Spatial Data and Geocoding

     What do you do when you can't find a ready made spatial data file or shapefile or feature class for what you are trying to do? You build it of course. This weeks lab was two fold. First, building tabular data using some known points originally in Latitude and Longitude, which needed conversion to decimal degrees for display and usage in ArcGIS. That was an exercise in taking some data points and building an excel file which would be the data for a new feature class. Second, taking other publicly available address data and geocoding it into ArcGIS. This involved formatting the known addresses, again into excel, and transitioning that tabular data into another feature class within ArcGIS Pro.

In both cases, ready-made feature classes were not a thing, and the exercise was in translating available information into that usable within ArcGIS. Both parts of the lab were also in different areas. The first of the below being known eagle nests in the vicinity of the University of West, Florida. The second being schools across Manatee County, FL. Thank you to the Florida Department of Education for the addresses for the known schools. 

Here is a look at the tabular data and resultant display for the eagles nests: 

The following is a look at the schools mapped across Manatee County. This result is also available publicly, hosted by ArcGIS Online at the following link:

Manatee Schools by Brandon

The challenge with this lab was the attention to detail with working in excel, and ensuring the formulas for data organization were appropriately entered. Making sure all of the headers were setup appropriately for the transition into ArcGIS online to be successful. Also I encountered some initial difficulties with running the address locator. This is a multi step process involving a specific tool which took the tabular data I created, and then matched it against the known map region to build point features based on address. Also, when data could not be matched, I relied upon finding the actual school within Google Maps, and then manually updating the point information for the geocoded results. 

Ultimately this is an incredibly useful process, and the exercise in manually geocoding is invaluable. Thank you. 

v/r

Brandon

Vector Analysis, De Soto National Park, Lab 4

     This was a process-centric week. Now while that may be every week and all projects, relying on a certain order of skills to be accomplished or performed to create an end result, this week absolutely revolved around learning some particular skill ordering. What is the process for changing, selecting, and creating a geodatabase? What are the various ways you can run a query to select specific features by particular attributes? What is the difference between a spatial join and a feature layer union? These are only a few of the questions answered by this week's lab on vector data analysis which was accomplished in two parts. 

Ultimately the focus of the exercises that answered the above questions was in findign a suitable camp site in the De Soto National park based on a number of road, water, and conservation area criteria. Overlay and buffer analysis tools, which are two of the most widely used and practical ArcGIS tools, were utilized to compare and overlap these features to qualitatively highlight potential campsite locations. That is the subject of the map below.

From the above I would say my primary critique with my work this week is the lack of centrally identifying characteristics with the park. That is a limitation of the originally provided data sets. while I could look more into the park, that was beyond the scope of this week. However I did need to find supplemental shapefiles for the inset overview, which came from the US Census Bureau's Tiger files. 

Also, I did deliberately put the inset in miles and the park in kilometers for the scale bar. The base data is in UTM and meters, which translated well to kilometers for the park itself. But for the average viewer i wanted them to be able to associate the larger overview region in miles, with a nice round number there, 100. 

The other aesthetic goal was to highlight larger areas as the priority for campsites. That is why the darker greens are the desired areas, and the smaller areas almost blend in with the background. This was a deliberate choice to down play them, but still have them discernable for those wanting that. 

This was a successful lab and served well at building an understanding of multiple tools and their uses in taking real data and providing an analysis for practical purposes. Thank you.

V/r

Brandon

Collection and Projection, Lab 3

    This was a multi-subject lab week. For the first part, it combined some physical activity in and around my local neighborhood for in situ data point acquisition. Then the second part looked at what and how projections affect the presentation of data and maps. 

Part I. Field Mapping and Data Collection

The goal: collect data on safety features in my local neighborhood.
My target: fire hydrants in and around the common areas of my neighborhood.
The process: build a feature layer shell to house the collected information, host and sync the shell with ArcGIS Online, collect real-world point data with Arc Field Map from my phone, present. 

The brief process above makes it seen simpler than it was. This multi-software, multi-step process, played to the strengths of several different programs and methods. First, ArcGIS Pro was used to build the feature layer shell that would essentially be the data base organization tool for the collected data. then this was hosted on ArcGIS online in order to be able to sync both the computer software with the phone application used for collecting and storing the points, Arc Field Map. This was a particularly useful tool for collecting data. 

While I did all of my data collection connected to cell signal, it can be done offline instead. This is particularly useful for inside buildings or in other areas with limited or no signal. For the collection, 3 classes or condition categories were established. They are; excellent for like new condition, fair for some damage but overall functional, and poor being broken, damaged, in need of replacement. 

