Month: February 2025

Week 4

Chapter 1 

To be honest, I think my first hurdle here was just figuring out how to open the tutorial and in the ArcGIS Program. Once I got there and things were starting to look like what was actually in the book, I started to feel a lot better. The steps in the book have been very useful because there are so many tools I barely know where to look. The first tutorial was very helpful in getting to understand where everything was located within the system. Tutorial 1-2 was also helpful with navigating different tutorials. Overall, lots of hovering over random icons to find what I was looking for from the book. For some reason, the Tutorial 1-3 section on attribution tables tripped me up a little bit. I was a little confused on what I was doing but I think I got it figured out. I really enjoyed playing with the attribution tables and can really see how they would be useful when comparing data and such. Locating the statistics tool was a little bit different than what was in the book but I was able to get the Summary Statistics to come up! Section 1-4 felt a little silly at times but I also know it will be very useful when making modifications to future projects to really customize them. In this section, the book had different directions for removing the halo tab. It said to go to the ‘symbol tab’ but I had to go to expressions first and the was able to find the section for halos. Could not get the park symbology to open the gallery to find the park symbol but I could find other symbols. When trying to open the 3-D view of the Health Care Clinics it kepts saying “failed to open map view”

 

Chapter 2

In 2-1 I had a very hard time getting the symbols to change for Manhattan but I eventually got something similar to what the book was saying to work. I was able to get the colors pretty close on this activity but I did not spend a lot of time getting the exact ones specified. In Section 2-2 the pop-ups were really cool and will definitely be useful. I think they were a little hard and confusing to use at first but then I caught on. In section 2-3, I can see how useful creating quarries would be when trying to pick locations for things, especially for those in need! In 2-4, it kept telling me that I couldn’t complete the steps for the section “Over age 60 receiving food stamps” because it did not have a valid data source, I tried to troubleshoot this to get it to work and some how found how to do it? I think I just stumbled up getting it to use the right data set but I honestly probably couldn’t do it again (used this for one of my pics for this chapter because I was really proud that I got it to work). 2-5 went very quickly, not sure if it was just that simple or if I am really starting to get a hang of the program. In 2-6, I had a hard time making adjustments for the histograms and feel like I could not fully figure out changing the values because it seemed to keep completing one of the values, so I am not entirely sure about that. I did figure out the whole swipe tool thing and that was pretty nifty. 2-7 was also super simple and not a lot of steps or confusion. I will be honest, I like to choose different colors than it says in the book to make the map prettier. 2-8 was slightly confusing because I don’t think the book fully matched the program, but I was able to get the burrows to disappear when I zoomed in so I think it worked!

Chapter 3 

For some reason, in 3-1, the regular Art Employment map always showed up blank but the Art Employment per 1,000 was fine. I ended up completely just closing and redoing this tutorial and it worked this time around. I can see why this section would be very useful when preparing maps for presentations, especially with the chart features. In 3-2, I am not exactly sure what I was doing wrong because once I got on the web version as per instructions, the metropolitan layer was always grayed out and I could not do anything with it. I was still able to enable pop-ups though! Section 3-3 was also very helpful and I think that this app feature could be used for a lot of cool projects within my public health major. Section 3-4, I had a very hard time with this section because it seemed like nothing was where it was supposed to be according to the book so I had to dig through places to find things. Also did not have the pie chart option for me when configuring the map. I was able to get a pretty good dashboard put together though!

Storyboard Link

3-2 Link

Dashboard link

 

This was the first time I have used ArcGIS and it was very easy for me to navigate. I found the tutorials extremely useful and easy to follow. There were some points where I did skip as I could not locate what they were asking of me, so that will most likely have to be something I look back on incase it is useful for future assignments. Overall I had fun exploring ArcGIS and seeing the cool features it has. My favorite is the 3d models they look really cool and I like also how you are able to explore with your mouse. Below I have attached some photos I took from each chapter (1,2 and 3).

Chapter 4, “Mapping Density,” got me thinking about how we present information, especially when you’re dealing with varying areas. It’s not enough to just count things up, sometimes we need a bit more context, like how spread out something is. I think that’s why the book dives into density mapping, the distribution of features or values per unit area. There’s a difference between saying “there are 100 houses in this neighborhood” and “there are 100 houses per square mile in this neighborhood.” What I found particularly interesting was how you can map density in a few different ways. The book highlights two methods: mapping density for defined areas, like census tracts, which is where you calculate density within existing boundaries. Then, there’s creating a density surface, which involves creating a continuous surface that shows how density changes across an entire area, even without defined boundaries. It’s like taking something that’s usually summarized by area, and making it a landscape you can view. It seems to me that these two methods would be used for different purposes and I’m wondering which gets used for what application and why? Mapping density is about more than just visualizing data. It’s about taking into account the context of the data and how it’s distributed. It’s a really valuable tool to see the patterns that might be hidden at first glance. I wonder if density mapping could be applied to research in neuroscience, since it deals with location based data sometimes.

