Lee L.- Week 3

Chapter 5

    • Mapping what is occurring inside an area is significant for monitoring, so when something occurs out of the ordinary, they know to take action
    • Taking how many areas you have to look inside into consideration, is it a singular area or is it multiple? 
      • Singular areas such as library districts allow you to monitor activity on a smaller scale. 
    • What’s a buffer? I thought I escaped these, but chemistry always finds a way to invade every discipline I’m in
      • Wrong buffer, these ones define a distance around a specific feature, like a stream buffer which is off limits to logging. (Does this mean they can only get so close to it on a map?)
    • Administrative or natural boundary-> parcel or land, watershed
    • Several areas would be contiguous, a prime example of this would be zip codes. 
    • Disjunct-An example of these would be state parks
    • Discrete features: unique and identifiable. Can list and count them as well as summarize them
      • Locations
      • Linear features: roads, pipelines
      •  Discrete features: Parcels 
    • Continuous features represent more seamless and geographic phenomena. 
      • A summarization of features in each area 
        • Spatially continuous categories or classes like vegetative type or range of elevation (SOIL TYPES, I LOVE SOIL).
    • Three ways to find what’s inside
  • Drawing areas or features
        • Visual approach is good for seeing whether one or more features are inside or outside of an area (In or out of bounds)
        • Need a dataset containing the boundary of an area or areas and a dataset containing the features necessary
        • Types: Locations, lines, areas, surfaces (The whole nine yards) 
        • Quick and easy, but visual based only so there is a slack of information from the inside
  • Selecting the features inside an area
        • Getting a list or summary of features inside a single area, or group of areas you’re treating as one 
        • Also good at finding what’s within a given distance of a feature
        • Types: Locations, lines, and areas (No surfaces. One time a guy said a seal was an impervious surface, is this true?)
        • Good for scaling down on information in a singular area, but doesn’t really let you know information in other areas (Only all areas as a whole.) 
  • Overlaying the areas and features
      • Combines area and features to create a new layer with the attributes of both or compares the two layers to calculate summary statistics for each area on the fly. 
      • Finding which features are inside which area, and summarizing how many or how much by area
      • Types: Locations, lines, areas, surfaces (The whole nine yards again)
      • Good for finding and displaying what’s within each of the several areas, but requires more processing 
      • The color palettes for these maps were created by god himself, they’re aesthetically pleasing and honestly are a little reward for reading through this book

Chapter 6

  • This is a thick mama chapter indeed
  • Using GIS can help us find out what’s occurring within a set distance of a feature, and help us find what is within traveling range. 
  • This makes me think of Apple or Google maps a bit, where you rely on the app to help you find things like restaurants, stores, etc. (Usually it tells you most places within a 20-40 mile radius rather than giving you a location to an ice cream place in Wisconsin, unless it is a business name within Wisconsin and that is the only one present in the GPS system.)
  • Totally not a virus. Trust me…im a dolphin
  • I never knew mapping.  I typed this thought out about 7 hours ago and I had no clue what I was going to say. 
  • Wait, I remembered what I was going to say. I never knew that maps had a cost or budget really. I know that there’s a system budget that is more like a resource value rather than a currency oriented cost but this seems like an actual cost.
  • Okay, so cost is more of an aspect like time and a more precise measure of what’s nearby. 
  • Things are adding up in the little brain, does this explain why there are sometimes alternative routes? (other than obvious factors like road construction, etc.) Because sometimes the cost of time isn’t really as valuable to those who aren’t on a time crunch compared to others? (Ex. It takes 1 hour and 35 minutes for me to get home to Cambridge, OH from Delaware, OH. I hate taking the highway so I’m willing to give up that cost of time and take a 2 hour drive home if it means I don’t have to take the highway.) 
  • Planar method: appropriate when area of interest is relatively small (cities, counties, states.) 
  • Geodesic method: larger scale, revolving around the interest in bigger areas like countries, continents, the big momma Earth. 
  • Lots of reiteration in these chapters, also that equation for distance was yucky. 1.5/10
  • Cost layer?? 
    • Reclassify existing layer based on an attribute value. 
    • Creating multiple layers? Combine all the input layers. 

