Module 6: Corridor Analysis

 Module 6: Corridor Analysis

In this weeks module we learned how to do corridor analysis, which considers a range of possible paths instead of a single optimal path. This is useful because the least-cost path is not always the most optimal path for what a person needs for the area. In scenario 4 we once again needed to do the conceptual steps before starting out analysis.

Step 1: State the Problem

Problem: Potential movement of black bears between the two protected national forest areas.

Goal: Create a suitability and corridor analysis of the black bear movement between the national forests.

Step 2: Break Down the Problem

Objective 1: Reclassify the roads into suitability classes.

Input dataset: Roads shapefile

Objective 2: Reclassify the slopes into suitability classes.

Input dataset: Elevation raster

Objective 3: Reclassify the Land cover into suitability classes.

Input dataset: Land cover raster

Objective 4: Invert suitability model

Input dataset: The reclassification models of the roads, elevation, and land cover.

Objective 5: Create a corridor analysis

Input: The cost path inputs for the two national forests.

Step 3: Explore Input Datasets

Once again this is from Model Builder within ArcGIS Pro which is helpful because it can do all the data for you without doing each step separately. Also, it lets you work through the problem before beginning the work. The image below is my flowchart for this scenario.

When I did the analysis, I did the corridor analysis. I made sure to get the least-cost path for both national forest polygons. First, I did the cost distance and cost path for both of the polygons. After changing the least-cost path to a polyline I then did the corridor analysis from Scenario 3. I used the corridor tool where I put in the two cost distance surfaces and inputs. There is no graduated color ramp, so I went to classify and switched it to three breaks and added in the threshold for each one. Similar to scenario 3 I times the minimum value by 1.05, 1.1, and 1.15. This showed the different areas and how they were less optimal as you went out. The image below shows the corridor analysis of the best path between the two national forests based on the criteria that was given to us in the beginning of the distance to the roads, elevation, and land cover.

When thinking about the data after the initial analysis there are more factors that I would see if we could get more information. First, I would possibly see if there were any criteria for the rivers in the area and what the black bears might want to be close to those areas as well. Another factor would be if there were any developments in the area that could disturb the bears and where they might want to go with the corridor analysis.

This scenario did have a couple of hiccups on trying to get the data to represent the best paths. Even though I did all the analysis correct I was only able to get one corridor analysis path. I tried different ways but it was only giving me one route each time. I am not sure the best way to fix this, but when doing the exercise it did say the Cost Distance, Cost Path, and Corridor Tools were being taken out and replaced with a newer one. This might help with getting the correct analysis in the future. At the same time, it is important to note that when doing the analysis it is important to take your time and try to understand what you are doing for each step. Lastly, make sure to name your outputs to where you can see which features are being represented and not to use the same name within the same geodatabase.

Module 6: Suitability Analysis

 Module 6: Suitability Analysis

In this weeks module we first focused on suitability analysis maps for a potential developer in Jackson County, Oregon. We needed to state the problem, break down the problem, and create a flowchart/Model Builder for this project. 

Step 1: State the Problem

Problem: How much land is suitable to build on.

Goal: Create a suitability map in raster form to find the areas that are suitable to build on.

Step 2: Break Down the Problem

Objective 1: Reclassify the land cover into suitability classes.

            Input dataset: Landcover raster

Objective 2: Reclassify the soils into suitability classes.

            Input dataset: Soils shapefile

Objective 3: Reclassify the slopes into suitability classes.

            Input dataset: Elevation raster

Objective 4: Reclassify the rivers into suitability classes.

            Input dataset: Rivers shapefile

Objective 5: Reclassify the roads into suitability classes.

            Input dataset: Roads shapefile

Step 3: Explore Input Datasets

The image below is the model builder for this scenario. These are the steps I took to finish this scenario.

