Month: February 2026

Chapter 7 

Chapter 7 was devoted entirely to editing existing features and creating new features, which turned out to be one of the most practical chapters so far in this tutorial. The tutorial has managed to cover a major portion of creating, moving, modifying, and deleting polygon features, which is quite different from what we have been doing so far in this tutorial.The chapter focused our attention on the fact that sometimes we, as GIS users, need to create our own spatial data. One of the most beneficial features of the tutorial is the use of the Modify Features pane, especially in choosing the vertices of specific buildings and making modifications to them as desired. We were taken through the tutorial step by step on how to utilize the Move, Rotate, Continue Feature, Reshape, and Split features, which showed us just how flexible ArcGIS Pro is, especially in making modifications to our features. We also learned the significance of precision in choosing our features because we need to be precise in choosing the feature that we would like to edit; otherwise, if we click slightly away from the feature, we may end up choosing the entire layer instead of the specific polygon. The most intriguing feature of the tutorial is the use of the smoothing edges feature. We learned how we can utilize the feature of smoothing edges on our features so that our features will be smoother instead of having jagged edges.Overall, this chapter helped me understand more about how GIS data is created, updated, and even corrected. I can definitely see myself using these tools again, particularly with any future assignments that require creating my own data or modifying data that already exists.

Chapter 8 

Chapter 8, like the last chapter, was short, and I feel like I have a much greater understanding of what geocoding is, particularly. It explained to me that geocoding is the process by which a set of location fields, such as street addresses or ZIP codes, is matched to the corresponding locations within a feature class. It also walked me through the steps necessary to use the ZIP code data, which is a simpler process, but then went on to explain how to use a full table of street addresses with an address locator. What I think this chapter did particularly well was to explain each part of the process within the Geo processing pane, making it easy to understand the relationship between the table, the locator, and the feature class. It also explained to me the importance of reviewing the addresses before geocoding to make sure they correlate with the locator. Should the addresses not correlate correctly, then the percentage of matches will decrease, resulting in a potentially incorrect outcome. It also showed how to view the matches once the geocoding is done. Another useful section for me was the part where I learned how to add the newly geocoded points to the map and visualize their distribution. While this chapter focused more on the process of creating the points, there was still a lot of emphasis on the importance of visualization when reviewing the results to spot possible errors. As this chapter used both ZIP codes and addresses, there was a distinction between coarse-level and fine-level geocoding. This will likely prove to be useful later on in the course, as accurate location information is the basis for almost all types of analysis. I can certainly foresee this chapter proving useful to me when working on the final project or any assignment with a table that includes spatial data.

Chapter 9 

Chapter 9 described one of the most used GIS tool types; buffer analysis. This chapter is one of the longer and more detailed ones because there are many ways of proximity measurement with geo processing tools. The chapter began with a tutorial on how to create a basic buffer around a feature at a specified distance and how the result differs when features are dissolved or not. The chapter also taught how to create multi-ring buffers and how to visualize data at different levels of proximity. This was a good approach to understanding how proximity affects data from a point or a polygon feature. The chapter also taught how to use various tools in the Geo processing pane. The chapter was also good at emphasizing the importance of setting input layers, choosing output locations, and making sure all parameters are filled out when running a tool. The chapter was also good at emphasizing the importance of keeping track of geo databases so that output layers are not lost, which can easily happen. What I thought was most interesting was how this chapter taught how buffers are a fundamental tool in many real-world analyses, including the ones in this chapter. The chapter taught how proximity could be used as a tool for analyzing and understanding different types of analyses, such as creating zones, finding areas of interest, and preparing for later network analyses. Although this chapter did not go in-depth with network analyses, it did show how buffers are a subset of this type of spatial analysis. Chapter 9 gave me a better understanding of spatial relationships and how to create meaningful proximity layers with the buffer tool in ArcGIS Pro.

 

 

Zip Code: All zip codes within Delaware County, and is updated and published monthly. 

Street Centerline: The center of pavement of public and private roads in Delaware County, updated annually and published monthly. 

MASG: Master Street Address Guide, represents the 28 different political jurisdictions in Delaware County. 

Recorded Document: Points that represent recorded miscellaneous documents in Delaware County, it is updated weekly and published monthly. 

