Geography 291: Geospatial Analysis with Desktop GIS

Tutorial

Chapter 4

 

This chapter was all about working with datasets. The first 2 subchapter modules had you implement data sets into the tutorial and started with some dataset management. Later chapters focused on summing data, selecting by certain data fields, and joining data types to find the answers you desire. There was also the introduction of SQL queries which gave some baseline knowledge on how one might code within the SQL language to get the desired data.

 Above is a Maricopa County figure separated by municipalities that was implemented in the first sub module.

You could switch between the normal easy to read GIS interface and the SQL coding tab to see how the identifiers would be used and written in a SQL query.

 

Chapter 5

First modules focused on teaching about distortion in a chunk of the world displayed. It illustrates how world maps are less accurate than smaller maps where all features are on a similar geographic plane. Ex: maps of the US are more accurate than maps of the world that have inaccurate representations of Greenland, Antarctica, etc. Many of the modules in this chapter also introduced various coordinate systems to label distinct features at exact locations. There was a lot of work that focused on finding the correct downloaded data in file explorer and implementing it into the GIS. I probably struggled with this chapter the most but I thought the last module was pretty cool and I can see the real world applications for these processes.

This first map displays libraries spread across New York City.

This second one is a map of Hennepin County, Minnesota with elevation contours and bike routes downloaded from the USGS website.

Chapter 6

This chapter had us focus on merging and breaking up spatial entities using both the merge tool and dissolving polygons to generalize distinct areas into more regular polygons. It had us use selection tools to find all streets within an area and used the pairwise clip tool to cut off unnecessary street data(pictured below).

There was a large focus on using geoprocessing tools in this chapter as we did various processes. The image below is a shot of New York fire streets. We can manipulate the data in a way that would be useful to firemen in the city. 

The last module showed similar use by showing the total number of people with disabilities in certain tracts and providing the information of where these tracts fall within fire station territories. I assume that this is useful because firemen have to assume that some people who are disabled(ex: in a wheelchair, can’t get out of bed) may be harder to account for at the scene of a fire or they may need medical assistance themselves.

Week 5:

 

Chapter 4:

In Chapter 4, we worked with spatial databases and databases in general. This was honestly very difficult, and I did not enjoy it. Having to download the data from the different websites was pretty challenging for me and was not the happiest of my times while doing GIS. This can be a very important thing, though, since some of this data could be important to a specific thing we are looking into, or it could also be something that increases information in our study.

Chapter 5:

In this chapter, we learned how to look at maps using specific locations. Throughout the chapter, we changed coordinates for each of the specific projects and made them show specific areas that were important to search.

 

Chapter 6:

In this chapter we learned a lot about geoprocessing, which I thought was the easiest chapter out of the 3 we did. This helps us build study areas in our GIS maps where tasks are preformed.  As in this chapter we did a good amount of looking into the Manhattan fire company’s works. We looked at a lot of attribute tables as well which helped point out spatial features, and other important information.

 

Chaptwer 4-

There was alot of new information learned in chapter four. We imported data into file geodatabases. Modify attribute tables and fields. Use Python expressions to calculate fields, Join tables. Get an introduction to SQL query criteria. Carry out attribute queries. Point data to polygon summary data.

 

Chapter 5-

Discussed latitude and longitude coordinates that pinpoint your location precisely on the surface of the earth. we learned about latitude and longitude coordinates and their geographic coordinate system. We learned about map projections, making flat maps from the nearly spherical earth.  This chapter provided some guidelines for choosing a projection.

 

 

Chapter 6-

We practiced geoprocessing and set of tools for processing geographic data. We had to use geoprocessing tools to build study areas in a GIS and perform tasks.  Learning how to extract a subset of spatial features from a map using attribute or spatial queries. Processing and prepare layers for emergency management officials in New York City’s Manhattan borough and one of its neighborhoods, the Upper West Side.

Chapter 4: 

I found this chapter to be very easy compared to last week. Everything was very straightforward and easy to understand. However, there were still a few parts I found difficult, like when I had to insert a code. The code given didn’t work and I had to ask another person for help, eventually after trial and error we figured out what was wrong and I smoothly went through the rest of chapter 4. I think the overall goal for this chapter was to teach us how to use the python coding and how to view attributes. (I’m not sure if the book or site is out of date, but it kept saying for the code to put robbery and burglaries instead of robberies or burglaries).

