I did all five parts in record time!

I really enjoyed this final as each part had a different challenge. Some required more thought and more help than others but it was good to learn.

https://sites.owu.edu/geog-191/wp-content/uploads/sites/208/2022/10/SD-GEOG-191-Final.pdf

DATA SUMMARY

• Zip Code
  • Contains all zip codes in Delaware County
• Recorded Document
  • Points that represent recorded docs in Delaware County Recorder’s Plat Books (how purchased property is divided in an area), Cabinet slides, and Instruments
• School District
  • Consists of all school districts in Delaware County
• Map Sheet
  • Consists of all map sheets in Delaware County
• Farm Lot
  • All farm lots in US Military and VA Military Survey of Delaware County
• Township
  • Created to identify geographic boundaries of each township in Delaware County
• Street Centerline
  • LBRS depict center of pavement of public/ private roads in Delaware County
• Annexation
  • Contains annexation data and conforming boundaries
• Condo
  • Consists of all condominium polygons in Delaware County
• Subdivision
  • All subdivisions and condos in Delaware County Recorder’s office
• Survey
  • Locations of the survey plat
• Dedicated ROW
  • Shows all dedicated right of way polygons in Delaware County
• Tax District
  • All tax districts in Delaware County
• GPS
  • All GPS monuments est. 1991 and 1997
• Parcels-DXF
  • All parcels in Delaware County
• Subdivsions-DXF
  • All subdivisions and condos in Delaware County Recorder’s office
  • DXF- kind of vector file (I think)
• Original Township
  • Original boundaries of townships in Delaware County
• Imagery 2019
  • Raster layer
• Hydrology
  • All major waterways in Delaware County
• Precinct
  • Polygons that determine voting precinct boundaries in Delaware County
• Parcel
  • All parcels in Delaware County
• PLSS
  • Contains polygons of boundaries of 2 land survey districts in Delaware County
• Address Points
  • All addresses in Delaware County
• 2022 Leaf- on imagery (SID file)
  • Image collection
• Building outline
  • All building outlines for structures in Delaware County
• Delaware County Contours
  • File geodatabase
  • 2 foot contours for Delaware County
• Address Points- DXF
  • CAD Drawing
  • LBRS provides spatially accurate placement of addresses
• Street Centerlines- DXF
  • CAD drawing
  • Depict center of pavement of public and private roads in Delaware County
• Building Outlines- DXF
  • CAD drawing
• 2021 Imagery (SID file)
  • Image collection
• Delaware County E911 Data
  • Feature Service

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Some of these data were self-explanatory, and others required a little more reading.

Definitions:

• CAD drawing- Detailed illustration using vector/ raster based graphics to create traditional drafts
• Feature service- Serve feature data  and nonspatial tables over internet
• File geodatabase- Collection of files that can store spatial and nonspatial data

 

Chapter 6:

• Domains provide a way for you to constrain input information by limiting the choice of values for a particular field, helps maintain data integrity
• Organization – shared online workspace that is tied to your software license
• Tree inventory map allows urban forest managers ti identify areas in which tree conditions can be poor and prioritize maintenance

Chapter 7:

• Geocoding- create features from information that describes or names a location, typically an address
  • Address table
  • Reference data
  • Address locator – file contains the reference data and various geocoding rules and settings
• Buffers are polygons that are created around a feature at specified distances

Chapter 8:

• Temporal data – data that has a time attribute
• Hot spot analysis – determines which areas are significant, including areas that are hot and cold and areas that are not significant are white
• Space-time cube – helps visualize the data, the bins can be viewed in 2 or 3 dimensions and can show patterns of incidents over time

Chapter 9:

• Cells – raster is composed of a grid of cells, instead of discrete x,y coordinates that define geographic entities
• Discrete data – shows distinct and discernible regions on a map, such as soil type
• Continuous data – there are smooth transitions between variations in the data
• Map algebra – language that combines GIS layers, is fundamental to raster analysis
• NoData – no values were recorded in cell (not the same as 0)
• Mask – means of identifying areas to be included in a geoprocessing operation
• Hillshade – surface layer that depicts shadows to model the effect of an illumination source over terrain of the land
• Azimuth – the direction of the sun, expressed in positive degrees
• Altitude – angle of the sun above horizon

Chapter 10:

• Labels – based on one or more feature attributes and placed near or on a feature
• Label class – used to specify detailed aspects of how labels are positioned and symbolized
• Map frames – containers for maps in your page layout
• Scale bar – dynamic element that provides an indication of the size of a feature and distance on the map