Survey: Point coverage that represents surveys of land in Delaware County, it is updated daily and published monthly. 

GPS: All GPS monuments that were established in 1991 and 1997, it is updated as needed and published monthly. 

Parcel: Polygons that represent all cadastral (used for taxation) parcel lines in Delaware County, it is maintained on a daily basis and is published monthly. 

Subdivision: All subdivisions and condos recorded in Delaware County, it is updated daily and published monthly. 

School District: All the school districts in Delaware County, and is updated as needed and published monthly. 

Annexation: Delaware County’s annexations and conforming boundaries from 1853 to present, and is updated as needed once an annexation has been recorded, and is updated monthly. 

Township: Consists of the 19 townships that make up Delaware County, and is updated as needed and published monthly. 

Tax District: All the tax districts in Delaware County, and is defined by the auditor’s real estate office. It is updated as needed and published monthly. 

Address Point: Spatially accurate representation of all the certified addresses within Delaware County, and it is updated daily and published monthly. 

Municipality: All municipalities in Delaware County. 

Condo: All condominium polygons in Delaware County that have been recorded. 

Precincts: All of the voting precincts in Delaware County, it is updated as needed and published by the Delaware County Board of Elections. 

PLSS: Public Land Survey System polygons for both the US Military and the Virginia Military Survey Districts, and is updated as needed and published monthly. 

Delaware County E911 Data: The State of Ohio Location Based Response System has a spatially accurate representation of all certified addresses in Delaware County, and it is updated daily and published monthly. 

Farm Lot: All the farmlots in US Military and Virginia Military Survey Districts of Delaware County, updated as needed where the new surveys have been recorded. 

Building Outline 2023: All of the Building Outlines from 2023. 

Railroads: The locations of all the railroads in Delaware County. 

Dedicated ROW: All lines that are designated Right-Of-Way within Delaware County, it is updated daily and published monthly. 

Original Township: The original boundaries of the townships in Delaware County before they were affected by tax district changes. 

Building Outline 2021: The building outlines for all structures in Delaware County and is updated on an as needed basis. 

Map Sheet: All map sheets within Delaware County. 

Hydrology: All major waterways in Delaware County, it is updated as needed and published monthly. 

ROW: All lines that are designated Right-Of-Way within Delaware County, it is updated as needed and published monthly. 

2024 Aerial Imagery: Aerial images from 2024 of Delaware County. 

Delaware County GIS Data Extract Web Map: Allows users to extract Delaware County GIS information in different formats. 

2022 Leaf-On Imagery (SID File): 2022 imagery 12in resolution. 

Delaware County GIS Data Extract: Allows users to extract Delaware County GIS data. 

Address Points – DXF: The LBRS Address Points data provides a spatially accurate placement of addresses within a given parcel, and is updated as needed. 

Delaware County Contours: 2018 Two Foot contours for Delaware County. 

2021 Imagery (SID File): Images of Delaware County from 2021. 

Street Centerlines – DXF: The LBRS Street Centerlines depict the center of pavement of public and private roads in Delaware County, and was collected by field observation. 

Building Outlines – DXF: An image of the building outlines in Delaware County. 

Auditor Logo: The logo of the Auditor’s GIS Office in Delaware County. 

Fall Background: The background for different GIS data. 

Building Outline 2024: The outlines of buildings in Delaware County from 2024. 

 

I put the Hydrology layer on top of the Parcel and StreetCenterline so I could actually see it. The Add Data feature was very helpful in this, and I have attached my map and an image of my catalog pane.

Chapter 4: Mapping Density

This chapter mostly focused on what a density surface is and how GIS takes point or line data, like businesses, roads, or population centroids, and turns it into a smooth surface that shows where things are more concentrated. The chapter explains that cell size really matters because smaller cells show more detail but take longer to process, while larger cells make the patterns more general and can hide smaller variations. I also learned about search radius, which is basically how far the GIS looks around each cell when calculating density. A smaller search radius shows more local differences, but if it is too small, broader patterns might not show up. A larger search radius smooths everything out and shows bigger trends, but it can also blur details that might matter. Another important idea is that GIS can calculate density using simple or weighted methods, where the weighted method gives more importance to features closer to the center of the search area and usually creates smoother and easier to read maps. The chapter also talks about choosing the right units for density, like per square mile or per acre, and how using very large units can make density values seem misleading even if the overall pattern stays the same.
It was also very interesting to learn how much control you actually have over the patterns you end up seeing. Just changing the cell size or the search radius can completely change how the map looks, even when the data itself doesn’t change at all. The examples showing how patterns become too blocky with large cells, or too smoothed out with a big search radius, makes it clear that there is not really one “correct” setting. It depends on what kind of pattern you are trying to understand. Another thing that I noticed was that the highest density area on a map does not always mean something is actually located there, since density is calculated based on nearby features. That made me realize that density maps are more about showing general patterns than exact locations.