Chapter 5: 

initially when I looked at chapter 5 I thought it was going to take forever, but thankfully most of the tutorials were only like 4 steps.  This chapter was pretty chill; I wasn’t completely mind boggled at the random instructions, everything was kind of just right where it said it was and I liked that. All except for 5.5, that tutorial had me completely stumped. It was the longest tutorial, and it was about going to websites and downloading files, then I needed to go to excel and do stuff on there just to go back to ArcGIS and do more stuff on there. Overall, I’d say this chapter was like a 6/10 on difficulty (3 of the points coming from 5.5 alone). I also kept getting annoyed that it would go “open the attribute table, not sort it, ok now close it” just for me to never use it again.

Chapter 6: 

I’d say chapter 6 was about a 4/10 (on difficulty), the steps were laid out better than the others and at the end of each like tutorial there were pictures showing me what mine should look like. Although there were times, I had little mistakes and confused myself the chapter went by smoothly. Most of my confusions were just because of user error (which is why I’d say it’s a 4/10, the faults were mine not confusing instructions). I also think that this chapter was a bit length though, it took me a bit longer than expected. That might just be because I was being asked to use new tools constantly and was trying to understand what I was actually doing.

Delaware county GIS Data:

Within the Delaware GIS data, the first data set is Zip code. Within this data set, all the zip codes in the Delaware county area are recorded. This data set is updated on an as-needed basis. The second data set is Street Centerline, which displays all recorded public and private roads within Delaware county. They collected this data through field observations of addresses and updated it every year. 911 emergency responses, appraisal mapping, disaster management use this data, and more. The next is the Recorded Document, which consists of recorded documents that are not represented by subdivision plates that are active. They update the dataset every week and use it for locating miscellaneous documents. The next is survey which represents surveys of land that were recorded within Delaware and is updated on a daily basis. The GPS dataset identifies all GPS monuments established 1991 and 1997 and is updated on an as-needed basis and contains very low dot density on the map compared to the previous data sets. The dataset includes all subdivisions and condos in Delaware, updates daily, and uses coloured map sections instead of individual dot markers to represent them. Parcel consists of polygons that represent all cadastral parcel lines in Delaware and is updated daily. School districts show all school districts within Delaware and get updated on an -needed basis and use large colored plots to show the separate districts and their locations within Delaware County. The township includes all 19 separate townships of Delaware, and the team updates it as necessary. They symbolise the tax district areas of Delaware on the map with highlighted sections and update it as necessary. Annexation records Delaware’s annexations and confirming boundaries all the way from 1853-present and is updated as-needed and includes very dispersed sections.

GIS Tutorial Chapter 4:

The first 2 chapters, you were creating your own ArcGIS map using the data provided. Calculating the data from the attribute tables challenged me the most in the first two tutorials because I was confused about selecting the field in the table to calculate, but clicking the header helped me figure it out.  My favorite part of this was messing with the databases and creating new ones using the export tool because I found it straightforward. The third tutorial involved using the select by attributes tool and creating additional clauses, and looking at the effects in the attribute table. The next 3 chapters were all very brief and simple and included creating a central point layer, building a spatial join, and creating a table. Calculating the tract name was also challenging to me because I had an issue where I did not highlight the final exclamation point so when I pasted the expression, it was not correct. I am still confused how you know which expression to use for which calculation, but with the tutorial walkthrough in the textbook, it was easy to follow along after I corrected my previous mistake.

Screenshots:

GIS Tutorial Chapter 5:

The first tutorial as well as the second of chapter 5 was extremely simple and displayed how to change map styles in ArcGIS. The third tutorial adds geographic coordinates that display a solid border around the designated post between the two maps. The fourth chapter, you use the export features tool and add the data in the map that you then adjust the symbology for. The fifth tutorial I found to be the most challenging of this chapter because of having to use the Microsoft Excel as well as create a choropleth map at the end using ArcGIS with data from the spreadsheet. I struggled to use Excel because the fields were not letting me delete them, so I had to move on without finishing this part. The final tutorial of this chapter was another simple tutorial that involved going to 2 different websites, browsing them and finding the given downloads, and downloading those and extracting them to the chapter 5 downloads folders. At the very end you simply add the data and adjust the symbology, which was my favorite part because you watched the map change and the impact that it made. This chapter was probably the hardest of this week’s assigned chapters because it was long and the fifth tutorial with the Excel worksheet was quite difficult.

