Blog Post #5: GIS Portfolio

 Blog Post #5: GIS Portfolio

In the final weeks for the GIS Internship we were given the task of creating a GIS portfolio either on paper or digitally. I decided to create a digital portfolio so that I could give it to my supervisors and let them know everything I learned getting the GIS certificate. This was the first time I have done a portfolio online and I would say it was hard to try to find a free website. The Wix website said it was free but it would not post anything unless I bought for the domain name and the website. That is when I decided to search more and found a website called IONOS. It still made me pay for the domain but it was only $13 for the year and there were no other things I had to pay for in order to make my portfolio. I would say I am extremely happy with the portfolio. I will definitely add more in the future and keep the website updated. Since I am a graduate student I had to create a video or audio from questions that were provided to us. I decided to do an audio because my computer was having a hard time with the visual. Lastly, I decided to answer two questions: 1. Describe a time when you were faced with a challenging situation and how you handled it. and 2. What is your favorite map example in your portfolio and why? I felt like both of these questions would be ones that I might answer in future interviews and it was nice to practice my answers.

Portfolio: https://www.emilydemontalvoportfolio.com/

Audio: https://drive.google.com/file/d/1jjTnO8o0ZX_Pmy0IIkxyhePngxcap5uu/view?usp=sharing

Blog Post #4: GIS Day

 Blog Post #4: GIS Day

This year for GIS Day I just did an event with my co-workers. I taught them how to georeference maps within ArcPro. The reason I did this was because as archaeologist we need to georeference historic maps to see if there were structures or other features on the landscape. This was more of an informal discussion but wanted to show them what I do on a daily basis for the USACE, Mobile District. I was able to geo-reference an environmental map for an Environmental Impact Statement from the 1980s. This was a real world experience that I could use to teach my co-workers. I was able to show them the different ways one can georeference a map. Also, I made the map transparent to make sure you are getting to where you need on the base map. The image below shows a goereferenced map that I did for the event. It was nice to show my colleagues the different things I do for the USACE, Mobile District and how I could help them in the future.

Blog Post #3

 Blog Post #3

The interview I watched was GIS in Natural Resources with Sarah Bellchanber. The reason I chose this one is because I work with some people in Operations who do Forestry and I wanted to see if there was something she used to help her team. The first important thing she said is learning about Python is very helpful for her job. I know we learned about that for our certificate and it is nice to see that it could help find future jobs. The second important thing was learning more about the apps we might need to use for the field and making sure people know how to use them. This is helpful because I have created manuals but I should probably do a learning session before people go out in the field. The third important thing is to tell people your specialty when you apply for jobs. Since I have been doing archaeology it would be good to tell someone that and my emphasis in GIS. After listening to the interview it was nice to hear that the GIS certificate we are getting could help us get jobs in other careers in GIS.

So far, I have created shapefiles for different studies. Also, I have been helping other people on my team on how to use GIS and what analysis they can do with the study. Finally, I have worked with other divisions on online mapper and getting all the data we need to do a project into one area. One life lesson I have learned is that you are not going to love every job you do but that you can make the most out of every job. Another life lesson is that even if you work hard, it does not always mean you will get the promotion, but that should not stop you from working hard. A final life lesson I learned is that you do not have to speak at every moment and it is okay to just listen. I have definitely felt academically prepared for the internship. Knowing how a geodatabase works, creating shapefiles, and using online mapper has helped me for the internship. Also, being able to help other people has been very nice with all we have learned while getting the GIS certificate.

The approach was to have me update my profile because I created mine back in 2017 when I graduated from the University of Oklahoma and updated after graduating from the University of Mississippi. It was good I added more information because I had not updated the information in over 5 years. I wanted to make sure I had my current job but also add in the different things we learned while taking classes for the GIS certificate. It was hard to remember everything but luckily I had updated my resume to where I could just copy and paste onto my profile. LinkedIn Profile: www.linkedin.com/in/emily-demontalvo-2a0142113.