Chapter 5: Finding What’s Inside

Some of the key things I picked up from this chapter were how GIS is used to figure out what falls inside certain areas and how that helps compare places in a more meaningful way. The chapter explains that you can do this in a few ways: sometimes you can just draw the boundary on top of features to visually see what is inside, sometimes you select the features inside an area to get a list or count, and other times you actually overlay layers to measure what is inside each area. This makes it possible to answer questions like how much forest is inside each watershed, which parcels fall at least partly inside a floodplain, or how many roads run through a protected area. It also talks about vector and raster overlay, where vector overlay is more precise but slower and can create small and messy pieces called slivers, while raster overlay is usually faster and avoids slivers but depends a lot on cell size for accuracy.
Another thing that I found important was how the type of data changes what kind of summary you can get at the end. When working with categories, like land cover types, you can summarize how much of each category is inside an area and even convert it into percentages to compare areas fairly. When working with continuous data, like elevation or precipitation, GIS calculates statistics such as the mean, minimum, maximum, range, or standard deviation for each area. The chapter also shows how results end up in tables that can be joined back to maps, which makes it easier to compare areas visually instead of just guessing from the map.
It also made me think about how often people use this kind of analysis without realizing it, like when cities decide where to put new services or when environmental groups compare protected areas. It makes me curious about what kinds of “what’s inside” questions are most common in real GIS jobs.

Chapter 6: Finding What’s Nearby

This chapter helped me understand what “nearby” actually means in GIS and how GIS can define it in different ways depending on what you actually mean by near. Sometimes it is just straight-line distance, and sometimes it is just about travel range, like what is within a 3-minute drive of a fire station. This chapter explains that “near” can be measured by distance, but it can also be measured by cost, especially time. It also introduces three main approaches, which are: using straight-line distance, measuring distance or cost over a network (like streets), and calculating cost over a surface for overland travel. I also learned about details that can change results, like planar vs geodesic distance (flat vs curved Earth) and the difference between inclusive rings and distinct bands when you need multiple distance ranges.
As always, I have found this very important to know how much the method you choose can change the story the map tells, even if the starting point is the same. For example, a circle around a store might be fine for a rough estimate, but it is not the same as a real 15-minute drive because streets, turns, traffic, and one-way roads can shape how people actually move. This chapter makes that really clear with the network examples, especially when it talks about assigning “impedance” to street segments using distance, time, or money. I also liked the idea that you can build more realistic travel time by adding turn and stop costs using a turntable, because that is the kind of small detail that matters a lot for something like emergency response. And I didn’t realize there were so many output options like buffers, selections, point-to-point distances, spider diagrams, distance surfaces, and service area boundaries like (compact vs general) depending on what you are trying to show.
If someone uses straight-line distance for something that really depends on travel time, the results can be misleading, especially in places with rivers, highways, or weird street layouts. That made me wonder how GIS people deal with real projects when the data isn’t always perfect. Like, if you don’t have exact speed limits, turn delays, or updated road closures, how do you decide what’s “good enough” without making the map seem more accurate than it actually is?

Data Inventory:

Zip Code: Contains all of the zip codes that fall (either completely or partially) within Delaware County. These parcels were created in 2005 according to property addresses, likely to ensure that properties were not split across zip codes.

Street Centerline: This data depicts the center of the pavement of all public and private roads in Delaware County to give a fair approximation of street routes throughout the county. This street system is called the Ohio Location-Based Response System (LBRS) and is heavily used by ODOT and emergency services. Street segments are measured from vertex to vertex.

MSAG: The Master Street Address Guide (MSAG) delineates townships and municipalities in Delaware County. Most townships are simple geometric rectangles, but the municipalities are irregularly shaped. Some municipalities are also their own townships, and they are located inside of other townships, as is the case with Sunbury Township located inside of Berkshire Township.