Chapter 7

  • People typically map what’s changed in order to anticipate future conditions
    • Does this apply to climate change? Like those deforestation maps where we anticipate less treeage in an area? 
  • It does, I think. (Flipped to the next page and was violently humbled. :o) ) 
  • We can also look at features that move! Although I find that hard to calculate at the moment unless you’re a meteorologist or a person who monitors natural disasters frequently. 
    • Discrete features: tracked as they move through space. So we can map paths for things like hurricanes or animals. 
    • Linear can track things like the direction a stream is going or the boundary of a fire. 
    • A fun fact about me is I hate Smokey the Bear, if you read this post, ask me why. 
    • When it showed linear range, it made me think of my animal behavior class in the spring when we looked at turtles going to nest and then going to the opposite pole location because of the magnet things in their brains. (So it was technically the right area, but not. AKA, the turtle went to the other side of the island, but was in line with the location it was supposed to nest at. I don’t know how to explain this.) 
    • Natural disasters and crimes represent geographical phenomena that occur in different locations. They are tracked and mapped in a specific instant. 
    • How do we map things in real time? Does that fall under the realm of specific instant? 

The three time patterns

  • A trend-change between two (or more) dates or times 
  • Before and after-conditions preceding and following an event
  • A cycle-change over a recurring time period, such as day, month, or year. 
  • Moth 

Three more ways, but for mapping change

  • Time series 
    • Good for showing changes in boundaries, values for discrete areas, or surfaces. 
    • Create a map for each time or date showing the location or characteristics of features. 
  • Tracking map 
    • Good for showing movement in a discrete location, linear feature, or area boundary. 
    • Create a single map showing locations of the features at several dates and times. (Weather?) 
  • Measuring change
    • Change to show the amount, percentage, or rate of change in a place. 
    • Calculated by difference of amount in a category or in the value of numeric attributes, and display the features based on said values. 

Good stuff John. 


Lee Leonard-Week 2 oops

Chapter 1

 I actually enjoyed this chapter a lot because it expressed different types of analysis in numerous parts of daily life (%forest, burglaries, parcels near a liquor store, etc) I cannot stress how cool it is that GIS is around us more than just using it for maps and navigation, which was my initial thought prior starting this course. 

Keeping in mind what different types of geographical features are and how they are represented, a discrete feature is a feature that has definite boundaries. An example of this is a lake or a building. Continuous phenomena is something that can be measured regardless of location, so in the book it mentions temperature or precipitation. (no gaps, starts off as sample points.) Lastly, summarized data is the density of individual features within a boundary, the data applies to a whole area, but isn’t really a specified location. (Example: 740 area code is typically in Southeastern Ohio, but it isn’t specific to what county it is in. It could be Guernsey, Belmont, Noble, or even Marion county. It’s more than those counties. I’m just using the example that it isn’t limited to one county.) There are two ways to represent GIS model wise: These are called vector and raster. 

Vector: This is defined using x,y locations in an area which does not have boundaries, GIS then connects these dot-like coordinates to  draw lines and outlines. These dots can be areas, lines, events and of course locations. Main takeaway: Vectors utilize lines as a way to create almost an outline for locations, streams, and areas. ->discrete and summarized data typically

Raster: These are seen as more of a matrix of cells with continuous space. Used in layers, but layers can be added on top of one another and analysis is then done by combining all of the layers to make a new layer that contains cell values. This seems to rely more on scale of the cell, because it changes the layer being analyzed and also the presentation of the map. Main takeaway: cell size should be close to the original scale of the map, because using too large or small of a cell size can cause conflict with information and lack of precision on the map. ->continuous values

Both Vector and Raster: Continuous categories. 

Map projections: locations on the globe, but are translated onto a flat surface like a map (Flat earth vibes, not liking that but okay GIS.) 

  • Distorts features and shapes, also measurements of distance, areas that are already established (counties, villages.)

Coordinate system: Uses specific units to target features in a 2D space, as well as the origin of those units. 

Good gravy, this chapter is packed with information 

I liked that it mentioned land use in this chapter, we learned about that in GEOG 347, so that’s rad. 

Chapter 2

Now there is some degree of significance to plotting and mapping things, mainly because when we look at a map, we are looking for something specific, whether it is a similar structure or a pattern that is familiar to us. (When I drive home, sometimes on Apple maps I look specifically for the Y bridge on Zanesville because it is what I use to get into Cambridge, I can see this on a map and in person.) Maps also can be used to determine trends in areas (ex: Police officers investigating crime activity and seeing whether or not it is in the same area or different parts of the city.) 

Maps are dependent on audiences and what issue is being addressed. (I really liked the color palette for the keys, sorry that’s random but it’s very pretty.) Features require locations, and locations require coordinates prior to being typed into a geographical database (so latitude, longitude, and addresses.) 