Module 3.1: Scale & Spatial Data Aggregation

 Module 3.1: Scale & Spatial Data Aggregation

This weeks module was about the Modifiable Areal Unit Problem (MAUP) which involve the scale and zonal effects of data. According to Manley (2014), the scale effect shows that there are hierarchies that are put into our work that if we are not aware can skew the data. Also, the scale effect is attributed to variation in numerical results owing strictly to the number of areal units used in the analysis of a given area (Openshaw and Taylor 1979). Lastly, when thinking about MAUP it is important to know that your data can be skewed but if you make changes in how you correlate your data such as with scale it can help make sure you are getting answers that make sense.

Another issue that arose this week was the basic resolution effects on raster data. When talking about raster data the most common are digital elevation models (DEM) which focuses on the grid cell size that has significant effects on derived terrain variables such as slope, aspect, and curvature. Knowing that the grid size changes the look of the maps helps to choose the correct raster grid cell size. Such as when trying to carry out realistic terrain analysis is limited by the quality of the DEM applied (Kienzle 2007). There are three important aspects to keep in mind when trying to use DEM to analysis data: 

• "The accuracy and distribution of the elevation points used to interpolate the DEM.
• The interpolation algorithm used to generate a continuous DEM.
• The chosen grid cell size."

Knowing all this information can help the GIS analyst understand how to use the data to get the results they might want for terrain analysis (Kienzle 2007).

The last issue that was in this weeks module was how gerrymandering of political boundaries can cause issues because of boundary definition. These boundaries can change to make it to where one political party has more of an advantage in that congressional district. When trying to determine the compactness of a district I learned about the Polsby-Popper score and the formula. First, we needed to get the area and perimeter of all the districts. I did it in kilometers to keep it consistent. The formula was not working for me to use it all at once, so I split it to three different attributes. I did the formula for the top (4 * pi * Area). Then another formula for the bottom (Perimeter^2). Lastly, I created a formula where I divided the top number by the bottom number. The worst offender was North Carolina Congressional District 12 with the score 0.029476. It is seen in the picture below.

Module 2.2: Interpolation

Module 2.2: Interpolation

This weeks module was about using different interpolation techniques to represent different data points. The different interpolation techniques we used were Thiessen, Inverse Distance Weighted (IDW), and Spline (Regularized and Tension). Each one of the techniques shows a different way to represent data and it all depends on what the person wants to represent changes which technique you are going to use. The techniques were used to represent water quality in Tampa Bay, Florida.

The first technique was the Thiessen or Nearest Neighbor interpolation. One advantage of using Thiessen interpolation is that it assigns a value for any unsampled location that is equal to the value found at the nearest sample location. This is helpful because it makes each point its own polygon. Another advantage is that it provides an exact interpolator which means that the interpolated surface equals the sampled values at each sample point. It means that each location is preserved and there is not a difference between the true and interpolated values at the points. A major disadvantage is the sharp edges between the different points. It causes the image to look harsh with the edges. A final disadvantage is that the areas there are no points causes bigger polygons than what might actually be in the area. An example would be if they were on a terrain that changes quickly and there is no point in which the polygon would not represent that change.

The second technique is IDW. When looking at the data distribution of the IDW it is based on the assumption that the points that are close to one another are more alike than the points farther away. This could mess up the areas that have significantly different elevations next to each other. The IDW does not provide prediction standardized error which makes the use of the DEM a little problematic. Also, the IDW is oversensitive to outliers which could cause “bullseye” areas that have one point as a high or low point.

The third technique is Spline (Regularized and Tension). Spline, it is helpful because it is smoother and looks like it could be more accurate than the IDW. It is important to note that the DEM only works if the data is very accurate. The use of Spline is more adaptable because they may be used for lines or surfaces, and they may be estimated. Also, Spline is points that is used are considered “guides” that helps make the lines smoother. Lastly, a major problem to consider is that it can create unpredictable surfaces (overestimation or underestimation) in areas with low data density or high topographic change.

