Group Presentation Reading with Lonnie and Patrick
This exercise is to get us to create metadata for the different layers of Delaware GIS data. The point is to do a brief description of each layer in order to become familiar with them for future use.
Delaware_2008 and 2010 Ponds and Lakes: This layer contains data on, as the name suggest, where the different ponds and lakes are in the county of Delaware. This might be relevant to the swifts project, and their feeding patterns. Maybe? Perhaps? I don’t know that much about swifts.
Delaware_Address_Pts: This layer holds data on all of the addresses in the county of Delaware.
Delaware_Annexations: To be honest, I’m not exactly sure what kind of data this layer holds. But according to the Delaware GIS website, “this data set was created to facilitate the process of locating annexations and conforming boundaries within Delaware County, Ohio.”
Delaware_Archeological: Again, I’m not entirely sure, and the GIS website doesn’t give much help with this layer, but I am going to guess that the points on the map are place of archaeological interest, perhaps even dig sites.
Delaware_Bench_Marks: Shows all of the places in Delaware County where there are official benchmarks that can be used as geographical references.
Delaware_Building Outlines: This layer allows one to see every building that is in the county, and you can use the information feature in ArcGIS to determine what kind of building it is, e.g. residential, commercial, etc.
Delaware_ Census_Block: Well, nothing shows up when you add this layers into ArcMap, so I am not exactly sure what data this holds.
Delaware_ Census_BiockGroup: No clue
Delaware_ Census_ Tract: Allows statistical data to be viewed about different sections and parcels of the county
Delaware_Economic Development Layers: This shows all of the development projects that are currently in the works in the county.
Delaware_Farmlots: This layers consists of all of the farm lots. This could be useful for the drone project if taking a look at vegetation health with the infrared camera.
Delaware_Floodplain_1OOyr: Shows the extent of 100-year flood plains around different bodies of water in Delaware County.
Delaware_Floodplain_500yr: Shows the extent of 500-year flood plains around the county. It appears there aren’t that many, or they’re just not that big.
Delaware_Floodplain_2009: Displays places in the county that are at risk of being flooded
Delaware_Floodways: I believe this layers hows all of the bodies of water that are risk of flooding.
Delaware_Historical_Local: Allows points of local historical interest to be viewed on a map
Delaware_Historical_National: This dataset contains points of national historical interest, and where I am guessing there are physical markers, such as in front of Elliot Hall.
Delaware_Hydro: Shows the locations of major bodies of water in Delaware County.
Delaware_Hydro_Detail: Lists all of the bodies of water, from small ponds to the Scioto and Olentangy.
Delaware_Landmarks: This dataset shows multiple places of interest around Delaware, from schools to parks to cemeteries.
Delaware_Master Point Coverage: I’m not entirely sure what this layer is, but it seems very similar to the address points layer.
Delaware_Municipalities: Shows the different municipal areas of the county, such as the city of Delaware, Sunbury, Powell, etc.
Delaware_Natural_Heritage_ ODNR: I cannot postulate what exactly this layer is showing, but it is individual points on the map.
Delaware_ Orthophoto _Detailed_2010: This layer holds much newer satellite imagery of Delaware. It looks a lot nicer and sharper than the imagery basemap that comes with ArcMap. I can definitely see this coming in handy if something needs to be mapped with good satellite imagery.
Delaware_Parcels: This dataset contains data on every parcel in the county, and who owns that parcel in a lot of cases.
Delaware_Parks: Consists of all the park locations in Delaware County.
Delaware_Places of Interest: I’m not sure how this differs from the Landmarks dataset, because it shows similar locations. It shows, golf courses, medical centers, mobile home parks, police stations, post offices, public buildings, and schools.
Delaware_Public Land Survey System: This dataset puts a grid on the map of all the individual sections that have been surveyed. They are split up into squares for the most part, like the USGS quadrangles.
Delaware_Railroad: Shows the railroads in the county.
Delaware_Road_Center_Line: Displays all of the roads with a single line for each road.
Delaware_Road_RightOfWay: Somehow shows the right of way on the roads. I don’t know how to interpret it though…I guess I’m going to end up going the wrong way on some streets.
Delaware_School_Districts: This splits the county up into all of the different school districts. You could add on the address layer and see what addresses match up with what districts, and determine who goes where for school.
Delaware_Soils: This seems like a very complex dataset, consisting of a lot of information on the different soil types in the county. This could definitely be useful when taking a look at the garlic mustard for Project 3.
Delaware_Subdivision: I’m not sure exactly how to describe it or classify these sections, but they are parcels that are their own, well, subdivisions. I guess subdivisions is an appropriate classification.