Here is Chapters 6-10 and the Delaware Data Inventory. The second PDF is a series of maps from the chapters (I do not remember which ones they belong to), and the last maps are from the Delaware Data inventory. Tried to put them into a pdf but the files were all more than 20 mg–click on them or they will be blurry…

Chapter 6-10 and Delaware Data Chapter 6-10 and Delaware Data

Chapter 6:

 6a

• I was able to work on the tree inventory data up until I had to publish it because I kept getting a pop up that my account does not have publishing privileges?? Idk if anyone else ran into this problem, but I was not able to find a solution so I couldn’t complete the rest of chapter 6 🙁
• Here’s my sad little screenshot

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Chapter 7:

7a

• Geocoding- able to create data from collected points of interest
• To geocode addresses , you need-
  • Address table (list of addresses stored as database or text file)
  • Reference data (usually streets layer, where addresses are located)
  • Address locator (contains reference data+ geocoding rules/settings)

7b

• Geocode location data

7c

• Buffers- polygons created around features at specified distances
• Using GIS in this way can help businesses evaluate the best locations

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Chapter 8

8a

• Temporal data
  • Data w time attribute
• Kernel density
  • Calculates the density of features in an area around those features

8b

• I think the 3d stuff is pretty cool 😀

8c

• Not much to comment on this exercise, but the scene is much easier to look at now

8d

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Chapter 9

9a

9b

• At this time, no parts are in complete shadow
• 3 to 4 planting sites have low-slope topology
• 4 planting sites include land that faces S, SE, or SW
• No planting sites in shadow @ 2pm
• Some sites meet ⅔ criteria, but not all 3

9c

• Greenfield fine sandy loam, Xerorthents, loamy, Rincon clay loam
• Yes, some of the potential sites have been planted

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Chapter 10

10a

• 43 areas are fixed wireless technology
• For whatever reason I wasn’t allowed to change the color scheme

10b

• Labels- based on feature attributes and placed by/ on a feature

10c

• Map frames- containers for maps in a page layout
• Changing the map extent does not affect actual map
• Legend helps readers understand the features on the map
• Scale bar indicates the size of distances/ features on a map
• North arrow indicates north

10 d

Hopefully, this will work finally and save room in the process….Week 5 Blog post (2)

 

 

Taking this idea from Savannah and linking a pdf with all of my work from those first 5 chapters.

Lashway Week 4 Write-Up

Week 5 Work – Savannah Domenech

I hog up much less room this way. Click the text above to view my Week 5 work.

Chapter 1:

• GIS is composed of 5 parts: hardware, software, data, procedures, and people
• Can be used to map relationships, patterns, and trends in addition to simple cartography
• Interesting how GIS has been around since the 60’s. I wonder how difficult it was to use it back then
• Point, line, polygon data = vector data
• Features of same type = layers
• Raster= digital surface
• Attributes= in depth data

• Don’t be like me and do the exercises in the classic map view, and then get confused as to why everything looks different

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Chapter 2:

Exercise 2A

• PM concentrations are highest in Africa
• To restore contents, go the ‘View’ and select contents
• Geoprocessing toolbox is right next to contents
• Shanghai has the largest population

Exercise 2B

• Symbology= the way GIS features are displayed on a map
• The distance between San Antonio and Toronto is 1,440.32 mi

Exercise 2C

• The highest building is 339.8 ft
• Extrusion= stretching flat 2D features vertically to appear 3D
• This was cool- I felt like I was playing the Sims lol

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Chapter 3

Exercise 3A

• The field name that indicates the state within the which the county features are located is called STATE_NAME
• 10575 residents of Wayne county are between 22-29 yrs
• Definition query: limit visible counties to only in Illinois, but source data will not change 
• Clip: Select data based on layer of boundary (However, Illinois boundary not defined)
• Select and export: select counties in Illinois and export to new dataset

Exercise 3B

• Columns= fields
• There are six years of data represented
• Graduated colors= features are assigned a color that represents a quantity
• Classification methods:
  • Manual interval classification
    • Modify classification breaks manually with manual intervals
  • Equal interval classification 
    • Range of data is equally divided by the number of classes chosen
  • Defined Interval Classification
    • Similar to equal interval, but define interval size to determine class number
  • Quantile classification
    • All classes have same number of features
  • Natural breaks classification
    • Based on natural groupings inherent in the data
  • Geometric interval classification
    • Creates class breaks that are based on class intervals with a geometric series
  • Standard deviation classification
    • Creates classes according to a number of standard deviation classifications