What I learned about surface interpolation is that there is not one method that is better than another. When choosing the technique all depends on different factors such as cost of sampling, available resources, and accuracy. Also, it was interesting to see that the sampling techniques can change how you try to represent your data. The results that surprised me were how spline interpolation can cause a big change just because one extra point was in the area. All I did was change the spline regularized interpolation by taking away one point. The way I would decide on a different technique is if I knew the sampling techniques that were used. I feel like the systematic technique would help make all the interpolations better whereas the cluster and random sampling would need to be more with spline interpolation. Lastly, when doing the adaptive sampling pattern would work best with the spline tension interpolation because it seems to be like the original data points.

The image below is Spline Tension interpolation. This image to me represented the data points the best. The reason is because it seemed to be the most like the non-spatial data that was represented. Also, the spline interpolation is smoother when it is representing the data points. Lastly, I chose this technique because they can be changed to represent the data points, and they cab ne used for lines and surfaces unlike the other techniques.

Module 2.1: TINs and DEMs

 Module 2.1: TINs and DEMs

This weeks module was using continuous surface in TIN and DEM models to represent elevation. One would use TIN for vector data models and DEM for raster data models. You can use either one to represent your data it just depends on what you are trying to present.

When doing the exercise for the module there were different ways we tried to represent the data but the one that I am going to talk about is how the contour lines were presented by using TIN or DEM. TIN stands for Triangulated Irregular Network and is created out of elevation points. A Digital Elevation Model or DEM is a digital representation of a topographic surface. This is also created from the same elevation points. The image below shows how the elevation points were able to show the different contour lines from TIN and DEM. It is important to note that the black and red lines are from the TIN data set and the gray lines are from the DEM data set.

The key differences between the two contour lines are that the DEM contour lines are smoother whereas the TIN contour lines are very sharp. Another difference is that the DEM contour lines showed more in the higher points than the TIN contour lines. It looks like the greatest difference is where the higher numbers were located. There are more DEM contour lines. The smallest difference was where the lower numbers are located for both the DEM and TIN contour lines. I would say these differences were because there were more points in the lower numbers compared to the higher numbers. Lastly, of the two sets of contour lines I would say the DEM is more accurate.

When trying to know if you should use TIN or DEM it depends on how you are getting your data. TIN is usually not widely available and has been made for the specific study. Whereas, DEM have various resolutions and used for larger study areas. Luckily with new data collections such as LiDAR it makes the differences between DEMs and TINs obsolete. It is all based on how you want to represent your data.

Blog Post #2

Blog Post #2

I searched for jobs where I can use archaeology and GIS. The job title is a GIS specialist for an archaeological cultural resource management firm. I chose this job because I have worked for this company in the past and they were really good but I needed to find a job where I could work from home. Also, it makes it a dream job because I get to use both archaeology and GIS to do my job which is the reason I wanted to get the certificate. Based on all the requirements I have all of them because of what we have been learning in our GIS classes and already having both a BA and MA in Anthropology with an emphasis in Archaeology. Also, since I have worked with the team in the past I know how their maps looked and what they would need in order to do field work. The only thing was they did not show a range of pay and that would make me worried they are trying to hide how much they would be willing to pay.

Job announcement: https://www.terraxplorations.com/gis-specialist

While doing the job search the biggest thing I noticed is that the GIS jobs that paid the best were the senior level jobs but you needed 5 plus years of experience in GIS. The entry level jobs sounded that I could apply for currently but I would need to get paid more to live in today's economy. It is interesting to find the line between what you are qualified for and what you need to get paid. 