Screenshots:

GIS Tutorial Chapter 6:

I found this chapter to be very simple, but also interesting and insightful. The first tutorial was similar to0 the tutorials in the previous chapter, where you adjusted the symbology and sorted and selected data in the attribute table. In the next tutorial, you use the export features tool and the pair-wise clipping tool, which you use for the rest of this chapter, and it allows you to export a new attribute. The next 3 tutorials, tutorial three, four, and five, were all very brief and followed a similar format with separate tools being used in each. Step 3 involves using the merge tool to combine separate waterways, while step 4 uses the append tool to view combined data points of firehouses and police stations, and step 5 applies the summary statistics tool and pair-wise intersect tool to intersect features and summarise street length. In tutorial 6 you use the union tool to create a new layer and calculate acreage. In the final tutorial, you tabulate the intersection tool to get an estimated number of people with disabilities in the area. This chapter followed a similar pattern with all seven tutorials, which made this one of the easier chapters so far but was insightful in showing how to utilize the various tools.

Screenshots:

Chap 5 

 

Chapter 4 is about learning how to work with spatial databases and databases. For 4.1, I could not put YouthPopulation.gdb as an Output Feature Class. It told me that the folder was empty, so I could not include it or change the name. But when I ran the geoprocessing, I still ended up with the same result that the GIS tutorial textbook showed. 4.1 also wanted me to change the outline for MaricopaCounty and Tracts. But when I found the option for outline width, a big grey shape covered up my screen. 

I wasn’t able to complete all of 4.2; it was hard to modify the tables. And my validate join was invalid, so I could not get Pop youth to join the Tracts feature class. 

I am unable to save the expression “qryDateRange”. I could not find an option for expression under the Select by Attributes tool. So this means I was not able to then reuse a saved query to create a definition query. 

Building the commands for the queries was slightly tedious, but rewarding. 

5.1 explored how to use geographic coordinates. Some things I had to troubleshoot, like under “Projected Coordinate System,” when I double-clicked the folder, nothing happened, so I was not able to click “Hammer-Aitoff (world)”. But then when I saved the project, exited and then opened it again I was able to double-click. 

5.2 was about US map projections. Changing the coordinate system was fun and very easy to follow!

5.3 explained how to set projected coordinate systems 

Chap 6

For 6.2, I don’t have the option for “UpperWestSideStreetsForGeocoding” when using the Pairwise Clip tool for this section. 

I found that sometimes it was very difficult to find and navigate through certain tools. 

Chapter 6 mainly helped me become more familiar with the Pairwise tools, streets, study areas, and data within different boundaries. 

Delaware Data Inventory 

 

Zipcode: This includes all ZIP codes in Delaware County, Ohio. This dataset is updated on an as-needed basis and is published monthly. 

Street Centerline: This depicts the center of the pavement of public and private roads within Delaware County. The address data was developed from data collected by field observations of current existing addresses. 

Recorded Document: This dataset contains points that represent the recorded documents in Delaware County Recorder’s Plat Books, Cabinets/Slides, and Instruments records. This dataset was made to facilitate the process of locating miscellaneous documents in Delaware County, OH. 

Survey: These are points that are a shapefile of point coverage which represents surveys of land in Delaware County, OH. The surveys are then scanned and saved as PDF files. 

GPS: This dataset has all known GPS monuments that were established in 1991 and 1997. This dataset is also updated on an as-needed basis. 

Subdivision: This dataset has all the subdivisions and condos recorded in the Delaware County Recorder’s Office. This dataset is updated on an as-needed basis.

Parcel: This dataset has polygons that represent all cadastral parcel lines in Delaware County, OH. On a monthly basis, this dataset is maintained and published. 

School District: This dataset has all School Districts in Delaware County, OH. It was first created from the Delaware County Auditor’s parcel records for the school districts. 

Tax District: This dataset has all of the tax districts within Delaware, OH. The data is derived from the Tax District code. 

Township: This dataset has all of the 19 different townships that make up Delaware County, OH. It is published on an as-needed basis. 

Aerial Imagery: “2024 3in Aerial Imagery. Flown Spring 2024”

Building Outline 2023: Building outlines 2023

2021 Imagery (SID File): Delaware County, Ohio. 

Condo: This dataset has all the condominium polygons in Delaware County, OH.