Once this was established, I walked all the way around the neighborhood and through the central common area. I identified 20 fire hydrants along this path. I identified 3 hydrants in the excellent category with brand-new fresh and shiny yellow paint with no cracking. Oppositely, there was only 1 poor-condition hydrant which had significant rusting, pitting, and worn hydrant nuts which could prevent them from being unscrewed in a timely manner during an emergency. The other 16 were in fair shape with some rust spots and cracking and peeling of paint, but otherwise, they should be completely functional. 

After collecting the points and reconnecting to Arc Online and ArcGIS Pro, the goal was to develop several different methods for sharing the collected data. These were through a Map Package from ArcGIS Pro, through Arc Onlines web maps, and also as an exported KML for use in Google Earth. The deliverable below is an example of the upload into Google Earth. Which KML sharing is one of the most useful and easiest sharing methods. However, for more interaction with the data itself, I would rely upon ArcGIS Pro or the Online Webmaps. 

Part II. Projections 

Map projections are the methods in which the spherical earth is transposed to a 2d or flat map. Different projections seek to retain different qualities of a map, such as the shape presented, or the area of a particular region or shape, or the linear distance and direction from one place to another depending on scale. 

In this part of the lab, a basic map of Florida divided into counties was given three different projections to compare differences and similarities. Surprise, Florida has the same shape in all three. However, there are slight differences to its orientation, that the scale below, unfortunately, doesn't highlight well. The UTM projection specifically has a slight skew counter clockwise of the others. The more prominent comparison is in the Area Table which highlights four counties spread across the state. These 4 counties show differences in size (miles squared) for each. You can see that the Albers and State Plane projections lend incredibly similar results. They range from nearly identical out to approx 3-4 square miles separate. Whereas the Albers and UTM projections provide the biggest differences, up to approximately 18 square miles difference for the larger southern counties of Polk and Miami-Dade.  

One of the primary goals here is to show the importance of having data sets or products with consistent projections to allow for accurate calculations and analysis. There are two potential projections issues. either having an inaccurate projection to the data, or having no projection information assigned at all. Arc GIS software does a great job at reprojecting on the fly, that is converting new layers to whatever the base projection of the project is. However, if there is no projection associated or Arc can't determine what it is, then it can provide erroneous results. 

This was also evidenced with a Raster (picture) dataset which had an 'unknown' projection, which when added to the base map, appeared drastically out of position. That is until the projection was defined for it, to then associate appropriately. Below is the end result map showing the three projections and adding a little of my own desired Red, White, and Blue flair. 

Ultimately, this was a fun experience working on many facets of data collection and depiction. While the two parts seem distinctly different, they are apart of the full understanding and generation of a broader project from nothing to full depiction. Onward to the next project. 

V/r

Brandon

Cartography and the Essential Elements: Lab Two

     Aesthetics are a big part of map making. But they aren't the only thing that go into it. Besides, there are as many aesthetic styles as there are map makers. What's more than aesthetics in map making? The professionalism which is demonstrated through the usage of essential elements. The essential elements that we are being taught to always incorporate into our maps include; Worthwhile Titles, Legend, North Arrow, Scale Bar, Attribution to Creator, Date and Sources, and Effective Bordering. 

    This week's lab was an exercise in employing the essential elements while generating a practical layout. As well as letting the creator start to get a feel for their own style. Big picture, it is an overview of where the UWF Main Campus is in Escambia County, Florida. It is highlighted alongside a couple of the prominent interstates and local cities. There is also an inset highlighting the county's location in the greater state area. 

    As I crafted this map I wanted to mute the background areas outside of the county, while still leaving them recognizable. Additionally, I wanted to draw focus to the lower portion of the map where UWF is, and where most of the key elements are. The brightest item is the red star indicating UWF, and everything else surrounds that. All of the text elements outside of the Key were manual entry text to afford me the most flexibility with the font and background sizing, positioning, and manipulation. 

    One area that I feel is underutilized is the semi-void square that is northern Escambia County. While I did think about putting the logo in that region I thought it would unnecessarily draw attention to that area. I thought it better to have the logo adjacent to the campus location, even outside the county to keep from obscuring other elements but to be in visual line with the campus. 

    There is plenty of alternative options that could have been made with the legend, inset, and open county space, but I think it is well balanced with the more subtle elements like the source, creator, date, scale, and north arrows information presentation. Ultimately, for highlighting UWF amongst the rest of the county and starting the creative journey, I think it is successful. Thank you.

V/r

Brandon