Module 1.3: Data Quality-Assessment

 Module 1.3: Data Quality-Assessment

This weeks module was trying to see the completeness of the roads separately and compare them to each other. There were two road networks that were used, TIGER and Jackson County street centerlines. TIGER stands for Topologically Integrated Geographic Encoding and Referencing which was collected by the Census Bureau. The TIGER data that was used is from 2000 and it does have some major errors, but in 2010 the government tried to fix these issues. The other data is from Jackson County street centerlines that is more accurate than the TIGER data but it is not more complete. Knowing where your data comes from is important because it will help see the completeness of the road.

First, I needed to make sure that the roads were only within the grids. The way I did this was by using the select by location and choosing the input features as the roads. I made sure to use the relationship as Intersect and the selecting features as the Grid shapefile. Then I hit apply to get only the lines within the grid polygons. Next, I used the Intersect geoprocessing tool and each road as the input feature. I made sure to do it separately because I did not want all the information be put together. I made the Output feature class as “Grid_Street” and “Grid_TIGER” so I would know that was different than the other shapefiles. Then in the Attributes to Join I wanted All Attributes because it would make it easier in the future to get the information for each grid. Finally, I made sure in the Output Type to put Line so that it would make the line features that intersected the grid. Lastly, I added a new column called “New_Length” to get the new information of each polyline. Once it was created, I did the calculate geometry to get the new length in kilometers. I did this to make sure that there were actually new lengths.

After getting all the data it showed that the TIGER 2000 data  were more complete than the Jackson County street centerlines data. Also, that even though the data is open to the public and done by the Federal Government it does not mean it will always be accurate. This indicates that it is necessary to have two datasets to help compare against one of each other. The image below is a choropleth that shows the percentage of differences between the two dataset length within each grid. It indicated that there were major negative and positive differences between the two data sets. The negatives indicated that the TIGER data had more total length than the Jackson County street centerlines. The positives indicated that the Jackson County street centerlines had more total length than the TIGER data. Lastly, the little change is approximately from -3 to 3 on the number scale.

Module 1.2: Data Quality-Standards

 Module 1.2: Data Quality-Standards

This weeks module is looking at positional accuracy of road networks. The data for the roads came from Albuquerque and TeleAtlas for Street Map USA. The Albuquerque dataset is considered pretty accurate while the TeleAtlas is distributed by ESRI in ArcGIS. We were supposed to look the different street views to see where they were similar and where they diverted from each other. It was interesting to see the ESRI Street Map USA seemed further away from the "true" data points. One would think the street views that ESRI produces would be very accurate. The way we put the "true" data points in were but doing a close up of the orthoimages to see where the streets seemed to be located. The image below shows data points and street views of both Street Map USA and Albuquerque data sets.

I did 40 data points because it was hard to get the sampling rules of 20% in each quadrant and >10% of diameter apart at the 20 data points. The way I did the accuracy assessment was using the table on Page 5 in the Positional Accuracy Handbook within excel. In order to make sense of the data I switched it to UTMs to get the Easting and Northing in meters. In the handbook we are supposed to see the difference between the test points and the "true" points of both the x and y coordinates. Also, we had to square both of the x and y coordinate differences. Then we would add together the squared differences and then add it all up to get the sum. After that we would get the average which is the sum/number of points. We would get the RMSE which is the average^1/2. Lastly, we would get the NSSDA which is 1.7308 * RMSE.

Once we got all that information we needed to write our final accuracy statement. This statement does change between a tested and complied to meet. The tested statement is used when the accuracy was determined by comparison with an independent data set of greater accuracy (Positional Accuracy Handbook, pp. 5). The compiled to meet statement is used when the data has been thoroughly tested and that method produces a consistent accuracy statistic (Positional Accuracy Handbook, pp. 5).  Based on the way these points were collected we would use the test statement. For the Streets Map USA the statement is Tested 306.9 meters horizontal accuracy at 95% confidence level and the Albuquerque Streets is Tested 16.7 meters horizontal accuracy at 95% confidence level.

