Module 5

 In this lab I created feature classes to create a line of the coastline and point representing land parcels. I used the mosaic dataset feature to create two different mosiacs of pre hurricane rasters and post hurricane. I used these 2 mosiacs to find the damages in a study area. 

I used the select by location tool, selecting within a distance 100 meters from the coastline. The for 200 m i selecting by location 200m and the removed from current selection with 100m. The did a new selection for 300m then removing from current selection 200m.

Within 0-100 meter: 8% had minor damage, 33% had major damage, and 58% were destroyed

Within 100-200 meters: 71 had no damage, 10% had major damage, and 18 % were destroyed

Within 200-300 meters: 95% had no damage, 5% had minor damage. I dont think this is reliable enough for nearby areas since the parking lot only had minor damage being within 100 meters of the coastline throwing off the minor damage rate.

Module 4-Coastal Flooding Lab

 

In this weeks lab we used tools to analyze LiDAR data to find areas that will likely be flooded due to storm surges in different areas. Using the reclassify tools we were able to find cells that would be flooded at 2 meters and at 1 meter. when then used building data to see what building would be flooded and what kind of building they are. I had a hard time with this lab but I am proud of myself for getting through it.

Module 3- LIDAR Visibility Analysis

 In this week we had to take a course on ESRI for Visibility Analysis. We took four courses within the activity: Introduction to 3D visualization, Performing Line Sight Analysis, Performing Viewshed Analysis in ArcGIS Pro, Sharing 3D Content Using Scene Layer Packages. 

In the first course, Intro to 3D visualization we learned how z values can represent elevation adn will allow us to see a 3D image. We used the provided data to navigate and investigate a 3d scene of Crater Lake in Oregon.

In Performing Line Sight Analysis we learned about line of sight. We used the Construct Sight Lines tool to create sight lines from an observer point. After we used the Line of Sight tool that tells you the visibility along the sight lines we just created. 

In Performing Viewshed Analysis in Arcpro we used the Viewshed tool to see waved based items by adjusting refractivity coefficients. This tools let us see streetlight cover, and how buildings can be effected by terrain. 

In Sharing 3d content we learned how we can upload our data to AGOL to share with the public, or organizations

Module 2, LiDAR and forestry

 In this weeks lab I had to extract liDAR data from Virginia, from the vgil website. With the liDAR data I used many geoprocessing tools to find information. In part 1 of the lab I used tools to separate the part of the liDAR that represents the ground, and then get the area that is not on the ground. Using the minus tool and these 2 new datasets I was able to find the hieght of the canopy in Virginia. 

In part 2 of the lab I used the count, null, plus, float, and divide tool to create a Density map of the canopy in Virginia. In the density map you are able to see areas with dense vegetation with the 1 values showing up darker. The lighter (0) values are showing the ground areas, with low vegetation. This is helpful to foresters to see the areas with higher density, You can see in this map that roads have very low density. 

The lab this week was very interesting but was difficult with the slow speed of the remote desktop. Creating my presentable maps was a challenge due to the slow network speed. 

Module 1 Crime Analysis

 The purpose of this lab was to use Arcpro to analyze crime data. Using different techniques of showing the hotspots of crimes can be extremely helpful to police forces for predicting future crime areas. With this lab I create three different homicide hotspot maps using kernal density, grid-based thematics, and local morans I analysis. 

Grid_based: Spatial Join of Chicago grid and 2017 total homicides, keeping all other parameters as default. Opened the table for this feature and selected by attributes- join count greater then 0, exported this as new feature class. To get the top 20 percent I took the total number of of objects (311) and multiplied by .2 which gave me 62.2. Rounding to 62 i sorted join counts by descending and selected the top 62 and exported this.

Kernel Density: Using the Kernel Density tool I made the KD raster image using the 2017 total homicides with the chicago boundary as the barrier. Under symbology, statistics, the mean was 1.18*3=3.54 and the max was 38.86. I used those 2 numbers at the 2 breaks. I then used the reclassify tool, then the raster to polygon tool. I opened the table for the polygon and selected by attribute for grid code is equal to 2.

Local Morans I:I used spatial join between census tracts and  2017 total homicides, keeping all other parameters as default. Added the field “crime_rate” and used the field calculator with the equation  (Crime Rate = !Join_Count! / !total_households!) * 1000. I then used the cluster and outlier analysis (anselin local morans I) tool with the new feature class with the crime rate field as the input field. In this new feature class I selected by attribute only the high-high crime areas, exported this feature. I then used the dissolve tool to create my final feature class. 

If I were given a limited policing budget and had to choose which of these three maps I would use to allocate my officers I would choose the grid overlay. According to the crime density, 11 homicides per square mile is the highest rate of homicides. This is also the map with the smallest total area, so I would need the least amount of land cover with all the maps.

Module 7

 

In this lab I learned how to use google earth pro to create maps and create a tour of south Florida. Initially I converted data of south florida surface water into a kmz file so we can use the data in google earth pro. Then I used that data and other data provided to create a dot density map of south Florida. I learned how to overlay a legend into google earth pro to create the map provided above. 

In the second part of this lab I created a tour of south Florida but dropping pins in different south Florida locations. I recorded a tour of the pins by clicking on different points of the map. 

I enjoyed exploring the different ways you can show data on google earth pro. I felt like this lab was relativlely easy and google earth pros function were very user friendly and straightforward. 

