahead SDCC – Ballroom 06 B Wednesday, July 10 8:30 AM ArcGIS Enterprise: The Road Ahead SDCC – Ballroom 06 C 10:00 AM Spatial Analysis and Data Science: The Road Ahead SDCC – Ballroom 06 D Thursday, July 11 10:00 AM Spatial Analysis and Data Science: The Road Ahead SDCC – Ballroom 06 D 1:00 PM ArcGIS Pro: The Road Ahead SDCC – Ballroom 06 D Friday, July 12 9:00 AM ArcGIS Pro: The Road Ahead SDCC – Ballroom 06 F Special Interest Group Meetings Special Interest Groups – or SIGs—are communities within multiple organizations with a shared interest in advancing a specific area of knowledge, learning, or technology. Every year, the Esri UC supports dozens of SIG meetups, both technical and non-technical. SIGs are a great way to network with your peers and learn something new. We encourage you to check out and participate in any of several SIG meetups highlighted below. Sunday, July 07 7:30 am – 5:00 pm Imagery Summit Marriott – Marina Foyer 8:30 am – 5:00 pm GeoSpatial Deep Learning SDCC – Room 32 A/B 9:00 am – 4:30 pm Esri GIS Hydro Meeting Marriott – Santa Rosa Tuesday, July 9 11:30 am – 12:30 pm Ocean SIG SDCC – Room 26 A Thursday, July 11 11:30 am – 12:30 pm Weather and Climate SIG SDCC – Room 17 A Summary, and come see us! Additional details on these sessions listed above, and much more, is available in the full agenda. Be sure to download the Esri Events Mobile App, too!   (UC content coming soon) Please come by the Spatial Analyst island within Spatial Analysis & Data Science at the Product Showcase in the Exhibit Hall during the week (Tuesday and Wednesday from 9:00 – 6:00, and Thursday from 9:00 – 4:00.  We’d love to hear about the work you do, any difficulties you have encountered, and any ideas you might have to make our software suit your needs even better. ***Note: All listings subject to change; not every paper or event may be presented here. For the list of the all sessions visit the full agenda page. ... View more

ArcGIS Pro 2.3 and ArcMap 10.7 Spatial Analyst tools now support a new environment, the Cell Size Projection Method, to control the calculation of the output raster cell size when datasets are projected during tool execution. To learn more about projections, see Coordinate systems, projections, and transformations. In previous software versions, the cell size was converted using a linear unit conversion when projecting from a Projected Coordinate System (PCS) to another PCS or an angular unit conversion when going from Geographic Coordinate System (GCS) to another GCS. So, projecting a 30 meter raster dataset from UTM zone 10N to WGS84 Web Mercator would continue to use 30 meter as the output cell size. When projecting from GCS to PCS or vice versa, previous versions took an average cell size based on the ratios of diagonal lengths in source and destination projections. Therefore, this method, currently exposed as the ‘convert units’ method simply copied the cell size from input to output, changing units if necessary. This approach has some limitations: It does not account for distortion when projecting from one PCS to another PCS (1 projected meter in, say, a UTM zone at a given location may not cover the same ground distance as 1 projected meter at the same location in an Albers projection). When computing the ratios of diagonal lengths, the behavior of the projection at only the four corner points was used. This may have introduced excessive distortion, depending on the projection and the extent. The two new methods: ‘preserve resolution’ and ‘center of extent’ both are intended to calculate a projected cell size that doesn’t change the ground distance of the cell, thus avoiding unnecessary resampling during an implicit projection operation happening as a Spatial Analyst tool or a feature to raster conversion tool executes. You should use the ‘center of extent’ method when you know the geographic location where your cell size is most accurate. If you use this method, you can use a raster with a small extent in that same area as the cell size source or create a temp raster ‘around’ that location and use that as the cell size source. If you are unsure, use the ‘preserve resolution’ method. To learn more about the three cell size projection methods see How the Cell Size Projection Method environment setting works. If you do not know at which specific geographic location your cell size is best but want to improve on the ‘convert units’ method when a projection is involved while working with Spatial Analyst tools, you can use the ‘preserve resolution’ method. Why use the resolution preserving cell size projection method? In the ‘preserve resolution’ method the same number of square cells as are in the original extent are preserved in the projected extent. The output cell size is calculated based on the ratios of the areas of the projected extent to the original extent. This approach is based on how Esri software currently chooses a cell size when moving from a camera image coordinate system to a geodetic (GCS or PCS) system. This method calculates the average size of a square cell more accurately for all combinations of GCS and PCS than the default ‘convert units’ method. If the areas of the original rectangular extent and (shape preserving) projected extent are A 0 and A 1 , then the areas of the square cells, respectively, are, ca 0  = A 0 /n and ca 1  = A 1 /n Since the number of cells remains constant for both cases, the ratio of the area of extent to the area of the square cell are equal, A 0 /ca 0  = A 1 /ca 1 Figure 1: Cell size projection using the new ‘preserve resolution’ method So, the output cell area is ca 1  = (A 1 /A 0 ) * ca 0 and the output cell size is CellSize_projected = √((A 1 /A 0 ) * ca 0 ) In this method, the cell size conversion factor is, √(A 1 /A 0 ) Let’s look at two examples to see how the ‘preserve resolution’ method picks a better output cell size than ‘convert units’. Example 1: Let’s take an elevation raster (R_input), located in Vermont (Figure 2), in a PCS (NAD_1983_StatePlane_Vermont_FIPS_4400) and project it to another PCS (WGS 1984 World Mercator) using the default cell size projection methods, ‘convert units’ (R_out_CU) and ‘preserve resolution’ (R_out_PR). Here both the spatial references are shape preserving, where the input spatial reference is suitable at a state level and the output spatial reference is suitable for the entire world. We will then compare the geodesic distance between the cell centers to determine which method preserves the geodesic distance more accurately. Figure 2: Location of the input raster (R_Input), its cell boundaries and cell centers. Before we compare the geodesic distances between the two methods, lets understand what are the different ways a raster is projected in ArcGIS Pro. When you add a raster to the map in ArcGIS Pro, the spatial reference of the map becomes the same as the spatial reference of the raster. For example, if the first layer added has a NAD_1983_StatePlane_Vermont_FIPS_4400 PCS, the map will have the same spatial reference, and all other layers will project on the fly to match this spatial reference. This on-the-fly raster projection is meant for a richer display experience but does not preserve the raster structure (different cells can have on-the-fly-projected different sizes, rotations, and distortions). However, when you project a raster using the Project Raster tool or specifying the Output Coordinate System environment of a geoprocessing tool, the raster is actually  projected into a new raster structure (every cell is an identical rectangle in the output spatial reference, with sides parallel to the coordinate system axes). When measuring geodesic distance for the comparison of the cell size projection methods, it is recommended to use the actual projected raster instead of the raster that is projected on the fly. In figure 3, the cell size of the input raster, R_input is 30 meters. This is approximately the same as its geodesic ground distance, which you can find out using the Measure tool in ArcGIS Pro. Figure 3: Cell boundaries of R_input, cell centers of R_input, R_out_CU, R_out_PR and the geodesic distance between the cell centers. To overlay the points, the cell centers have been projected back to the spatial reference of R_input for comparison. When the raster is projected using the ‘convert units’ methods, the projected cell size of the output raster, R_out_CU remains 30 meters, but 30m in WGS 1984 World Mercator is a much smaller distance on the ground, 21 meters. So, raster projection using the ‘convert units’ method has unnecessarily increased the resolution of the output raster. If we were projecting in the opposite direction (starting from WGS 1984 World Mercator), then we would have lost a significant amount of raster data. When the same raster is projected using the ‘preserve resolution’ method, its cell size becomes 42 meters and the geodesic ground distance remains 30 meters, which is the same as the geodesic distance of the input raster. Example 2: In this example, we will project from a UTM zone to its adjacent zone, which can happen when mosaicking together many different DEMs for a larger (state sized) area. In figure 4, let’s take a raster (R_input2) in WGS 1984 UTM Zone 11N and project it to its adjacent zone WGS 1984 UTM Zone 12N, creating output (R_out_CU2) and (R_out_PR2) for ‘convert units’ and ‘preserve resolution’ respectively. From here we will again compare the geodesic distance between the cell centers to determine which method preserve the geodesic distance more accurately. Figure 4: Cell boundaries of R_input2, cell centers of R_input2, R_out_CU2, R_out_PR2 and the geodesic distance between the cell centers. To overlay the points, the cell centers have been projected back to the spatial reference of R_input2 for comparison. The cell size for the input raster, R_input2 is 30 meters, which is approximately the same as the geodesic ground distance in this area. When the raster is projected using the ‘convert units’ method, the cell size of the output raster, R_out_CU2 becomes 30 meters and the geodesic ground distance becomes approximately 31 meters. When the same raster is projected using the ‘preserve resolution’ method, its cell size becomes 28.99 meters, and the geodesic ground distance is 30 meters, which is the same as the input raster. In both the examples, the ‘preserve resolution’ method seems to preserve the geodesic ground distance between cell centers better than the ‘convert units’ method. Other combinations of input and output spatial references may, of course, show difference between the methods, but overall it is safe to say that the ‘preserve resolution’ Method is a better approach for preserving geodesic distance while projecting datasets. An important property of the ‘preserve resolution’ method is that the output cell size depends on the location of the dataset. The same combination of input and output spatial reference will yield a different output cell size if the input raster dataset is at a different geographic location. If you need to use one cell size for different raster datasets (for example, processing adjacent DEM tiles), specify one raster dataset to use as the cell size source while performing raster analysis for the tiles. Next time you use a Spatial Analyst geoprocessing tool or python command to: create an output with a spatial reference different from that of the input dataset, use input datasets with different spatial references, specify an analysis cell size using a dataset with a different spatial reference, pay attention to the output cell size and consider using either the preserve resolution or center of extent methods. By default, the projection method will do the ‘convert units’, which existed in previous versions of ArcGIS, but as we have seen, this method may unnecessarily increase or decrease the resolution of your valuable raster data. Additional resources Handling projections during analysis in Spatial Analyst How the analysis window is determined in Spatial Analyst This blog post has been prepared by James TenBrink, senior software engineer in the raster analysis group at Esri, and Sarmistha Chatterjee. Original post: https://www.esri.com/arcgis-blog/products/spatial-analyst/analytics/introducing-the-new-resolution-preserving-cell-size-projection-method/ ... View more