Chapter 5 seems to build off the idea that location matters, but in a different way. Now instead of looking at where things are, we’re looking at what things are contained by. The book’s main question here is, Why map what’s inside? and, in my understanding, it’s because it allows us to explore relationships between features. The book outlines three approaches for this: drawing areas and features (where we manually create areas to select features), selecting features inside an area (where we use existing boundaries), and overlaying areas and features, which sounds like the most complex one. The overlay method, as I understand it, combines two layers of features to see how they interact spatially. I’m starting to think this is where GIS really shines because it creates new relationships between features that wouldn’t exist in the real world otherwise. I’m curious how you all go about choosing which of these three methods to use? I imagine that using drawn areas is more appropriate when you need to be more precise with your selection, and that using existing boundaries is better for broader analysis. How do you decide which features to overlay, if that makes sense?

Chapter 6 seems to add a layer of complexity to our spatial analysis by focusing on closeness. I think the main idea is that we can learn a lot from the distance and relationships between features. The book asks, Why map what’s nearby?, and the answer I think is that it allows us to explore how features interact, or how they might influence one another. Three ways of exploring this include using straight line distance, measuring distance or cost over a network, and calculating cost over a geographic surface. It seems like the first one is the most basic, just measuring distance as the crow flies, so to speak, while the second one takes into account that movement is often confined to networks, like roads. The last one, where you calculate cost over a geographic surface, is a bit more abstract, where you take into account the “cost” of travel, which is interesting. I’m realizing that “cost” doesn’t always mean money, and that different types of cost can be included in research. It seems to me that GIS is very useful for understanding and calculating all these different types of distance. I’m also thinking about the different applications for these analyses. It seems that you could use straight line distance to do quick analyses, or when the network isn’t important. You could use network analyses to find optimal routes, and surface analysis to calculate the cost of travelling across different topographies. I am wondering if there are times when you would use a network analysis to find straight line distance, or is that redundant?

Chapter 4

Often, GIS can be used to map density as a manner in which to clearly represent areas of highest concentration- such as with feral cats or oversized rats- for the purpose of seeking out various patterns. For example, oversized rat phenomena could be located in large urban centers due to the access of food, adaptations, etc, but maybe their oversized rats can be found in a rural area that is coincidentally near a nuclear waste site. Things such as the United States Census records data that can be used for density mapping for population, income, family size, etc. There are also two different options of representing density as density of features or feature values. As an example, density of features would show the areas with oversized rats while feature values would show the number of oversized rates in the areas. In addition, there are two manners of mapping density either by area or by surface, each with their own uses and drawbacks depending on the type of data you have and what you want to do with it. Some data can be best represented using individual points while other data is best represented using shades of color. For example, for relatively isolated incidents of the oversized rat phenomena, a dotted map could be used to examine relationships between rat outbreaks. However, if incidents of oversized rats become a prevalently explored and recorded phenomena, then a shaded map would become better suited to facilitate pattern recognition. GIS has the computing power to be able to make calculations with density data to generalize and present density data in a manner conducive for seeking out patterns within the data.

Chapter 5

When mapping a subject within the boundaries of an area, patterns within the area may be assessed or internal patterns can be compared to the patterns within other areas. Using feral cats as an example, feral cat information could be tracked with Delaware City’s Township and compared with other townships- within or outside of the county- to track patterns in where feral cats are the most prevalent. With that information, trap, neuter, vaccinate, and release programs can be sent out to places with the most need in order to manage cat populations. This information could also be used to find cats or kittens that could possibly be integrated into human households. The power of GIS allows users to use pre-established boundaries- i.e. counties, townships, zip codes, etc., or created boundaries to assess data depending on the intended span and use for the data. Determining whether the features are continuous or discrete is important when mapping data within. Discrete data would be the number of feral cat colonies whereas continuous data encompasses things such as elevation, vegetation types, temperatures, precipitation, etc. that are continuously present. GIS can be used for lists of features, the numbers of features, and for summaries, and GIS can be used to cut off certain data that is outside of a drawn boundary. From that point, there are three methods of finding what’s inside: drawing boundaries to show features outside and within, specifying an area, or to create a new layer that overlays the original. Each method, like the others, has its own uses depending on your goals for your data and analysis. Summaries for numerical data can also be implemented such as sum, mean, median, and standard deviation depending on the relevance of each for the best representation of data. To best express the need for managing feral cat populations within the city of Delaware,  I may want to use the sum of cats or average number of feral cats per square mile to stress the gravity of the problem. 