Module 6

 

This week I learned about Isarithmic maps, continuous symbology, and hypsometic tint. The map about depicts the the precipitation data for the 30 year period was derived by using point precipitation data. This data was then used in the PRISM analytical model to make gridded estimates of precipitation and temperature throughout months and years of the 30 year period.

I used hypsometic tinting to symbolized the data. Hypsometric tinting uses light and dark shades to add a 3D effect to the map. This uses different colors and shades between contours. I implemented this into our map by using our Annual Precipitation layer and running it through the “Int” spatial processing tool to enhance the shading from the original continuous tone. Then I added the hillshade effect for more depth. 

I enjoyed this weeks lab and learning the many different ways to depict data of a raster image.

Module 5

In this lab we created a map showing population density and wine consumption across Europe.

 

I created a chloropleth map to show the population density across Europe. I used the natural break scheme to represent this data, with 6 classes. I believe the natural break was best for this data class because it excludes outliers, and doesn't give misleading information of the more sparse areas. I chose a graduated color scheme of purples. This color reminds me of wine because it is purple and using shades of one color is easier for the color blind to distinguish and leads to less confusion.

Then i used graduated symbols to show the wine consumptions in each country. I chose the graduated method for this data. I think this gives a better representation of the wine consumptions increase throughout Europe.

I struggled with this map because using all these functions really slowed down arcpro to the point where I had to take many breaks while waiting for it to load.

Module 4: Data Classification

Figure 1. Data Classification of Seniors using percentages

Figure 2. Data Classification of Seniors using counts 

In this module I learned how to use different data classification method to show how populations of seniors are distributed uses percentages and counts.

The data classifications I used were the following: 

1. Equal Interval-this uses the highest and lowest amount of 65 years old's and breaks that up into 5 categories. This showed the left census tract having more 65 year old's than any other method. Equal intervals do not take into account outliers so this can inaccurately display data

2. Quantile-this method breaks the total number of classes into equal categories. This helps eliminate outliers skewing the data. This and the natural break class ended up having very similar looking maps. 

3. Standard Deviation- Standard deviation method is using the standard deviation to add or subtract from the mean of the data. Showing how far data deviates from the mean can cause outliers to stand out more. Using standard deviation is best with “normal” datasets. this map has the least variation than any other map, having the largest amount of the map be in the <-1.5 Std. Dev. category. 

4. Natural Break-Natural breaks uses the natural grouping over data within the dataset. This method used the Jenks optimal method. This method does consider the outliers when creating the classifications. This map ended up looking very similar to the quantile method. 

I believe the population count normalized by area using natural breaks gives the best idea of where most of the seniors are distributed in Miami Dade County. This map shows where the seniors actually are, and not showing misleading data from percentages living in sparse areas. If one area has a small population of people, but most of those people are seniors, the percentage map would show this area as an area of interest, even though across the map, only a very small number of seniors actually live there. The natural break method gives the most accurate representation of the population. This map takes the outliers into consideration.

Module 2, Map of Florida

This lab was about the importants of all the map elements, specifically labeling. We learned the differences of labeling points (cities), lines (rivers), and polygons (swamps and marshes). My map shows all of the important features in florida, including important rivers, cities, the capital of Florida, Okefenokee Swamp, and the Everglades. The water features are in italics and the cities are in regular font. The font I used was Bodoni MT because I thought it looked sleek and easy to read. To label I used ArcGIS pro's labeling function for all labels. For the rivers I customized by placing the labels in river placement curved above the river. I learned how to use the convert labels to annotations to have more control of the location of the labels. I made the Everglades label fit the entirety of the largest part of the everglades. Here are three more customizations I made: The first customization I made was in the label properties for rivers, under position->conflict resolution under remove duplicate labels I removed within a fixed distance of 150 points. I think this made the rivers look less crowded. The second customization I made was to move all point labels to the upper right corner. According to the lecture this is the best location for a point label. The last customization I made was changing the capitals symbol to a star to indicated that it is an important city and is different from the other cities

Good Maps vs. Bad Maps

THis week we are looking into the differences between a good map and a bad map. Here is an example of what a good map would be:

This is a map depicting the largest ancestry in the 2000's in each state and each county. This map is very easy to understand. There is minimal "crap in the map, showing only the important inforation. The important things in the map state out, with the focal point of the map being the map of ancestery with largest population in each county. This depicts the imformation truthfully showing smaller areas to give better understanding of ancestry in america. Here is an example of what a bad map would be:

This is a map depicting the wildlife and game in the US usnig pictures. This map is not easy to understand. So much information is given on this map that I cannot focus on one thing. The labeling is very small to inlcude all of the info. The title is on the bottom of the page and if very small so it goes overlooked. The infomration around the map seems unneeded and difficult to undertand. I do think the map could be pretty to look at, but does not convay information clearly of effieciantly.

A little about me

 Hi everyone! My name is Abby Stack and I’m in the GIS Administration Masters program. I currently work as a GIS technician for a utility pole auditing company. I just started a couple months ago. My goal with this program is to expand my knowledge of GIS and eventually get a job using GIS in some sort of environmental protection field. I work full time and have a 16 moLinks to an external site.nth old son named Eli. I think some adjectives they describe me are fun, happy, creative, and caring. Here's a storymap of my hometown: Story MapLinks to an external site.

Guana Tolomato Matanzas National Estuarine Research Reserve LULC change report

https://docs.google.com/document/d/e/2PACX-1vRk7CW1vqW5702wcNRThcLMiou7KdrJm23p9HyX8ZZphUaI4zE9Ija-CvnNzlR38ETIqO6kjNAu0BHU/pub