Recorded Document: These are records that do not match up with the subdivisions that currently exist on the Delaware County map. They include records of vacations, cemeteries, road centerline surveys, and utilities easements.

Survey: This dataset is a collection of the locations of all recorded land surveys in Delaware County more recent than Old Survey Volumes 1-11. There is a pretty high density of land surveys all throughout the county, except over bodies of water and in parks like Alum Creek State Park, Delaware State Park, and the Dover Recreation Area.

GPS: This dataset displays the shapefile of GPS monuments, or metal disks in the ground that mark latitude and longitude and serve as reference points. These monuments were established between 1991 and 1997. 

Parcel: This dataset is incredibly detailed, showing land parcels in Delaware by ownership. Contains extensive information on each property, such as the address, current owner, sale history, and number of rooms.

Subdivision: This dataset contains subdivisions and condos in Delaware County. (These types of housing are typically higher-density residential areas.) Most subdivisions appear to be concentrated around the town of Delaware or the southern part of the county.

School District: All the school districts in Delaware County are displayed in this data set. Similar to the Zip Code data set, some small portions of school districts that mostly fall within adjacent counties are included.

Tax District: The tax district dataset appears to line up similarly to the MSAG data set but includes a few more divisions. Most of the tax districts around municipalities are shaped irregularly and are even sometimes nested shapes within the more geometric townships.

Annexation: This dataset shows annexations in Delaware County. They are concentrated around towns like Delaware, Sunbury, Powell, and Westerville.

Township: Shows all of the townships in Delaware County. Very similar to the MSAG dataset.

Address Point: This dataset uses LBRS to show all registered addresses in a shapefile. The point on the map is located in the centroid of the building.

Municipality: This dataset contains the municipality parcels that are noticeable in the MSAG and Township datasets.

Condo: Condo polygons are shown in this dataset. They are pretty small and well-dispersed, which, when compared to the Subdivision dataset, leads me to believe that Delaware County has lots more houses in subdivisions than condos.

Precincts: Delaware County voting precincts line up pretty well with township and municipality parcels but are divided within into much smaller areas.

PLSS: This dataset contains Public Land Survey System (PLSS) polygons, most of which are near perfect squares. However, the west side of Delaware County comprises more irregular PLSS polygons.

Delaware County E911 Data: This dataset uses an LBRS system of Address Points and is used in particular by 911 Emergency Services. Other uses include appraisal mapping, geocoding, reporting accidents, and managing disasters. This is measured in terms of US Military and Virginia Military Survey Districts.

Farm Lot: Contains all farm lots (as measured by military districts). Many are different shapes: square, long and thin, uniform rectangular, or irregular (as in the western and central parts of the county).

Building Outline (2021, 2023, 2024): Contains all building outlines in Delaware County. Very reminiscent of a Google Maps view. Each of the three databases was updated in its respective year.

Dedicated ROW: ROW stands for Right-of-Way, which is a type of easement, so it shows accessible street routes in the form of line data. It appears that streets that are not included as ROW routes are in private subdivisions or similar areas.

Railroads: The dataset highlights railroads running through Delaware County, and it appears that most of them run north-south.

Original Township: Displays boundaries of Delaware County townships prior to division by tax districts. Consists of 18 original townships. The eastern portion of the county has rectangular parcels, and the western portion’s parcels are more irregularly shaped, which is consistent with other similar datasets.

Map Sheet: A map sheet is just a map that is part of a larger map series. The data appears to show data at the sub-municipality or sub-township level. The smallest parcels are clustered around the cities of Delaware and Sunbury, and in the southern portion of Delaware County.

Hydrology: Contains the portions of all *major* waterways in Delaware County. Many small ponds and lakes on the map do not appear to be counted in this dataset.

ROW: Just like the Dedicated ROW dataset, this contains all line data of street rights-of-way in Delaware County.

Delaware County Contours: Contains two-foot contours showing the topography of Delaware County. This data was updated in 2018. It is in the form of a downloadable geodatabase.

Map:

Figure 1: Delaware County Parcels (yellow), Street Centerline (green), and Hydrology (blue) layers.