By creating the different data points it helps know the positional accuracy of your shapefiles. It is important to try to get a higher accuracy data set to compare. After doing all the data points and getting the math correct it seems that the Albuquerque data set is more accurate than the Street Map USA data set. This is interesting because the Street Map USA dataset is used within ESRI ArcGIS and one would think that is more accurate then other datasets. 

Blog Post #1

 Blog Post #1

The internship program that I am doing is for the U.S. Army Corps of Engineers (USACE) as a GIS Analyst and Archaeologist. The way I plan to earn this credit is by creating an archaeological geodatabase for the Planning and Environmental Division. Create maps for different divisions and/or projects within the USACE, Mobile District. Create maps for reports and letters for the division. Do GIS analysis of LiDAR data and coastal flooding for the division. Lastly, conduct remote sensing for projects requiring quantifiable impacts analysis.

The GIS user group I chose was GIS Association of Alabama (GISAA). This group is a non-profit group that covers the whole state of Alabama. The focus is on geospatial professionals within the private and public sectors. The reason I have chosen this group is because the region I mostly focus on for my work is the state of Alabama. It seems they are able to connect a lot of different professionals together and look at different ways to use GIS data. Membership is Individual - Professional Membership - $50.00 (USD) and Individual - Student Membership - $10.00 (USD) for one year. 

The website is: https://gisaa.org/

Module 1.1: Calculating Metrics for Spatial Data Quality

 Module 1.1: Calculating Metrics for Spatial Data Quality

In this weeks lab we looked at data that was collected by GPS coordinates to see the precision and accuracy of that data. This is important to know because when using data you need to make sure you are paying attention to what is being given to you and not just accepting it. It is important to note that when looking at the data one needs to aggregate data for both precision and accuracy. 

In the first portion of this module we took the data points and made an average data point. This is necessary because it helps show where the data should be compared to the rest of the area. Then we needed to project the waypoints and the average waypoint to the correct projecting so the data could be used in feet or meters better than decimal degrees. Having a lot of data it is important to do the Batch geoprocessing tool. This makes sure to get all your data in the correct projection without doing it one at a time. Also, you can do multiple feature classes to make sure all the data is projected correctly. Next we need to use the Spatial Join tool to help with the distance that falls within the different percentages (50th, 68th, and 95th). This makes sure your data is correct without having to guess the different buffer zones and is much more accurate than a visual inspection. The map below is a visual representation of the horizontal accuracy and precision of the waypoints that were given from the GPS unit.

After getting all the data to make the map the next step was getting the horizontal accuracy. There was a reference point that was put in the data to help measure the distance between the average location of the waypoints and the reference point. Then we measured the distance in meters to see how far apart they were from each other to see the accuracy of the point. The following numbers were the numerical results of the horizontal accuracy and precision. The horizontal accuracy is low accuracy because everything is slightly offset from the reference point. This compares to the horizontal precision which is high because it is only 1.6 meters off from the reference point. Lastly, the way that horizontal accuracy is measured by using the average point and comparing it to the reference point that was taken in the field. On the other hand, the way the horizontal precision is measured by looking for the standard deviation of the points such as the different percentiles that were shown in the map above.

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.

Module 6: Geometries

 Module 6: Geometries

This weeks lab was about how to use geometries within Python and ArcGIS Pro. We wrote a script that creates a TXT file and write to it the coordinates and object IDs for vertices in a shapefile. This lab had us use two for loop statements. It was more difficult to understand how to make two for loop statements. Working with geometries is very time consuming but we learned about how to use geometry tokens. Geometry tokens provide shortcuts to specific geometry properties of individual features in a feature class. Another important factor we learned was when reading geometry types (polygons and polylines) requires extra code because an array of point objects is returned for each feature. As a result, an extra iteration is required to interact with the array first before you can get to the points that make up the array. This is where the for loop statements came in and made it easier to get all the information we may need. So that we did not have to do another for loop statement we did the getPart() method. The getPart() method returns a single point object for one single-part feature. If it is a polyline or polygon, it returns an array of point objects of the different features you are trying to get. In short, the getPart() method allows you to access and look through the different parts within a single geometry object. The hardest part I had was getting the information to print in the textfile and I remembered using the Try statements so that I could do the except and finally statements so that I could get the information to print. Being able to use those statements were important because it could show me where the issue was happening before things printed into the text file. Lastly, my other issue was figuring out the .write() statement so the information did actually write into the textfile. That is when I created the line I wanted to write and then put it the parentheses. The image below shows a portion of the information into the textfile.