Address Point: The state of Ohio Location based Response System Address_Points data set is a spatially accurate representation of all certified addresses in Delaware County, OH.The layer makes it so you can reverse geocode a set of coordinates to determine the closest valid address and is intended to provide 911 agencies with the information needed to comply with Phase II 911 requirements. 

Address Points – DXF: The State of Ohio Location Based Response System (LBRS) Address Points data provides for a spatially accurate placement of addresses within a given parcel in Delaware County, OH. Through a partnership between the State and Ohio and Delaware county helped create the data. 

Annexation: The data set has Delaware County’s annexations as well as conforming boundaries from 1853 to now. This dataset is updated on an as-needed basis once the annexation has been recorded with the Delaware County Recorders office.

Building Outline 2021: The dataset has building outlines for all structures in Delaware Country, OH. The layer was updated in 2021. 

Building Outline 2023:Building Outlines 2023 

Dedicated row: This dataset consists of all the lines that are designed Right-of-Way within Delaware County, OH. 

Delaware County Contours: 2018 Two Foot Contours 

Farm lot: This dataset has all the farmlots in both the US Military and the Virginia Military Survey Districts of Delaware Country, OH. The dataset was created to facilitate in identifying all of the farmlots and their boundaries in both US Military and Virginia Military Survey districts of Delaware County, OH. 

Hydrology: The dataset has all the major waterways in Delaware County, OH. In 2018 the data was enhanced with LIDAR based data. 

Map Sheet: The dataset contains all of the map sheers within Delaware Country, OH.

Original Township: The dataset contains the original boundaries of the townships in Delaware County, OH before the tax district changes affected their shapes. 

Chapter 4:

The first tutorial for this chapter focused on importing data into your project. This taught me how to import data from external sources, such as the free websites mentioned in the book. When importing data, you have to know how it’s stored; if it is a Microsoft Excel workbook, it needs to be saved as a .csv file to properly import it. Using the GIS software, you can create your own databases. The second tutorial was about modifying the attribute table; this helps you display what you want to the user more easily. From this tutorial, I learned how to join tables together to display wanted information. When joining tables, if you don’t export the data table, then the change won’t be permanent. The third tutorial was about linking tabular data to the spatial feature classes. This allows the map to have symbols based on the values found in the data. There are different types of attribute queries. The different types are what and when; for example, what type of crime and the date it happened on. The second type is a refinement of when; this would be weekday vs. weekend or morning vs. afternoon. The third attribute query would be who or what; who did the crime or what was the crime are examples of this. The third tutorial really solidified how to do each type of attribute query. The fourth tutorial was counting the burglaries by neighborhood. The fifth tutorial was creating a central point for each area/polygon. The last tutorial in chapter 4 taught me how to make a table for many things; this helps a user understand what the numbers mean and how to interpret the data. 

Chapter 5:

Location is very important; when working with data, it is important to know the latitude and longitude coordinates corresponding to the precise location. These coordinates can tell us whether or not something is by a river, in a certain area, or even reachable. This chapter focused on spatial data. The first tutorial in this chapter taught me how to change the coordinate system for the map that you are creating. The second tutorial taught me to work with projected coordinate systems. The third tutorial followed the second with more projected coordinate systems. This one had me add a new layer to set a map’s coordinate system. I was able to change the map’s coordinate system for a specific layer in the data. The fourth tutorial reviews file formats that are commonly found with vector spatial data. The tutorial also covers how to import a shapefile, which is a different format that spatial data suppliers use because it’s simple. A shapefile consists of at least three files, which include .shp, .dbf, and .shx. The tutorial taught me how to import and change the coordinates because the imported data might not match the coordinate system of the map. The fifth tutorial was about working with US census map layers and data tables. A good website to get data would be the Census Bureau’s website. I downloaded the TIGER and tabular data, then refined it to be more useful. From this refined data, I followed the tutorial to create a choropleth map. The last tutorial for this chapter was about extracting raster features for a specific county. I did find this tutorial a little difficult to follow compared to the other ones. 