Once I remembered I had to use the Add Folder button to add my files into the Catalog pane, it was smooth sailing making this map!

 

 

chapter 7

I enjoyed completing chapter 7, especially the first few tutorials, where I got to just move around and adjust the outlines of buildings and correct them. It was fun to do but it also made a lot of sense. I liked that there were only 4 in this one, I was able to take in each tutorial really fast and go back through to make sure I fully understood. I actually really enjoyed doing these ones because I have gotten much more accustomed to all the contols like adding features, symbols, bookmarks, and the repetitive tabs.

chapter 8

It was really easy to do these ones, considering it was only 2 tutorials, but I still feel like I gained a lot from them and got valuable knowledge on Geocoding and analyzing locations, plus learning about things with zipcodes. Most of the infomation was stuff I had begun to feel confident with, doing things with the creating locator, for example, went smoothly. When rematching attendees by zipcode on 8-2 I got a bit lost at a certain part where I had to click the match button for the zipcodes, for some reason it wouldn’t load for me. Also, the create locator section kept resetting on me and deleting my data.

chapter 9

This one had the most tutorials technically but it still wasnt a long time to get through. Buffers were something I had been confused on before too, so i liked being reintroduced to this chapter. I know you use them to find whats near the features being buffered, but seeing them in action helped a lot. 9-1 and 9-2 on the swimming pools in Pittsburgh and estimating the number of youths was really quick and easy. I also thought it was cool how it highlighted the ones within half a mile. I also thought the dissolve option was really neat, helping with overlapping buffer rings helps make it much better to look at.

9-3 it estimates gravity models of geography, you can see the amount of attraction between two features. I had trouble figuring this part out, it was probably the hardest for me out of all the units. It wasnt that bad but I kept getting stuck at the parts where it would ask me to click the service area layer, it would genuinely not give me what I needed, it took me a second to try and figure out how to get through these tutorials at the end but it was still cool. I think analyzing the optimal soltuions and doing your turn was difficult, but it was also a pretty informative.

 

 

 

 

 

 

Chapter 7

Chapter 7 introduced the practical processes involved in editing and creating spatial data. It emphasizes that GIS is not simply about viewing maps but also actively maintaining them as well. Through moving and reshaping polygon features of the campus, it became clear to me that spatial data must continuously evolve. The ability to edit vertices and create new feature classes such as parking lots and bus stops seemed quite interesting to me. I found applying the Smooth Polygon tool to be easy and the end result of it aesthetically pleasing. 

Chapter 8

Chapter 8 explored the process of geocoding, which connects tabular data such as addresses and zip codes to geographic locations on a map. The only part I found interesting in this reading was the process of reviewing matched and unmatched records – was almost like a puzzle. It demonstrated that GIS analysis depends not only on automated tools but also on critical evaluation. This chapter emphasized that spatial accuracy directly influences the validity of conclusions drawn from mapped data. The consequences of having incorrect mapped data could affect overall public safety, or even service delivery. Moving on, the buffer analysis tutorial around public swimming pools in Pittsburgh taught me how juxtaposition plays a role in accessibility. This idea particularly applies to youths living within a half-mile radius of recreational facilities – in other words, the pools.

Chapter 9

This last chapter focused mainly on spatial analysis, specifically showing how GIS can move beyond simple mapping. Using buffer tools around public swimming pools in Pittsburgh, I calculated how many youths live within a half-mile of a pool. Afterwards, I calculated what percentage of the city’s youth population that represents (I am thankful I don’t have to do actual calculations. This made the concept of accessibility much more believable, especially when thinking about how distance affects whether someone will realistically use a public facility. With this in mind, I can see how it is important to look at maps such as this in order to acquire the most efficient spot for a public facility.  It also became clear that straight-line buffers do not always reflect real travel conditions. It is not accurate when terrain and bridges are not included. My thoughts for this chapter include knowledge of how GIS can support planning decisions, such as which public restrooms to shut down because of low funds. It is advantageous because it can turn spatial data into measurable series, ones you can apply to real-word problems. 