Chapter 6

As a counterpart to Chapter 5’s finding what’s inside is Chapter 6’s finding what’s nearby within a certain distance or range. An example of using range would be notifying people within a ten-mile radius of a colony of rabid bats. This can be used for distance but also cost through time, money, or effort. Maybe I wanted to let everyone know about the rabid bats that are a breezy five-minute walk away from their community park. With this knowledge of the proximity of rabid bats, I could better raise community awareness and issue warnings for pets, children, and nighttime park-dwellers to be wary of the rabid bats and remind them of the mortality rate of rabies. An alternative would be to determine the travelling pattern of my rabid bats to best alarm those in the path of rabidity. GIS has the power to draw my ten-mile bat radius and measure my five-minute walking time. Depending on my goals for rabid bat analysis, I could calculate distance if the Earth was flat, using the planar method, or incorporate the Earth’s curvature, using the geodesic method. The planar method would be ideal for a smaller area of interest- a city, county, or state, but the geodesic method would be necessary for any larger analyses- such as if my rabid bats spread from my little town to all contiguous US states. Just as with mapping what’s inside, data can be represented using lists, counts, or summaries depending on need. I would like a list of the addresses within a ten-mile radius of my rabid bat colony to ensure their awareness of the bats and to ensure that they have not yet gone rabid. In the aftermath, I would perhaps seek a count of the rabies cases in a post-rabid bat town, and perhaps I would seek statistics or graphs to easily review the impact of my rabid bats on pets, children, and those nighttime park-dwellers. The three manners of finding what’s nearby include straight-line distance- such as a ten-mile radius, distance or cost over a network- such as sidewalks, and cost over a surface- such as for the travel cost to reach my rabid bats.

Chapter 4

Chapter 4 “Mapping Density” explains how mapping density allows you to see “patterns of where things are concentrated”. While some maps emphasize specific locations of features, Density maps focus on the patterns of certain features. I thought this was interesting because in the previous chapters we were looking at specific features, like individual crime scenes. With a density map it is more of a broad way of showing where for example the most crimes occur, and through this type of map you can see patterns of where the most crime is located or where there isn’t as much of a concentration of crimes occurring. Density maps can provide you with a density measurement per area. Raster layers are used to create density surfaces. Based on the reading the raster layers allow us to see concentrated features. I think if I’m understanding this correctly, you can have a cell of a map and if you take a radius around that cell you can figure out the  amount of features within that radius and that number is assigned to the cell and after you do that with all of the cells that is where you get your smoothed area of concentrations. Search radius can be large or small. Larger search radiuses show more generalized patterns, while smaller search radiuses show more local variation.

Chapter 5

I am having trouble understanding how all of these maps are different. I think they are different based on the features and also how the features are being analyzed. This chapter seems to be talking about what is going on in an area. You can monitor what’s happening this way, or you can use this information to compare different areas based on what is happening to them. They give the example of potentially mapping the affected area of a toxic plume. If not for this information appropriate action could be taken by the public or those with the ability to handle the situation. You can show boundaries of certain things such as buffers around streams, soil types in a parcel of land, and floodplains. I think that this is an interesting thing that I haven’t thought about before while looking at maps. On the topic of discrete or continuous features, discrete features are features that you can easily identify and they are unique. They are locations, addresses, crimes, etc. With continuous features you can summarize the features for each area.

Chapter6

In the chapter, Finding What’s Nearby, I learned that you can set distances and you can figure out what is going on within these distances of the certain feature you are looking at. You can label the nearness of a feature using distance or travel cost. I have gathered that it is important to know what information you will need because that will help you choose the best way to carry out your analysis. There are three ways of finding out what is nearby. Straight line distance, distance or cost over a network or, cost over a surface. Straight line distance is what you choose a specific source feature and the distance and then the area is found within the distance that you specified. When it comes to layers you need the source feature and then a layer with the distance to form what you are desiring. Straight line distance allows you to create boundaries around a source.  With Distance or cost over a network, I see this as when you put an address in your google maps and it shows you all of the routes and which one is the fastest. At least this is what I have gathered from the reading. Cost over a surface is when you have locations of source features and a travel cost. Adn from that “ “The GIS creates a new layer showing the travel cost from each source feature.” Cost over surface is more for overland travel, while cost or distance over a network is if you are traveling in fixed infrastructure. And for straight line distance this would be used for estimates of travel range.