It’s important to use specific colors that draw attention to different categories on maps, oftentimes soil maps utilize different colors and codes because soil types are fairly wordy sometimes, but they put the full name in the legend/key. (I think back to a time when I was in a soil microbial lab where this person showed us all the soil types in Kalamazoo county. Here’s the website if anyone wishes to take a gander at it, https://websoilsurvey.nrcs.usda.gov/app/WebSoilSurvey.aspx

 After the hour-long talk the person ended it by saying soil doesn’t matter and I wanted to cry. anyways.) Using recognizable features in maps are extremely important to audiences, especially those who live there that are looking for specific rivers, landmarks, maybe a weird statue. Patterns can be seen just by the human eye, but sometimes maps have “easter eggs” for those who may see different patterns, this varies on how meaningful certain details are to people. 

Chapter 3

So why are we actually mapping all details? Shouldn’t we just focus on mapping the most important features out there? NOPE. Everything is significant in some sort of way, and may actually be beneficial to someone. (typically businesses.) You can include and exclude things from maps just by a degree of relevance, because honestly it may be weird to have “how many slugs are in each yard of Delaware, OH” on a map that shows who all has a subscription to oriental trading company (I bet I just unlocked a memory for you, you’re welcome.) In legends, specific things can be shown by having bigger or smaller, or thinner/thicker lines. (Expressed by having big circles as 2501-8000 employees, or very thin lines as unknown fish habitats in streams.) Mitchell describes continuous phenomena as more of a colorful part of the map. Where areas are displayed as graduated colors and surfaces are contoured, or a 3D perspective, but can also be graduated. (Remember my palette comment in chapter 2? I think this is what I meant. My heart and little GIS mind was in the right place.) Typically lighter colors are expressed as little or a lack of in a map, while darker more shaded in colors are seen as plentiful, lots of. I feel you can heavily argue the point made on page 56 of the Mitchell textbook based simply on perspective. Maps can be a way to express data or even trends especially if used in a long term fashion. (Long term deforestation, crop rotation, droughts, maybe long term crimes? Sue me.) 

Counts and amounts:  total numbers. (I actually don’t know what I truly meant by this, but I’m going with it.) 

  • Counts: actual numbers
  • Amounts: total value associated with each feature
  • Can be used for discrete features

Summarizing by area is a bit harder, because counts and amounts throw a wrench in the patterns if areas are different in size (use ratios so this is accurately represented) 

Ratios: Relationship between two quantities and are created by dividing one quantity by another. 

  • This evens out differences in areas that differ in size, but this again is the most significant for summarizing by data
  • Averages are also good, because it helps compare places that have little features to places that have a variety of features  

Standard classification schemes: 

  • Helps group similar values to look for similarities in data (I saw some statistics, gagged, and then moved onto chapter 4.)
  • I liked this chapter towards the end because it showed a lot of ways to plot things on a map (I think I saw a pie chart on one page? I saw topography “contour lines” and was very happy about that) 

Chapter 4

Mapping density is very important when it comes to looking at concentrations of features in an area. 

  • Useful for census, counties. I think this may also be used on occasion for political party concentrations in certain states?
  • Dot maps represent density of individuals locations and is summarized by defined areas
  • Divide total number of features or total value  by the area of the polygon=density value

The Dense surface is created via a raster layer and is more blobby (I think of this as when you look at a map when a storm is coming. Is it a cluster of dots coming at your town or is it a huge blob?

Be careful with dot patterns, they should be an appropriate size for the areas on the map. Too small or too large can obscure patterns which messes up the point the map was trying to make. 

  • Search radius: larger it is, more generalized patterns in density surface will be
    • Consideration of more features when calculating value of each cell 
    • Number of features is divided by a larger area
    • Smaller search shows more localized variation, but broader patterns in the data may be shadowed because of small radius
  • Calculation method: Uses one of two methods 
    • Simple: counts features within radius of each cell, which forms ring like structures that overlap each other 
    • Weighted: Mathematical function to give more relevance to features closest to the center (No edge effect here) Smoother and more generalized density surface. Maybe easier to interpret.

Units allow you to specify areal units you wish to use for density values. 

  •  If areal units are different, values in legends may be extrapolated. (may predict hypothetical values that fall outside the specific data set.) 

Main takeaway from this chapter: A map is so much more complicated than I ever anticipated in my lifetime, there are so many factors that go into this and I didn’t even know you could incorporate standard deviation into a map. That’s insane, mapmakers are insane.