 

7:

     In chapter seven, we learned how to edit polygon features as well as how to use specific tools to directly depict these features. I really enjoyed chapter 7 as it reminded me of working with vectors in Adobe Illustrator, where you can adjust shapes using various tools and vertex points. When creating polygon features, I found the trace tool very satisfying to use. Learning how to smooth out the edges of the polygon was also very satisfying. I also found it interesting that when the chapter was teaching us how to transform features, it used an AutoCAD drawing as a base to transform. Until now, I had no clue that AutoCAD could be utilized by ArcGIS, as I am semi-familiar with AutoCAD software. In chapter 7, it also says we can click and hold the wheel button on our mouse in order to pan around the map when in editing mode. This was helpful information that I wish the book had told us when making feature edits earlier. 

8:

        Chapter eight was more confusing than the previous chapter for me. It has to do with geocoding, which is the ability to convert various geographic data descriptions/addresses into geographic coordinates that can be displayed on a map. To be specific to chapter 8, it taught us to geocode using zip codes and street centerlines. These actions that allow for geocoding are done mostly through the use of specific tools found in the Geoprocessing pane. Throughout the later tutorials, sometimes they dont actually say “run tool” when that step needs to be conducted. I guess it can be assumed that the tool needs to be run, but I prefer tutorials that are very detailed and explicit in their instructions. This was a small detail, but it made the workflow feel slightly less clear and more frustrating at times. It also doesn’t help that you don’t get a visual on what you’re imputing until the very end of the imput.

 

9:

        I ended up understanding the concepts from chapter nine much better than in chapter eight. With the concept being how to work with/display spatial data, which is (to my understanding) the area surrounding a data point that can be utilized to answer specific questions. For example, we created service areas to help compare how often a pool is used by youths depending on the travel time to said pool. The travel time is visualized by the service areas as ring boundaries around the pool data point. As expected, the use rate of the pool was decided as travel time went up. I also may have found this chapter easier because, unlike the previous chapter, you could visually see the map changing as you performed each little step. 

Chapter 7:

In this tutorial, I learned more about tools used for manual digitization by tracing and a more in-depth understanding of the use of base maps. Base maps will help me edit and create vector map features while also learning how to use other existing layers like streets as spatial guides for digitizing features. Also learned a little about lidar and how it is used as a reference for heads-up digitizing. ArcGIS field maps or GPS receivers that collect data about longitude and latitude to then create vector features and build information modeling, can then be imported into GIS maps creating feature classes. I had some issues when trying to shape and form a building that fits to scale in the map while doing this chapter. This tutorial was definitely one of the easier ones, but it was pretty hard to make vector points when I was confused on the correlation between the directions and my own screen. It took me a while but I eventually got it, I then promptly closed my computer and didn’t reopen ArcGIS until the next day. I think that in general, this chapter had some fairly easy to follow directions, but it also took me a longer amount of time to accomplish. It made me realize that I do better when dealing with point locations on maps, and sometimes have more trouble when working with CAD drawings and using cartography on zoom scale maps. In this chapter, the outlines of buildings in my practice GIS sites were a little askew and not as lined up as in the directions. This can also be said when talking about what the maps looked like and how they differed between my directions and my computer. This caused me to be confused in tutorials like the second one where I had to simply map bus stop locations as points. I was confused because part of the map that I had, differed from the pictures and images that were in the diffraction, causing me to guess at some parts on where points are supposed to go (not to say that mapping these points was a high stakes operation or anything like that). 

 

 

Chapter 8:

In this chapter, I learned about geocoding and how it matches location fields in tabular data to corresponding fields in existing feature classes in order to map the tabular data. Through these location fields, we are able to geocode their location by using zip code polygon or street feature classes. GIS has to use “fuzzy matching”, and make matches that are approximate as opposed to being one hundred percent accurate. Fuzzy matches are made through  rule-based expert system software, ArcGIS is seen as a system. The geocoding expert system can be seen as attempting to mimic a mail delivery person through using expert knowledge to get mail that was messed up or written differently, to the correct address. This can be shown in GIS through source tables, reference data, locator, or the geocoding tool. This tutorial only had two sections so I feel as though I don’t have as much to talk about or say that I had difficulties with (though I definitely did have trouble figuring things out throughout this section and chapter). I do feel like it was easier to run through the two tutorials, especially in comparison to the other chapters and tutorials that I had to push and force myself to finish. This feeling of understanding can be associated with the fact that I paid attention and was learning how to do things and locate different tools and functions white struggling and feeling like I wasn’t learning anything at all, genuinely just following the directions blindly. Although I have great disdain for the “your turn” sections of the reading, they really do check your knowledge, comprehension skills, as well as your problem solving skills while recalling everything that you have previously learned and read about. If I were to go back and compare how I handled these tutorials when I first started this course, confused on where the content pane was, and stressing out forgetting how to symbolize circles with different traits there is a big difference. I would like to think that I am learning and improving, not to say that I’m ready to tackle the final/midterm which has a fast approaching due date. 🙁

 I again forgot to take pictures of my work in chapter 7 and 8 so I will be leaving this message here incase I forget to go back and collect images for some of my work. 