Doing the three steps are helpful because you can work through the issues of the project before starting the analysis. Also, when using the model builder you can use it to create all your data at once instead of doing each step separately. We created a map with a side-by-side comparison of how the data would look if you had equal weight with all the information and if you changed the weight depending on the feature. This is helpful because it give you different scenarios of what it least to most suitable in your dataset. In the image below it shows that least suitable areas are on mountain tops and most suitable are by a river with equal weight. When you change to an unequal weight it is still the same but the kilometers square changes to being higher in group two. 

Looking at the information is able to give a better idea to the developer on the areas that seem the most suitable and the kilometers square of what they might be able to use in the areas. 

Module 5: Damage Assessment

 Module 5: Damage Assessment

This weeks module was about how to assess damage to structures after Hurricane Sandy. We were supposed to use FEMA "standards" to get the information they might need to help with relief.

The first part was to make a map that showed the tract of Hurricane Sandy from the Atlantic Ocean to the eastern coast. We learned how to create a line from points that were create from a XY Table. The geoprocessing tool is Points to Line (Data Management Tools) and it showed the path of Hurricane Sandy. The next part of the module we needed to change the symbology of the points so they would represent the different phases of a hurricane. The way we did this was to use Creating Marker Symbols because it makes it possible to make the points into a good symbology. The way to do this is to click on the symbology tab to change the different values into the correct symbols. We were given the exact ones to select for the points but under the properties tab then go to the layers menu. Under the Appearance tab choose Font. This will give you more options to change your symbology. The image below shows the final outcome of the path of Hurricane Sandy.

The next portion of this module we needed to create a survey on the Survey123 in ArcGIS Pro Online. This is useful to learn because you are able to send this out to the public and they can help you get data after a tragic event such as a hurricane. This survey was for the damage assessment for Hurricane Sandy. You are able to make so many different questions that have to be required in order to go on with the rest of the survey. Also, another important aspect is that you can add images and the exact date/time it was taken. Lastly, the survey can have a map put in and there is a point added when they open this portion of the survey. The link is where you can look at the survey for instructional view only: https://arcg.is/1zTePH1.

In the final portion of the module was getting the images for pre-and post-Sandy, adding domains, and analyzing the data for the "FEMA" client. We were giving the images that were going to be used for this exercise but when getting your own data being able to get good imagery data is important to assess any damage. First we needed to create a Mosaic Dataset and then add the raster data to the dataset. Having both of the images on top of each other is helpful because you can keep going back and forth to see where the damage is located. Next we needed to add domains so we can have it for attribute values for the points that we are going to use to assess the damage from Hurricane Sandy. You want to right click you geodatabase and choose Domains. It will open up a table similar to an attribute table. We put in the domains we wanted for this assessment, which were inundation, structure damage, wind damage, and structure type. After putting in the domain you want to add codes/descriptions that are going to be used when trying to interpret the data. The image below is a look to the different domains with the view of the code/descriptions of the structure damage.

After creating the domains and the codes/descriptions we created a new feature class that has the different fields with the domains. Even though the directions say "No" on the Allow Null portion you do need to say yes in order to actually create the features. In this portion we needed to classify the structure damage. When trying to identify the properties I was looking at the pre-storm image to see if the structure looked like people would be vacationing or living there versus a commercial place. The scale that worked the best for me was between 1:500 and 1:250. I was trying to see the surrounding area to see if the structure was affected or there was no damage. The decisions that were the most difficult were trying to decide the line of when the structure is inundated or not. There is a lot of sand around but in these areas many of the structures are higher from the ground so they could be perfectly fine. Another difficult decision was trying to decide wind damage because this is an image it makes it hard to know what was wind and what was water. I would say I would like another image that was at the same time as the previous image because it made it hard to know what was wrong with the structure and what was a shadow. Also, I think we should add another structure type of commercial because I was having a hard problem with wanting to put unknown because I knew it was commercial and not industrial. A last piece of information would be the structure height to know if the structure was inundated or not from the water and sand. The image below is a view of the different points I put on each structure.