Chapter 6:

Geoprocessing is a framework and set of tools for processing geographic data; these tools are used to build areas of study and perform tasks. This chapter taught me how to extract a subset of spatial features from a map using spatial queries. The first tutorial was about dissolving features to create neighborhoods. The tool we used in this tutorial was the pairwise dissolve tool. This tool takes data, a dissolve field, fields, and statistic type to dissolve block groups to create neighborhoods. The second tutorial was about extracting and clipping features of an area to study them. The tool used in this tutorial was Clip; this tool clips streets to study the area. The third tutorial taught me how to merge two or more layers into a single layer. The tool used in this tutorial was the merge tool; it was used to create a single water feature from 5 different water features. The fourth tutorial taught me how to use the append tool, which added features to an existing feature class. The fifth tutorial used the pairwise intersect tool, this tool creates a feature class combining all the features and attributes of two inputs feature classes. The sixth tutorial taught me how to use the union tool. The union tool overlays the geometry and attributes of two inputs to create a new layer. The tutorial also taught me how to calculate geometry attributes. This allowed me to look at the acreage of the polygons. The last tutorial used the tools intersect and union. The tutorial was about the tools to create a feature class with combined features and data. When using these tools, the data is not split into parts for the new features. One of the tools I used was the Tabulate Intersection tool, which estimated the number of features within the boundary. 

Chapter 4

This chapter marked the introduction of working with spatial data. The tutorials first walked me through how to interact with a project’s geodatabase. This included the process of inputting outside information into the project, such as a table, how to use the database utilities in the catalog pane, and how to join attribute tables. Although this tutorial was simple, the method was extremely useful to learn since many future projects will likely require the addition of outside data. In addition to joining attribute tables, I was introduced to other functions related to attribute tables such as how to export a feature class, add a field, calculate the sum or percentage of fields, and how to extract substring fields. Although I did initially run into a few problems when working with the Python language, I was able to fix them by quickly retracing the steps. By the end of the chapter, I felt that I had a good understanding of how to use Python. The chapter then shifted to how to carry out attribute queries. Attribute queries are useful for finding geographic features based on their descriptive properties rather than their geographic properties. I found this function to be extremely useful because when you have a large dataset to work through, narrowing down the data based on attributes makes the process easier. Lastly, I learned how to aggregate data and create a point layer. Both of these processes allow for comparison between features and data to be displayed on the map.

Key Concepts/Definitions

Database: A container of the data for an organization, project, or other events for record keeping, decision-making, analysis, and research. A geodatabase has a file folder that contains .gdb at the end.

Chapter 5

Chapter 5 contained information all about the geographic coordinate and map projection systems in GIS. The first tutorials walked me through how to change the map projection, or the flatness or curvature of the map, for a variety of different setups. For example, I practiced changing the map projection on a world map and on a map of the United States. Although I appreciated the lesson on this, these tutorials did not seem as useful to me as some of the previous ones. Additionally, since we worked with larger scale maps, I wondered if these various map projections would also be beneficial when working with maps for specific locations. Other skills I learned throughout the tutorials include how to navigate and use the coordinate systems tab in properties, import a shapefile into the geodatabase to be added to the map, add data to the map from ArcGIS Living Atlas, and the different spatial reference systems. The use of different tools was also emphasized a lot in the chapter. After the tutorials, I feel much more comfortable with searching for and using the different functions of the tools. This chapter, however, was probably the first one that I struggled with a few of the tutorials. In one section, the chapter assumes that you have had some previous work with Microsoft Excel. Although I am somewhat familiar with the program, I struggled with this part because I do not know all of the proper functions of Microsoft Excel. I also had an issue with downloading the elevation contours in the sixth tutorial. The book was not up to date with the new layout of the website, so I had a hard time downloading the file in the proper format. Even with the difficulties however, I was able to complete all of the tutorials in the chapter correctly.