 

Chapter 9: 

In this chapter I had some issues but it was mostly in relation to the amount of work that I had to do, in relation to the other 2 previous chapters of work. In this chapter, I visualized spatial data so that I and other people can get answers or solutions to problems. I learned about four different spatial buffers including: buffers, service areas, facility location models, and clustering. I also learned about and used another spatial data type, “the network data set”. Which we use to estimate travel distance or time on a street network. We apparently used the free service version in GIS, not that I would have honestly been able to tell the difference between the two in the first place. In the first tutorial, I learned about using buffers for proximity analysis. With a buffer being a polygon surrounding map featured of a feature class. I tough that this first tutorial looked cool, and was fairly easy to do. I will say that I had issues trying to understand what they meant when they said to find the “number and percentage of youths” within a distance after having created a one mile buffer. I was confused because I didn’t really understand how we were supposed to be getting these calculations, or how we were supposed to be comparing them. I was also confused and I believe that I might have done my map wrong because when I compared what I had created to the picture, they had darker shading in the circles then I did. In the third tutorial I also had fun making the map and adding the colors, but my computer kept freezing while I was trying to swap that color out per the instructions so I was getting fairly frustrated. 

Chapter 7

In chapter seven, we learned how to create polygon and point features. We also used a CAD drawing and learned how to match overlays with the basemap features. I really enjoyed this chapter. It was much more intuitive than the previous ones, and I absorbed the material quickly. We began by matching the outline to the buildings on the campus, and learned how to create intersections to split buildings. We then learned how to use the cartography tools to smooth areas on the map. The transformation with the links on the CAD drawing confused me at first, but the drawing helped me realize what it was asking us to do.

Chapter 8

Chapter eight was short but had us do a lot. We learned a lot about geocoding and how to use zip codes. Most of the chapter wasn’t a tutorial, but an introduction to geocoding. The first tutorial taught us how to apply zip codes. I thought it was interesting that it does it by matching rates with percentages. I never knew that was how it worked. I got an error when doing the collect events section in the first tutorial, but it looked the same as the picture in the textbook. The second tutorial went over how to use geocoding with addresses. It was a little similar in theory to the first tutorial, but it went more in-depth about connecting the address to a zip code and assigning them based on a match.

Chapter 9

Chapter nine was also very interesting. It taught us about buffers and service areas. I feel like buffers are a very common thing to have on a map, so I am glad we went over how to apply these in ArcGIS. The first two tutorials were pretty self-explanatory. I was a bit confused with the rest of the chapter in some parts, because of the wording. It was also hard to tell what exactly we were doing. I really enjoyed learning everything in the fourth tutorial. I feel like learning how to find the most attended pools is something that is very important to use in real life.

Chapter 7: 

Chapter 7 overall was easier for me because it was all very Intuitive. Formatting buildings as polygons and using the tools to relocate their outlines was pretty simple. I had to try a few times on splitting the buildings in two because I kept getting error messages but figured it out in the end. I also find it interesting that a lot of the tutorials involve Pittsburg, maybe the person writing it is from there.

Chapter 8: 

My major problem with this chapter was finding where the tools were and making sure I was accessing the right ones. This continues to be my main issue as I don’t always remember where every tool we have used is. I found it helpful to ask google ai for guidance on where I could find the tools in Arc Gis and that turned out to be fairly helpful. This chapter dealt with zip code data and how to change symbology for it along with a few other things. 

Chapter 9: 

I forgot to take a picture for this chapter but this chapter felt smoother than the rest. It was interesting seeing all the different visuals from the different tools used and how they layered with each other to make the full picture. I did struggle some and had to redo a few steps but overall it wasn’t too bad.