Lastly, we needed to take the data we created an analysis the data to understand what it happening post-hurricane sandy. To get the information I needed to know the distance was by using the Near geoprocessing tool where I could get the distance of the structures from the coastline. It did show that there were four structures that were past the 300-meter radius. Also, to get more structures I did have to change my coastline a little because it was not showing the structures. Lastly, I looked at the near distance portion of the attribute table and saw that most of the structures were 92-meters and above. This is interesting because if you focus on just the distance you might not get the information for all the structures. It looks like the closer to the coastline the more likely the structure is either destroyed or has some major damage. At the same time, further away from the coastline it does look like the structures were more affected by the hurricane. I would say it is reliable because I kept the same thought process while I was analyzing the data. The only problem I had was only being able to see the top of structure I was having hard time telling how much was damaged. I based the decision on the sand a debris surrounding the structure.

Module 4: Coastal Flooding

 Module 4: Coastal Flooding

In this weeks module we were focusing on how to use DEM and LiDAR data to see storm surges within 1-meter. This module was more difficult than other ones because you had to do a lot of geoprocessing before being able to do analysis. I did have to rely on past modules to help me get the information I needed for the module.

We were given two data types which were dem_lidar and dem_usgs. The dem_lidar is a high-resolution elevation model for a portion of Collier County, FL created using Lidar data. The dem_usgs is a regular elevation model from the USGS created using traditional methods (i.e. photogrammetry) before Lidar data were available. The first thing we needed to do was reclassify the values to where it showed 1-meter or less within the values. After that we needed to use the raster to polygon geoprocessing tool so that we could use the spatial tool to get the information of how many buildings were disturbed by the storm surge. Being able to get this information is important because it shows how each of the data sets interpreted their storm surge. After creating the polygons I got the select by attribute table and got the join_count to get the selected buildings within each data set. This was more helpful for me to do so that I could focus on just the buildings I needed for the table. Also, when making the map having them as their own layer it made it easier to change the colors. The last aspect we needed for the map was to figure out the error of omission and error of commission. Errors of omission are those buildings impacted by the storm surge based on the Lidar DEM (the “true” scenario) but not identified as such based on the USGS DEM. Errors of commission are the buildings not impacted by the storm surge based on the Lidar DEM but incorrectly identified as such based on the USGS DEM. The image below is how the different buildings looked based off if they were within both the data sets, in one data set, or not in a data set at all. 

As stated earlier this module was one of the most difficult because you needed to not only change from feet to meters but use a lot of geoprocessing tools in order to do any analysis.

Module 3: LiDAR Visibility

 Module 3: LiDAR Visibility

This weeks module was learning how to do visibility analysis within ArcGIS Pro. There are two types of visibility analysis which are viewshed analysis and line-of-sight (LOS) analysis. The viewshed analysis is trying to answer the question, "Which areas in a landscape are visible from an observer point?" Where given one or more locations, the output is a map of the visible areas. On the other hand LOS tries to answer the question, "Which segments are visible along a specific site line?" Where given one or pairs of locations, the output is a map of visible segments along a set of line. It is important to note that both of the analyses use elevation models, such as digital elevation models (DEM) or a triangulated irregular network (TIN).

During this weeks module we took four courses (Introduction to 3D Visualization, Performing Line of Sight Analysis, Performing Viewshed Analysis in ArcGIS Pro, Sharing 3D Content Using Scene Layer Packages). Each one of the courses were taken on the ESRI website where they explained what you needed to do and then did an exercise with the data they gave you in the beginning. 

The first exercise was Introduction to 3D Visualization course shows how the data can be seen in 3D mapping. There are two 3D maps which are the Local Scene and the Global Scene. The local scene is used more for looking at data within a city whereas the global scene is more of looking at things internationally, such as flight patterns. The next part of this course was understanding how to present the elevation within the 3D maps. There are three types: On the Ground, Relative to the Ground, and Absolute Height. As said on the ESRI website "Elevation types are a property of the layer and are dependent on the type of elevation surface that you choose in your scene." Also, you can also manipulate height variables using techniques such as cartographic offset and vertical exaggeration. Cartographic offset vertically adjusts the height value (or z-value) of the entire layer, raising or lowering all features in the layer by a given height. Vertical exaggeration is used to emphasize subtle changes in a surface. Another thing that was learned in this exercise was extrusion of 2D features to 3D features. Extrusion enables you to take your points, lines, and polygon features and stretch them vertically to create real-world 3D objects. There are fours ways to extrude data: Adding to the feature's base height, Adding to the feature's minimum height, Adding to the feature's maximum height, and Extruding to an absolute value. Lastly, in this exercise we learned how to add 3D symbology to our maps including trees, water, and buildings. Also, we learned how to make those features look more realistic such as moving water.