Delaware_ TaxDist: Splits the county into the different tax districts.
Delaware_ Topography: Puts a raster layer of shaded relief topography. It gets blurry when you zoom in, as is expected of raster data.
Delaware_ Townships: Displays the different townships in the county
Delaware_ Townships_Historical: I don’t know what differs between this layer and the previous one, but this layer is less detailed it seems.
Delaware_ Watersheds_ ODNR: Shows watersheds…that are in Africa…I’m confused.
Delaware_ Wetlands: Again, in Africa…what the…
Delaware_ Woodland_ ODNR: Shows wooded areas of Delaware County.
Delaware_Zip_Codes: Shows where the geographical cutoffs are for the different zip codes in the county.
Delaware_Zoning: When I added the shapefiles for this, nothing popped up, so I’m not entirely sure what exactly it’s showing, but obviously it’s something to do with zoning.
Ohio Wesleyan Parcels: Shows the extent of the OWU campus.
Watershed-Scioto: This dataset shows the full extent of the Scioto River watershed, extending outside of Delaware County.
I’ll be completely honest: at first, I really did not see the point in me doing this because I have already had a class that has used GIS. Then I started to actually go through the tutorial, and I realized I was being very stubborn. There’s quite a lot of features that I have yet to learn, and this has turned into something that is kind of fun. I am mostly just excited to finally start working with ArcMap and start creating…something!
Chapter 3 was basically review for me. I did not know, though, that the measuring tool automatically found the shortest distance between two points. I thought it was just a straight line from one point to the other, but nay! It follows the curvature of the earth and such.
I decided that instead of going through the tutorial book chronologically, I’m going to jump around and focus on the areas that are pertinent to completing the midterm on time. So, here we go (I will be writing this as I do it all).
I’m starting with the second section of the midterm that has us actually making maps, titled GIS Applications. The first section asks us to map the six major categories of land uses (agricultural, mineral, commercial, residential, exempt).
First thing that I did was skip to the part of the tutorial book that talks about selecting certain parts of a layer by an attribute. In this case, I am using the Parcels layer from the Delaware GIS data. I imported the data into ArcMap, and over 70,000 parcels jovially popped onto screen. In order to separate all of these parcels into separate entities based on what type they were, I knew I had to separate them by an attribute as mentioned above. The attribute that I used in this case was class, which had a number from 100 to 600, with each category being in a separate group of hundreds, i.e. a number in the 100s means agricultural, 200s means mineral, etc. So I jumped to the appropriate section in the book (15b) and re-familiarized myself with the proper equation to separate what I wanted to separate. I went through each category and separated them by the hundreds, and turned them into separate layers with their very own pretty colors to represent them. I noticed something though when I did each category. Besides the fact that there were no mineral parcels, there was a sizable plot of land in the northern part of the county that wasn’t filled in. I looked at its class attribute, and instead of a number, it simply had an ‘S’. I looked at the ortho layer, which contains relatively up to date satellite imagery, and it looked like it was part of Delaware State Park. I have no idea why this wouldn’t have a proper class number. So after consulting with my illustrious leader, I decided to go in and edit the attribute table so that it would have the proper number. Voila. No more void.
Chapter 4: Bringing It All Together
GIS is distinguished from cartography by its ability to analyze data.
- Impossible to look at a static map and determine how much population change might have happened in a certain time period
GIS is able to transform map data and customize it if so desired.
GIS is helpful when one wants to know “whether a given spatial entity is contained within an area” by using polygons.
- Gas stations
- Zoning laws
“As GIS developed and has been disseminated, its ability to model interactions among spacial phenomena has increased.
“Today GIS is used to query spatial data, analyze spatial relationships, and characterize regions, as well as to model spatial change over time and space.”
Overlay analysis is quite possibly the most common GIS analysis function.
- Allowing for a visual representation of possible commonalities in areas
- Allows relationships to be determined
- E.g. overlay analysis can be used to identify populated areas that are “at risk” for fires in Southern California
- Individual layers, such as population, roads, and vegetation, can be taken and basically combined. Also add on fire hazard zones, buffer zones, etc.