• I did not see a clear correlation between income and 2010 obesity rates

Exercise 3C

• I was not able to retrieve the infographics even though I was signed in to ArcGIS online 🙁

Exercise 3 dimension

• There are 4 food deserts in Knox county
• Spatial join= define spatial relationship between 2 layers and combine attributes into an output layer

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Chapter 4

Exercise 4A

• Coordinate systems
  • Geographic coordinate system- uses latitude and longitude to define locations of points
  • Projected coordinate system- uses map projections to transform longitude and latitude coordinates into planar coordinates
• On the fly projection
  • Projected coordinates on first layer applied to subsequent layers
• Metadata
  • Textual info about dataset

Exercise 4B

• Snapping= magnet
• The selected line has 4 vertices

Exercise 4C

• The shape area value was halved

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Chapter 5

Exercise 5A

• Conflict types:
  • Riots/protests
  • Battle
  • Remote violence
  • Strategic development
  • Violence against civilians
• 727015 fatalities

Exercise 5b 

• 41 riots/protests
• 71 fatalities

Exercise 5c

• Layer by attribute and Summary statistics are combined
• 26323 fatalities 

 

Chapter 1 (Law / Collins)

 

• It is important to note that the feature data in GIS contain attributes that correspond to the features attribute data. 
• Feature attribute information is stored in a table in a GIS database. Each feature occupies a row in the table, and an attribute field occupies a column
• ArcGIS Pro is similar but also different from the basis of ArcMap in that it uses ArcGIS Online basemaps as the backdrop instead of the typical import of basemaps from another source. ArcMap can also use Arc Online for basemaps but this is something that is not extremely important

 

Exercise 1: 

• This figure represents up to the 8th step in exercise one. At this point all that has really been done is opening an ArcGIS online map and seeing some of the features located in the layers tab that are associated with the map

• This figure represents the public schools in the area but with a different symbol. At this point in the exercise we have successfully changed and updated a symbol in the map

 

• The next figure shows the ToBreak attribute and what this attribute shows is the minutes it takes to walk to school from those areas.

• This figure represents the different areas and how long it takes to walk to school from those areas. Using the steps in the book I was able to create this map to show the difference between 5, 10, and 15 minutes of walking to get to school. Red being 5, blue being 10, and green being a 15 minute walk to school. 

• This figure represents the Vision Zero Safety Incidents that were configured using the filter and clustering section of the editing tools for the map. As well as the map pop-up windows were configured to produce this figures pop up window you can see in the top left of the figure.

• This figure shows what the final version of the map looks like with all three layers showing. Cities can use a map like this to create stricter speeding fines and punishments for school zones where these incidents occur. 

 

Chapter 2:

 

• This chapter begins with getting to know the basics of ArcGIS Pro. ArcGIS Pro offers 2D AND 3D visualization and analysis with an interactive, navigable interface.
• So far this chapter is showing that although ArcPro is similar to ArcMap, it varies in that it is more similar in some regards to Microsoft applications

 

• A folder connection was one of the first steps in exercises 2a and this was a very similar process to that of ArcMap
• This figure shows the air pollution by country layer in the first exercise in chapter 2. This map was produced from data that was imported into ArcPro and then manipulated to look the way it does.

• The continents that PM concentrations are the highest are Africa and Asia. 

 

• To restore the contents pane in ArcPRO just go to the view tab and click on contents

 

• The largest city according to the Cities attribute table is shanghai china

• This figure shows what the final map looks like after the first exercise in chapter 2. Some basic tools were used to manipulate the map and its data so that it looked this way as a final product.

• This figure shows what the map looks like at the end of the 2nd exercise in chapter 2. In this exercise we changed feature symbols, configured feature labels, used the measure tool, added a cloud-hosted basemap, and packaged the project to share online.

 

• The height of the tallest building in the third exercise in this chapter is 339.76 feet

• This figure shows the final map image for the final exercise in chapter 2. In this exercise we learned how to convert a 2d map into a 3d map, and then extrude features based on the building height attribute to visualize buildings in a more realistic perspective. This exercise was fantastic if I say so myself and I was genuinely surprised going through this chapter. I think chapter 2 contained great information that is vital in getting to know ArcPro.