The next exercise was Performing Line of Sight Analysis course determines whether two points in space are intervisible. A line of sight calculates intervisibility between the first vertex (the observer) and the last vertex (the target) along a straight line between the two. A line of sight considers any obstructions provided by a surface or multipatch feature class. Visibility between these points is determined along the sight line. There are three general processes that are used to perform visibility analysis. First you need to determine observers and targets. Second construct sight lines. Third determine line of sight. It is important to note the color of a sight line indicates the locations where the surface is visible and where it is hidden.

The third exercise was Performing Viewshed Analysis in ArcGIS Pro course we learned how to modify the input features to model the visibility from a known vantage point. The tool creates an output that models the areas that are visible from given vantage points. The Viewshed is where you can symbolize the visible area from an observer point in a raster. The geoprocessing tool considers the height of the individual, along with any obstructions surrounding the point. The areas that are considered visible from the observer point will be indicated in the output raster. The Viewshed tool is controlled through fields that are added to the input data to control the observation point elevation values, vertical offsets, horizontal and vertical scanning angles, and scanning distances. This tool was able to help an analyst see where lights cross the landscape depending on the height and the radius of the light beam. This is important because you can change the height and see where more or less areas cross one another.

The fourth and final exercise was Sharing 3D Content Using Scene Layer Packages course we learned how to change the data and share it with people who have ESRI accounts. ArcGIS Pro allows you to investigate and visualize your data in an intuitive and interactive 3D environment from any angle or perspective. You can use a scene for inspection and exploration workflows, for visualization, for communicating analytical output, or for storytelling about real-world projects and scenarios. Once again, it is important to know if you want a local or global scene. Also, we learned that there are three steps or workflow to authoring your data: 1. Load your data, 2. Display 2D data as 3D layers, and 3. Convert 2D data as 3D data. Before presenting your data there are some tips and tricks you need to remember: 1. Have all your source data and the scene in the same coordinate system, 2. Structure your content and decide what the user must see, 3. Define an area of interest (AOI) for your scene, and 4. 3D symbology is required for feature layers to be published and shared. After making your data there is a benefit of sharing your data because a wide variety of people can see it and it can be shared easily. It is important to note when using ArcGIS Pro, you can save your scene layer and its data as a scene layer package (an SLPK file). To determine the most appropriate option for sharing scene content with your intended audience, you must answer the following questions: 1. With whom do you want to share the content? (You can choose to share content with the public or a specific group), 2. How do you want to share the content?, and 3. What type of content will be shared? (Every content item that is published to a portal can be shared). To upload a scene layer package and publish a hosted scene layer, you will perform the following workflow steps: 1. Sign in to your organization, 2. Open My Content, 3. Add the scene layer package on your computer as an item, 4. Type a title and tags that describe the scene layer package, 5. Check the box next to Publish This File As A Hosted Layer, and 6. Add the item.

All the courses were very useful because first you learn about what you are doing in the exercise and why you are doing it. Also, you get step-by-step instructions of what you need to do with the data that was given to you. Lastly, there was a quiz at the end to help you understand everything you learned.

Module 2: LiDAR (Wetland Delineation)

 Module 2: LiDAR (Wetland Delineation)

This weeks module was about using LiDAR data to create different models including Digital Elevation Model (DEM), Digital Surface Model (DSM), Canopy Density, Vegetation Height, and a LiDAR map. 