This image below is the flowchart of how I created the script for the information I wanted for the rivers shapefile. Once again it was difficult to show the for loops and how they were being represented.

This module was probably the most difficult one because of the two for loop statements and getting the information to write into the textfile. Once I remembered the try statement I was able to make sure I got all the information correctly into the textfile.

Module 5: Explore and Manipulate Data

 Module 5: Explore and Manipulate Data

In this weeks module the focus was on how we can explore and manipulate data that are within ArcGIS Pro. The script creates a new file geodatabase, copy feature class data to the new file geodatabase, create a Search Cursor for the cities feature class, and create a new dictionary.

First we needed to create a new file geodatabase that goes into the results folder. It is important to note that before any of this can begin you need to make sure you import arcpy and import env. Also, to make sure you can create the geodatabase more than once you need to have the overwrite output to True. If you are getting an error message if trying it again it is because ArcGIS Pro is open and it needs to be closed before running it in Python IDLE. If you run it in the Notebook portion in ArcGIS Pro you do not seem to have that problem. The image below shows the outcome of the script that was written.

The next portion of the script was creating a feature class list and copying that into the new geodatabase that was previously created. When making sure the data gets copied you need to use the Describe() function. Also, to get all the data copied you need to make sure to the script as the following arcpy.CopyFeatures_management(fc, outputEnv + "/ejd31.gdb/" + fcdesc.basename). This piece of code makes sure everything gets copied and that the basename of the feature shows up when it gets copied. In the image below shows that each feature got copied and how long it took to get the information to copy into the new file geodatabase.

After that I needed to create a Search Cursor that only printed the Name, Feature, and Pop_2000 only for the county seat within the cities shapefile. I knew how to write the code for one row in the attribute table but was not sure for three. I made the fields code as fields = [“NAME”, “FEATURE”, “POP_2000”] and the sqlQuery = “FEATURE = ‘County Seat’”.  I used the arcpy.da.SearchCursor because I did not want to change the table but to just find the specific rows and print them. Also, you need to make sure to use a for loop statement to make sure only the specific rows get printed and not the other ones in the attribute table. The image below shows all the features that were only County Seat with the name and population of those features.

     

The last portion of the script was creating a new dictionary based off the information from the cities shapefile attribute table. The dictionary needs to focus on the name and population of the county seat features. Another issue I was having was trying to get the for loop statement to create a dictionary for an empty dictionary. I knew how to make the dictionary but was not sure how to make the statement work. I did try different versions and none were working for me. I think asked the discussions page and Serenity helped me with the script needed to have another line of code saying with arcpy.da.SearchCursor(fc, fields, sqlQuery) as cursor: The information in the parentheses is based off the previous script above. The difference is that I needed to add the specific row numbers for the keys and values that I wanted represented in the dictionary. Lastly, I chose the dictionary variable[keys] = variables which was county_seats[name] = population. The image below shows the new dictionary with just the information of the Name and Population of the county seats in the cities shapefile

An important note is that to get the copying features to print separately the code is print(f”Copying: {fc} \n”). This was able to help me get them to print one at a time instead of all of them at once over and over again. Knwoing how to get the information to print properly will help know what is being copied in the first place. The script creates a new file geodatabase, copy feature class data to the new file geodatabase, create a Search Cursor for the cities feature class, and create a new dictionary. The image is of the flowchart that I used for the script that was written.