 

Chapter 3:

 

Questions :

• (add data to the project) The field name that indicates the state within which the county features are located is called STATE_NAME
• (add data to the project) There are 10575 residents in Wayne County Ohio between the ages of 22 and 29
• (Incorporate tabular data) Six years of data are represented in the table
• (incorporate tabular data) No I do not see a correlation between income and 2010 obesity rates. There are many counties with moderate-high income and high obesity rates. There are also counties with low income that have moderate to low obesity rates.
• (Calculate data statistics) 18.7% of households had an income of less than 15,000 per year.
• (connect spatial datasets) There are 4 food deserts in knox county

• This figure shows the map after the first exercise in chapter 3. This map was configured using select features by attribute and then exporting those features into a new dataset into the map.

• This figure shows the final map product at the end of the second exercise in chapter 3. This exercise was all about joining the cdc data to the Illinois county feature class. This was done using a variety of methods and data manipulation techniques including joining data tables, incorporating layer symbology, and using the swipe tool to compare different years of the data on the map.

• This figure shows the final map product after the end of the 3rd exercise in chapter 3. This exercise was all about calculating the data statistics. We added a new attribute field and then populated the field with values. We then calculated the summary statistics for the state.

• This figure shows the final map product of the last exercise and the final map at the end of chapter 3. This exercise was all about spatially joining data. We spatially joined the 2010 layer with the IL_food_deserts layer and this is shown by the number of food deserts displayed on the map for each county. 

 

Chapter 4:

 

Questions:

 

• (Configure snapping options) The selected line has 4 vertices
• (Modify Features) The Shape_area value decreased from the original water pressure zone

 

• This figure shows the final map product after the first exercise in chapter 4. This exercise was all about switching the city’s data collection to a geodatabase model. It was easier than expected to build a geodatabase for the city.

• This figure shows the final map product after the second exercise in chapter 4. This exercise was all about creating features and fixing the missing water lines in the map. This was done using a variety of techniques in the data tab of the ribbon. Snapping was the method that was used for the exercise.

• This figure shows the final map product of the last exercise and the end of chapter 4. This exercise was all about modifying features which included splitting a polygon, merging polygons, modifying lines and points, and adding map notes.

 

Chapter 5:

 

Questions

 

• (manage a repeatable workflow using task): The conflict events recorded in the dataset are  battle (no change of territory), battle (government regains territory), battle (non-state actor overtakes territory), headquarters or base established, non-violent transfer of territory, remote violence, riots/protests, violence against civilians, and strategic development 
• (Author a task) 14,211 fatalities resulted from violent conflicts against South Sudanese civilians between 2010 and 2018 
• (Run the model) There were 71 fatalities that resulted from conflicts classified as violence against civilians in Rwanda from 2010 – 2018
• (Convert a model to a geoprocessing tool) 41 riots/protests occurred in Rwanda between 2010 and 2018 and 12 fatalities resulted from this
• (Use a custom script tool) The geoprocessing tools combined in this script are Select Layer By Attribute and Summary Statistics
• (Use a custom script tool) 26,323 fatalities resulted from conflicts classified as violence against civilians in Nigeria from 2010 to 2018

• This figure shows the final map product of the first exercise of chapter 5. This exercise was all about using the Tasks pane in ArcPro to establish a workflow for ourselves and so others can use the task we created in a similar way to create a similar map with the appropriate data. 

• This figure represents the final map product at the end of exercise 2 in chapter 5. This exercise was all about using ModelBuilder which is a design environment for creating spatial analysis workflow diagrams and in this exercise we used ModelBuilder to build the workflow to create this map.
• Here is the final modelbuilder model in ArcGIS Pro
• 
• This figure shows the final map product of the last exercise in chapter 5. We can see that all three countries (South Sudan, Rwanda, and Nigeria) are highlighted as they were all worked on in different exercises throughout the chapter. This exercise was all about running geoprocessing tools in ArcGIS Pro using Python. This workflow was similar to the first two exercises but we used Python coding and a script tool to execute the processes. 

Notes:

• These first five chapters of the book were very informative on how to use the tools and functions in ArcGIS Pro, and how to manipulate maps and data as well. Once we get to chapter 3 I think that’s when things start to get dicey. Although I can follow the instructions the book gives for each exercise in each chapter, I would still struggle somewhat if I were to do this off the top of my head (guess I need to review more).