The first step was downloading data from a state website. This is important because if you know how to download the data you can use many different websites to get your LiDAR data. This data is shown in .laz file that will need to be converted to las data. To convert your data, you need to open up the geoprocessing tool “Convert LAS” and put in your data. This will create an output LAS dataset that is a .lasd data file. In order to open the data under the insert tab you click the down arrow of the New Map and click on New Local Scene. This will open the 3D dataset of LiDAR.

Before doing the second step, so that you can calculate the data, we need to make sure that we have the 3D analyst and Spatial Analyst extension by clicking Project and then Licensing. If the words are black that means you have access to the extension. Next, we need to calculate the forest height from the LiDAR point cloud. First, we need to get the geoprocessing tool “Point File Information” where we run the .lasd data that we previously created. Make sure that the coordinate system is the same as the .lasd data you got from the website. (It is important to note that many surveyors across the US use State Plane when they collect data). After that we need to create a DEM dataset. First you need to click you data under the contents pane and then click on LAS Dataset Layer. Then click on LAS Points arrow and change the points to Ground. Once you do that you search for the geoprocessing tool “LAS Dataset to Raster”. Next you need to select the .lasd file as the input and change the value field to Elevation. Under interpolation type you need to select Binning with cell assignment as Maximum, and Void Field Method as Natural Neighbor. The Output Data Type is Integer and Sampling Type is Cell Size. Lastly, you need to make the Sampling Value as 6 and Z Factor as 1. This produced the DEM dataset that was back and white. To change the color right click the data and click symbology to what you want it to look like on the map. To create a DSM dataset, you need to change the LAS Dataset Layer to Non-Ground. When using the geoprocessing tool “LAS Dataset to Raster” keep everything the same and it will once again show the data as black and white. After that to get the tree height you want to get the geoprocessing tool “Minus” and set the Input Raster 1 as the DSM file and the Input Raster 2 as the DEM file. This was able to show the height raster data, and you can look at the attribute table to see where the negative and positive values are located. Looking at the data shows the negative values are where the roads and shadows of the trees are located. Where the positive values are where the trees are located and the different heights.

The map below shows both the LiDAR data and the DEM data that was derived from the LiDAR data. This shows that the visible points are in different colors to represent the height of the topography in the area. It looks like the lowest height is a dark blue and starting in the northern portion of the data to a red in the southern portion of the data. Also, you are able to see where the roads are located with the help of the base map being topographic. Lastly, you can see the slight rises and dips within the point cloud data.

The third step was to calculate biomass density. This is done by a lot of different geoprocessing tools. The first geoprocessing tools is “LAS to MultiPoint” where you put the .lasd data from the beginning. To get the metadata that represents the bare earth you need to set the average point spacing from the attribute table from the Point File Information dataset that created as the value and set the class code as 2. To create the metadata for the vegetation it is the same value but the class code is 1. After that you want to convert the MultiPoint files to Raster with the “Point to Raster” geoprocessing tool. You will put the data you named for the bare earth as the input. Set the Value field to OID and set the Cell Assignment to Count. Lastly you use the average point spacing times 3 and round to the nearest whole number. For the vegetation you will do the same thing. Next you want to create a binary file where 1 is assigned to all values that are not null by using the “Is Null” geoprocessing tool for both raster files you previously created. The next geoprocessing tool is “Con” so that if a value of 0 is encountered it will accept it as true value and if it is 1 it pulls from the original raster. You will set the Input from the previous data tool and set Input true raster or constant value to 0. Lastly, se the Input false raster or constant value to the raster dataset. Do the same thing for the vegetation dataset. The “Plus” geoprocessing tool is next that combines the vegetation and bare earth count datasets to derive the overall density returns. To get the Plus result from integer to float you need to use the geoprocessing tool “Float” which will provide the true representation of density from the next tool. Lastly, to calculate the density we need to use the “Divide” geoprocessing tool where you set the Input raster to the count dataset for the vegetation and set input raster 2 as the Float result. Once again it shows up as black and white so you want to change the symbology to what you want.