Between raster and vector data, overlay is well suited to raster
- A mathematical construct on which overlay analysis is based
- Uses the areas or spatial entities that are the basis of GIS to formally express relationships between them
- Set of arithmetical operations that can be performed on raster data
Reclassification of attribute values
One of the most common applications of GIS is to aid in decision making for spatial location
- “Multi-criteria evaluation (MCE) is a raster based modeling tool that allows users to combine several criteria (attributes) in order to derive a suitability index for location of a spatial entity”
- E.g., figuring out out where to build waste treatment plants
Intuition – used by GIS researchers as a means of making sense or interpreting visual displays of geographical data
- TB in Vancouver
- Using spatial analysis techniques to identify clusters, and displaying the patterns
Data and analysis are closely linked
- Especially when it comes to epidemiological and population health studies
– Population health is concerned with the influences that social relations play in shaping the health of individuals and communities.
- The spacial framework you are working with must be flexible since areas of analysis change
Spatial analysis problems
- MAUP – modifiable area unit problem
– A problem that occurs when spatial units, such as postal codes, are categorized into larger units
- Ecological fallacy
– When categorization or scaling introduces a bias when attributing the characteristics of populations or groups to individuals
Problems with using a raster based system
- In converting to raster from vector, data appears almost as blurry or granulated, since there aren’t as much data for specific polygons. When going from vector to raster, the data has to be disaggregated to create a raster coverage.
Health profiles and indices
- Jarman 8 – index developed to to measure underprivilege, initially in the UK to idenitify underprivileged areas for the purposes of health care planning.
I still don’t know. My group and I are still trying to figure that small and massively significant part out. What it seems like right now is that we will be figuring out applications for the drone and the infrared GoPro with all of the other projects. For instance, the first project has to do with building chimney towers for swifts, and using remote technology to observe and monitor activity of the birds. Perhaps the camera can be used in such a way? I’m not sure exactly how the drone itself will come in handy for this one, mostly because I’m afraid it will scare away the birds rather than be inviting.
For the second project, we have a slightly better idea on how the drone and camera might come into use. The second project has to do with the Delaware Run, and basically restoring it and possibly rerouting part of it near campus. We were asked by the fine people in this group to do a flyover the part they are thinking of redoing and taking some up-to-date images that they can use to help plan the route. I’m actually quite excited for that.
The third project is kind of right up my alley in terms of what I might possibly want to do with the drone and camera at a later time (refer to the Drone and Remote Sensing page). This project is taking a look at urban heat islands, and the affect they have on the growth of crops, in this case garlic. The infrared camera can be used to hopefully take a look at the health of the crops in both the urban setting and more rural setting and compare the two.
This is all still very much in the works, and we will continue to discuss with each other and Dr. Krygier to truly nail down a plan. To be honest, the fact that this is all still very much up in the air is slightly disconcerting and kind of uncomfortable. But I’m going to be cheesy and use a quote that I hold true, and it is that “when you are uncomfortable, you are growing.”
I know, I know, super cheesy. So sue me.
A few months ago, my father sent me a link to an article from the San Francisco Chronicle. The article was written by Tom Stienstra, an outdoors columnist whom my father is very much a fan of. He takes Stienstra’s advice on certain hikes and trails to take all around the Bay Area and California, and has shared those experiences with me. This article in particular though talked about how GIS is helping create interactive maps that “show all parks, open space and the network of roads and some trails that provide public access.”
A website that has one of these interactive maps is California’s Protected Areas Database (CPAD). The map displays all “fee-protected, open space in California,” and is even able to be downloaded. Viewers can also edit and contribute to the map by submitting their own data to the website. In short, it is one big GIS conglomeration. Below is a screenshot of the collaboration page, which has a map that shows proposed edits to the map.
A second website, ParkInfo, has a similar map, displaying all open spaces and parks throughout California, no matter how large or small. A very cool feature of this map, like CPAD, is that you can download and print any part of the map to use on a trail or hike. Below is a screenshot of most of the Bay Area from this website.
Hi everyone, I’m Christian, and I’m a mapaholic from San Mateo, California. Ever since I was little, I have loved maps, globes, etc. When I was a toddler, my parents got me this puzzle map of the United States that had each state as an individual block. My dad or mom would turn the board upside down, dumping out all of the states, and I would put them back in their correct places. I always spent a little extra time with Oklahoma because I always thought it looked like a pistol and sort of played with it as such, which I am sure made my parents ever so comforted.
Anyway, that little anecdote is to show how it is no stroke of luck that I am a geography major. Different places interest me, plain and simple. I love to travel, hike, bike, ski, get a feel for every terrain that I can. So now as a college student, to be able to create my own maps and such is most definitely ideal. Some of my other interests include soccer, cycling, hiking, skiing, playing FIFA with my cousin, and improvising on the piano. Below is a photo of me (and my father) so you can put a face to what I will be putting on this blog, which, let’s face it, will be nothing short of gospel.