Lee Leonard-Week one

Howdy! I’m Lisa Leonard (I prefer to go by Lee) and I’m a senior studying Zoology and Environmental sciences. I’m from Cambridge, OH. I’m taking this class because I realized I do not know much about GIS and wish to comprehend the program ArcGIS and other GIS-oriented things. I did an REU over the summer involving long term ecological research (Also drought legacies and how plant-soil feedback loops react when a stress variable is added in) and one thing my mentor recommended to me was learning how to work with GIS, so I’m here today. My interests in zoology and environmental sciences are biological indicators, specifically invertebrates, and lichen. I also like to study anthropogenic activity.

lover of annelids <3

I think one thing I heavily appreciated about the readings is the diversity in disciplines. It heavily emphasized that it wasn’t just used in geography, but rather spread across multiple fields. I personally never knew that those outside of the natural science bubble could have a use for GIS, so when I read that GIS was quite literally all around us (From getting your morning cup of Joe to organ donation), it blew my mind. I liked this chapter a lot because honestly I’ve stayed away from GIS because it seemed too complex, but now that I’m reading more about it I feel less intimidated? Stay tuned. It’s a nice dip your toes into the subject chapter in a way. I think it was more cooler seeing the figures than reading about it (i.e. Figure 1.4: Cholera in London in 1854) because that was before GIS was even computerized!

I looked into the different forms of GIS used at the place I did my REU at and found a lot of different images that I didn’t even know existed! Attached is a link to the W.K. Kellogg Biological Station’s Long Term Ecological Research site, where they have various scales of data, from soil to the roads in Kalamazoo county. (https://lter.kbs.msu.edu/data/gis-data/) Try not to click metadata because it has a more coding set up but please look at the images if possible! (The soil one looked so cool!)


For my research, I chose a paper called ‘A GIS-based method of lake eutrophication’, which was a fairly tough read honestly. While it isn’t 100% my preference, I felt it was significant to discuss eutrophication from a GIS sense because eutrophication is a form of anthropogenic activity caused by an overload of various nutrients leaking into waterways (this is usually caused by agricultural practices) and causing a decline in fairly sensitive organisms, such as amphibians. This paper doesn’t shine any light on our poor slimy amphibian friends, but rather discussing a variety of physical, chemical, and biological indicators. (Phytoplankton was the biological! I assume because some species do super swell under stressful conditions, while other species are extremely sensitive to eutrophic environments.) This study took place in a body of water, called ‘Lake Chao’, located in China with HIGH levels of eutrophication. These high levels have impacted the population around them socioeconomically, ecologically, and even caused the population to have some pretty intense health effects. The main GIS aspect these focused on in the results was a lot of spatial distribution, and what areas of the lake were heavily impacted and what parts were not. They actually said that the eutrophication levels and the genuine conditions of the lake were not too far off from each other. However, there is no distinct indicator or parameter that can be evaluated in a simple fashion when it comes to a body of water, but if we put multiple different indicators together to create a distinct evaluation of a lake assessment. I think this paper had a lot of complexity to it and frankly, the photo I’m attaching below from the paper seems intense to even explain

One fragment of figure 3 from the paper. It went from a-f and seemed to be explaining the trophic state index on a spatial scale? This was with the various indicators but holy moly I feel violently humbled.


Xu, F.-L., Tao, S., Dawson, R. W., & Li, B.-G. (2001, October 30). A GIS-based method of Lake Eutrophication Assessment. Ecological Modelling. Retrieved August 28, 2022, from https://www.sciencedirect.com/science/article/pii/S030438000100374X

This was from a journal called Ecological Modeling.

I also looked into the use of GIS with lichen and was not disappointed with what I found. Air in urban environments isn’t really good in terms of quality, and lichen is a great biological indicator to look at when understanding air quality. In this study they also used moss because lichen and moss both are great at absorbing things, making them great candidates for indicating toxicity in the air. In the article, there was a map (figure 4) showing agglomerations of lichen and moss (Used a high for higher concentrations in different areas) It was so cool to see it because I didn’t really expect GIS to include such microscale pieces of nature. I wonder if there is GIS data on every ant in Michigan. That’s so crazy to me. It also showed a wind rose in figure 2, which showed direction and speed of wind as well as the concentrations of toxins in the air. I think it’s awesome how GIS can have different ways of expressing data like how R can too (or scientists making graphs and different forms of data in general.)


Długosz-Lisiecka, M., & Wróbel, J. (2014, September 24). Use of moss and lichen species to identify 210po-contaminated regions. Environmental Science: Processes & Impacts. Retrieved August 28, 2022, from https://pubs.rsc.org/en/content/articlelanding/2014/em/c4em00366g/unauth

(If you happen to look at this, on the right there is a yellow bar that lets you see the whole paper.)