The image below shows canopy density from the LiDAR data. The information being conveyed in the density map is the areas that have trees and the areas that are not located. This would be helpful to foresters because it shows how good the vegetation is in the area. The higher the number the better the vegetation will be because it is showing a higher reflective value. Also, it could show where roads and houses are located.

The fourth and final step was to create a chart. This is done by clicking on the Height (results from the Minus tool) and go to the Data tab and click Create Chart arrow and click Histogram. For the Histogram to show up you need to click on Band_1 that will show the value and count of the information from the shapefile.

The final image below shows the height of the vegetation and a histogram chart from the LiDAR data. The graph tells you that the highest count of tree height is 63.25 feet tall and it is almost a normal bell curve of the different heights on the landscape. Also, this says the area is growing roughly at the same time and if they needed to cut down trees for money they would be able to get more across the landscape. Lastly, the values tell you that there are some areas that are higher topographically and not just the trees themselves.

Module 1: Crime Analysis

 Module 1: Crime Analysis

This weeks module focused on how to use ArcGIS Pro to make crime analysis maps. There are three different types of maps that are used to represent crime, which are grid-based thematic, kernel density, and Local Moran's I. Grid-based thematic mapping overlays a regular grid of polygons on top of the crime events to produce a count of crime events per grid cell. Kernel density mapping is when points lying near the center of a search area are weighted more heavily than those lying near the edge. Local Moran's I is asking the question of if there are similar features nearby.

Grid-based Thematic

When making the Grid-based Thematic Hotspot Mapping you once again need to use the Spatial Join tool that shows the information from the 2017 homicides within the grid cells. The Select By Attributes tool you need to select the correct column so you can get the information you want which was any grid that had a homicide count greater than zero. The way I exported this feature was under the Data tab where I selected “Layer From Selection” where it creates the feature off what I selected. Next, we needed to get the top 20% of the features that were selected previously, and this is done by opening the attribute table sorting the Join_Count field and dividing the total number of records by 5 and making sure to rounding down. Since there is no selection tool for this you need to do it manually by clicking the first box and then hold the shift button to where you want to end to get all the numbers in between. After all of that we need to dissolve the features together. This is done by making a new tab that says Dissolve and using the field calculator to give the entire column the same value. Then find the Dissolve tool to create the multipart features. You can see it in the image below.

Kernel Density

First, we want to search for the Kernel Density Tool. As stated earlier make sure there are no spaces in your folders and your features. This was done similar to the maps we did for Washington DC kernel map. We need to make sure the output cell size and the search radius are in same which is in feet. After creating the map, we needed to only make two breaks based off the mean and the maximum value. This is done by right clicking the feature and hitting the symbology tab. Once you are in the tab you need to click the more button and select “Show Statistics” which will show all the information we need to make your break values. Next, we need to make the raster to a polygon but first need to use the Reclassify tool to reclassify the feature. After that we need to use the Raster to Polygon tool to same the feature to a feature class within the project geodatabase. Lastly, we needed to do the Select by Attributes and find the value of 2 which is three times the mean of the original kernel density. The image below that shows the kernel density from the 2017 hotspots.

Local Moran''s I

The last map we produced was the Local Moran’s I Hotspot which is supposed to use crime counts or rates aggregated with meaningful boundaries. Once again we used the Spatial Join tool between census tracts and 2017 homicides. Then we needed to create a new field for crime rate and create a field calculator where homicides per 1000 housing units. Next, we used the Cluster and Outlier Analysis (Anselin Local Moran’s I) tool and leave the parameters as their default settings. After that we needed to get the high-high clusters because it shows the areas that had a high among of crime per 1000 households. Lastly, we dissolve those features so it looks good on the map which you can see below.

Based on all the information above it appears that the kernel density map would be the best because it shows a smoother and more continuous map instead of breaking it into blocks or zones. Some lessons I learned when doing this module was when setting up your environment setting make sure the extent and the mask are the same. If it is not, then the feature class will show up empty. Also, in the Kernel Density (Spatial Analyst) tool you need to make sure there are no spaces in either the folder the information is saved in and the feature class.