Chapter 6

This chapter was focused on geoprocessing and the tools it uses to process data in GIS. Throughout the tutorials, I learned how to use geoprocessing tools such as pairwise dissolve, clip, merge, append, pairwise intersect, union, and tabulate intersection. The pairwise dissolve tool can aggregate block group attributes to other feature levels using statistics such as sum, mean, and count. The clip tool allows you to select and remove features outside a specific boundary such as streets. Clip is often paired with the extraction of features to make a study area. A study area allows you to focus on one particular section of a larger map. This function will be useful when analyzing the statistics and structure of a subarea while comparing it to the entire map. By clipping the excess features, the tool gives the study area map a cleaner finish. The merge tool allows you to merge multiple separate feature classes together into one. Merge is useful when you want to display similar features, but they are not the main focus of your project. The append tool adds features to an existing feature class, considering that both have the same attributes. The pairwise intersect tool creates a feature class combining all of the features and attributes of two input or overlaying feature classes. The union tool overlies the attributes of two input polygon layers to generate a new output polygon layer. This results in the comparison of two attributes in the same area such as neighborhoods and land usage. Finally, the tabulate intersection makes apportionments proportional to the areas of split parts of polygons and assumes that the populations of interest are uniformly distributed by area within polygons. Overall, I appreciated the setup of this chapter since each tutorial served as an introduction to a new tool in geoprocessing. 

Key Concepts/Definitions

Geoprocessing: A framework and set of tools for processing geographic data. Builds study areas in a GIS and performs tasks.

Delaware Data Inventory

Address Point: This data set is a spatially accurate representation of all certified addresses within Delaware County, Ohio. The address point indicates the location of the building’s center. This data’s intended use is for appraisal mapping, 911 emergencies, accident reporting, and disaster management. The data is updated daily, but published to the site monthly.

Annexation: Contains Delaware County’s annexations and conforming boundaries from 1953 to present. The data is updated on an as-needed basis and is published monthly.

Building Outline 2024: Consists of building outlines for all structures in Delaware County, Ohio. The data was updated in 2024 and on an as-needed basis.

Building Outline 2023: Consists of building outlines for all structures in Delaware County, Ohio. The data was updated in 2023 and on an as-needed basis.

Condo: Consists of all condo polygons within Delaware County, Ohio that have been recorded. 

Delaware County E911 Data: This data set is a spatially accurate representation of all certified addresses within Delaware County, Ohio. It is used for the State of Ohio Location Based Response System (LBRS). The data is updated on a daily basis, but published monthly.

GPS: Identifies all GPS monuments that were established in 1991 and 1997. The coordinates are in Universal Transverse Mercator Northing and Easting. The data is updated on an as-needed basis and published monthly.

MSAG: The Master Street Address Guide (MSAG) is a polygon feature set of the 28 different political jurisdictions such as townships, cities, and villages that make up Delaware County. The data set was created to facilitate the process of locating the boundaries of the different political jurisdictions. The data is updated on an as-needed basis and published monthly.

Municipality: This data set consists of all of the municipalities in Delaware County, Ohio. 

Parcel: The data set consists of polygons that represent all cadastral parcel lines within Delaware County, Ohio. All changes to this data set are represented by recorded documents stored at the Delaware County Recorder’s Office. The data is updated on a daily basis and published monthly.

Precincts: This data set consists of voting precincts in Delaware County, Ohio. Precincts are polygons that determine each voting precinct boundary. This data is The data is updated on an as-needed basis and published as-needed by the Delaware County Board of Elections.

Recorded Document: This dataset consists of points that represent recorded documents such as vacations, subdivisions, centerline surveys, surveys, annexations, and miscellaneous documents within Delaware County, Ohio. Created to facilitate the process of locating miscellaneous documents. The data is updated on a weekly basis and published monthly.

School District: This data set consists of all school district counties within Delaware County, Ohio. The data is updated on an as-needed basis and published monthly.

Street Centerline: The State of Ohio Location Based Response System depicts the center of pavement of public and private roads within Delaware County, Ohio. Address range data was collected by field observation of existing address locations and by adding addresses using building permit locations. This data set is a spatially accurate representation of the road system. The data is intended to support appraisal mapping. 911 emergencies, accident reporting, disaster management, and roadway inventory. This layer is updated on a daily basis while the 3D layer is updated on an annual basis. The data is published monthly.

Subdivision: This data set consists of all subdivisions and condos within Delaware County, Ohio. The data is updated on a daily basis and published monthly.

Survey: This data set is a shapefile of point coverage that represents surveys of land within Delaware County, Ohio. Points represent the location of the survey platform. The data is updated on a daily basis and published monthly.

Tax District: This data set consists of all tax districts within Delaware County, Ohio. Data is dissolved on the Tax District code. The data is updated on an as-needed basis and published monthly.

Zip Code: This data set contains all zip codes within Delaware County, Ohio. The zip code layer was created in 2025 by dissolving all Delaware county parcels by their property addresses. This data set was also used to populate the zip right and zip left attributes for Delaware County’s road centerline. The data is updated on an as-needed basis and published monthly through the United States Postal Office.

Map Sheet: This data set consists of all map sheets whiting Delaware County, Ohio.

PLSS: This data set consists of all Public Land Survey System (PLSS) polygons in both the US Military and the Virginia Military Survey Districts of Delaware County, Ohio. Was created to facilitate in identifying all of the PLSS and their boundaries. The data is updated on an as-needed basis and published monthly.

Farm Lot: This data set consists of all of the farm lots in Delaware County, Ohio. Was created to facilitate in identifying all of the farm lots and their boundaries. The data is updated on an as-needed basis where surveys are recorded.

Township: This data set consists of 19 different townships that make up Delaware County, Ohio. The data is updated on an as-needed basis and published monthly.

Dedicated ROW: This data set consists of all of the lines that are designated Right-Of-Way within Delaware County, Ohio. The line data was created through Delaware County’s Parcel data. The data is updated on an as-needed basis and published monthly.

Railroads: This data set allows a user to view the locations of railroads in Delaware County, Ohio.

Building Outline 2021: Consists of building outlines for all structures in Delaware County, Ohio. The data was updated in 2021 and on an as-needed basis.

Hydrology: This data set consists of all the major waterways within Delaware County, Ohio. The data is updated on an as-needed basis and published monthly.

Delaware County Contours: 2018 Two Foot Contours for Delaware County, Ohio in a file geodatabase format.

Original Township: This data set consists of the original boundaries of townships in Delaware County, Ohio. Data was before tax district changes affected their shape.

Chapter 1 we are introduced to the foundations of arc and its basic functions. Here we learn how to save files and work with a layer of a map. 

 

I

n chapter 2 we learn how to design and symbolize thematic maps. We work with a thematic map. A thematic map consists of a subject layer placed in spatial context with other layers, like streets and political boundaries.

 

Chapter 3 things ramp up and we are now moving data and information to arc online. Here i got very confused and lost while transferring information. I also had trouble navigating the layout of arconline due to irt being different from the previous app we have been learning on. Steps seem to be a little less clear to me. 

Part Two: Preparing Spatial Data for Use

Chapter 4: File Geodatabases 

In this chapter, the focus changes from the visual aspect of maps to the data behind it. Chapter 4 shifts from map layouts to managing spatial data in the file Geodatabase, which is ESRI’s storage system. File geodatabases serve as organized storage for feature classes, raster datasets, and attribute tables. This is useful in research, spatial analysis, and keeping records. The purpose of this chapter is to learn how to organize project data, import external shapefiles and tabular data into a project geodatabase, modify field attributes, and execute relational joins and spatial queries in ArcGIS Pro.

Terminology:

• File geodatabase (.gdb): ESRI’s database used to store feature classes, raster datasets, and standalone attribute tables optimized for ArcGIS Pro.
• Feature class: A collection of geographic features with the same geometry type (point, line, or polygon) and identical attribute fields stored in a geodatabase.
• Shapefile: A legacy vector data storage format for storing the location, shape, and attributes of geographic features, commonly converted into feature classes for geodatabase use.
• Table join: An operation that appends the fields of one table to another based on a common matching attribute field across both datasets.

Tutorial: The tutorial was very helpful in learning how to navigate spatial data management. It showed how to:

• Import data into a new ArcGIS Pro project
  • Create an ArcGIS Pro project
  • Set up a folder connection
  • Convert a shapefile to a feature class
  • Import a data table into a file geodatabase
  • Use database utilities in the Catalog pane
• Modify attribute tables
  • Delete unneeded columns
  • Add a field and populate it using the Calculate Field tool
  • Join a data table to a feature class attribute table
  • Export a feature class to make a join permanent
  • Calculate the sum of fields
  • Calculate the percentage of total population under 20 years old
  • Extract substring fields and concatenate string fields
• Carrying out attribute queries
  • View crime incidents
  • Create a date-range selection query
  • Reuse a saved query to create a definition query
  • Query a subset of crime types using OR connectors and parentheses
  • Query the day-of-week range
  • Query person attributes
• Aggregate data with spatial joins
  • Build a spatial join
• Use central point features for polygons
  • Create a central point feature class for polygons
  • Create a point layer
• Create a new table for a one-to-many join
  • Create a table
  • Make a one-to-many join

Overall, I learned a lot in this chapter about backend data management. Cleaning up attribute fields and setting up relational joins makes spatial analysis more organized and increases efficiency

Chapter 5: Spatial data

This chapter focuses on spatial data formats and coordinate systems used for mapping locations accurately on Earth’s surface. It explains geographic coordinate systems, world map projections, US map projections, and setting projected coordinate systems for local, state, and national maps. The chapter also demonstrates how to work with vector data formats, download spatial and tabular data from the US Census Bureau, and source ready-to-use geospatial datasets from external repositories.

Terminology:

• Geographic Coordinate System (GCS): Three dimensional reference system that uses latitude and longitude angular units (degrees) to measure locations on the Earth’s surface.
• Projected Coordinate System (PCS):  Two dimensional planar surface that uses a mathematical transformation to project the Earth’s spherical surface onto a flat map.
• State Plane Coordinate System: A set of 126 geographic zones dividing the United States to provide localized, high-accuracy projected coordinate systems with minimal distortion for local government and surveying work.
• Geospatial data: Digital spatial data that represents geographic features and can be rendered into vector layers, raster datasets, or web services in GIS software.

Tutorial: For this chapter, the tutorial shows how to:

• Examine world map projections
  • Examine distortions in latitude/longitude maps
  • Apply world projections like Hammer-Aitoff and Robinson on the fly
• Change map projections for national maps
  • Change map projections to Albers Equal Area Conic
• Set local coordinate systems
  • Look up State Plane zones in ArcGIS Living Atlas
  • Apply localized coordinate systems and project geographic layers on the fly
  • Configure map display units to feet or meters
• Work with vector data formats
  • Convert shapefiles into file geodatabase feature classes
  • Plot point layers from XY coordinates
  • Convert KML files to feature classes using KML To Layer
• Download Census Bureau spatial and tabular data
  • Download TIGER shapefiles and demographic data from the US Census Bureau
  • Join demographic tables to spatial boundary layers using GEOIDs

I learned a lot in this chapter about coordinate systems and projections. It was very helpful to see how changing projections prevents spatial distortion when analyzing geographic areas.

Chapter 6:Geoprocessing

This chapter covers geoprocessing tools and workflows used to extract, aggregate, overlay, and analyze spatial features. Geoprocessing is used as a tool in ArcGIS Pro to isolate specific geographic study areas, combine overlapping dataset boundaries, and apportion demographic attributes in unaligned spatial units. In this chapter, the text shows how to process and prepare layers for emergency management officials in Manhattan and the Upper West Side neighborhood.

Terminology:

• Geoprocessing: A fundamental GIS framework and set of analytical tools used to manipulate, process, extract, transform, and evaluate spatial data to perform spatial analysis.
• Pairwise Dissolve: Aggregation tool used to remove interior polygon boundaries sharing a common attribute while calculating summary statistics across combined shapes.
• Pairwise Clip: An extraction tool that acts as a boundary cutter, keeping only the intersecting portions of input features within a specified boundary polygon.
• Apportioning data: A method of estimating tabular attributes for custom target zones by calculating the proportion of spatial overlap between non-aligned polygon boundaries.

Tutorial: This chapter shows how to:

• Dissolve features to create higher-level boundaries
  • Open attribute tables for block groups and examine housing attributes
  • Run Pairwise Dissolve on block groups to remove interior boundary lines and sum housing units by neighborhood
  • Dissolve fire companies to create fire battalion and fire division feature classes with population totals
• Extract features for a study area
  • Use Select By Attributes to isolate a neighborhood boundary and export it as a study area
  • Use Select By Location to extract block groups and street segments intersecting the study area
  • Execute Pairwise Clip to trim street networks cleanly along perimeter boundaries
• Overlaying datasets
  • Merge separate borough water feature layers into a single consolidated water dataset
  • Append firehouses and police stations to an existing EMS facilities point layer
  • Intersect streets and fire companies using Pairwise Intersect to assign response responsibilities
  • Run Union on neighborhood and land-use layers to calculate land-use areas
  • Use Tabulate Intersection to apportion demographic population data between census tracts and fire zones

This chapter was helpful in teaching about how spatial overlay tools work together to aid in urban planning and emergency management tasks.