Author: adveerjee

Chapter 4: Mapping Density
Why would somebody want to map density? People map density in order to see the highest and lowest concentrations of certain features. This is done mainly to see some patterns rather than where these features are. It is easier to see density with different colors than using points on a map. There are 2 key approaches to mapping density, creating shapes through a density surface. And using a defined area, through either using a dot density map or through a shade based on a determined value. It is very important to keep the units consistent. When creating a dot map, it is important to have a decent conversion rate for the data. Such as 1 dot for 200, but this does sacrifice accuracy as instead of an exact location, the dot will go in the general area of the features. With the dots themselves, it may be keen to change how the dots look in order to exemplify a pattern, mostly through the method of sizing up or down the dots.

Key Takeaways:

• Calculating the density value for inconsistent units
• pop_density = total_pop / (total area / conversion ratio)
  • The reason for 27878400 is that is the total square footage in a square mile. So if I wanted to turn square footage to square mileage…
    • Population density = total population / (total area in greater units / 27878400)
• The Balancing Act of Dots: The less a dot represents, the more exact the area will be, but the map will be full of clutter. There is a balancing act between amount & size to adequately show the desired pattern & data.
• Search Radius: The larger the radius, the more generalized the patterns & density.
• Displaying density: We can use either graduated colors or contours in order to display density.
  • To display the best amount of contour is a black, light gray, or white for 0, and a color as anything within the displaying pre-requisites.

Chapter 5: Finding What’s Inside
People map what is inside in order to compare several areas based on the interior of them. While seeing a pattern is important, providing information on what is inside of the mapped area can provide a crucial amount of context. If this were to be used for something similar to an official proposal, there may be explanations that are answerable by the context provided by the map. Why would there not be any sales data for a large amount of land within a town? There may be a large parcel of land owned by 1 person for a farm. Are features discrete or continuous? This can be crucial for the context that an interior map provides. The cartographer will also need to answer the question of what features deserve documentation. We may use different methods such as the raster method to take in information about the area that we have selected and overlay different sets of data over each other in order to get a result regarding the different sets of data that we are using for this location to see patterns. We can then use the information generated to either make a map using it or different types of charts hat display a few of the key indicators together.

Key Takeaways:

• Discrete vs Continuous features:
  • Discrete features are unique & identifiable. These can be listed & counted. Some examples are addresses, towns, buildings, crimes, or known locations of animals.
  • Continuous features: Supposed to represent a seamless geographic feature. This can be elevations, sea levels, climates, etc.
  • Count v frequency:
    A count would be the knowledge of how many features are inside of a total area, whereas frequency would be knowing how many of a counted feature is inside of an area.
    Usage of the Raster Method: Using a combination of raster layers to compare each part of the cell with categories. It will then come up with an end result of some sort to put in the table. This is typically the most efficient way to display overlapping features.

Chapter 6: Finding what’s nearby

People usually want to find out what is nearby in order to use the patterns generated by the data to predict what may come up in the future due to various conditions being tracked. This is called the travel range, which can be measured through either time, distance, or cost. This information can be used to decide what to do & potential ways to combat potential problems that come up in the future. This is typically done by showing what is near by the area that is mappeed using methods used in chapters 2-5. Within the three key ways that I have listed, which are straight line distance, distance or cost over network, and cost over surface, once we have picked whatever method works the best, we can create a buffer to see what is within the distance through the method that we have chosen. There are several methods to measuring this distance through maps, such as point -> point, using color codes, or spider diagrams. Having a lot of data for this type of map is key, and needs to be gathered & separated accordingly to the needs of the maps.

Key Takeaways:
Three key ways to measure what is nearby:
Straight line distance: Used to see what is around the selected feature using a distance in order to create some sort of boundary or see what features are within said distance.
Distance/Cost Over Network: Measures travel based on things such as roads, typically used to see the cost/distance of travel between two+ points.
Cost over surface: Measures overland travel, such as a plane, to see the area within a range based on different costs.
Rings: Typically used for finding how much a value of features increases/decreases as the distance covered increases/decreases.
Buffer: Typically used to create a boundary of some sort, there may be multiple.
Bands: Good for usage in comparing distance.

Chapter 1:Introduction to GIS Analysis

GIS analysis is similar to something that I am sure many of us have been doing, but now we are putting it into a geographic context. Especially when looking for both patterns and relationships, sometimes these maps can be self explanatory, however the more interesting problems are finding ways to explain both the correlation and causation of what occurs and what data is shown with the maps. The GIS analysis process that the first chapter outlines reminds me of the scientific method if we were not to make a hypothesis, finding a question to ask, finding a way to frame the question while also being able to measure said data, understanding the data that is presented and gained, choosing a method of measuring the data and method to present the data for further analysis, processing the data, and looking at the results of the data. One thing that I had not considered before was the many ways of looking at the results that are gained, such as displaying them on a map, looking at a data, or within a chart. Measuring geographic features can be extremely useful, but I was unaware at first of how many different types of features there are, there are things easier to measure such as mountains, city limits, and where rivers are located, those things can be pinpointed and are considered discrete features.
Discrete features seem to answe the question of ‘is this feature here or not?’. Continuous phenomena is something that is a value that can change during the average day, such as precipitation or temperature. Continuous data can be represented in areas enclosed by boundaries assuming that everything within the data is the same type.
Features summarized by data can represent a simple count or density of various features within a certan enclosed area.Within GIS, there are 2 different ways to represent data, through vectors and rasters. With vectors, the system uses a table with shapes & points to represent data at certain sets of coordinates and bounds, this is most similar to a graph or shapes. With a raster model, it is similar to a large excel sheet where you paint the different types of cells to represent different numbers & data points. The book states that both types of representations of the data are good, discrete features are usually represented with vectors, continuous are represented as either vector or raster, and continuous numeric values are represented with raster models.
With attributes, every feature has some attributes that are able to be used to identify whatever we are trying to represent. These attributes are the following: categories, ranks, counts, amounts, and ratios. A category is an overarching topic that contains a group of similar stuff. One example is that if I were to be mapping a city, i would put office buildings, restaurants, and shops in under the category of ‘Businesses’. With ranks, I would put something in order from high to low, or Excellent -> Poor. With ratios, I would put different colors for the amount of features in a map, if I were to try to map something similar to population density, a lighter color would reflect a lower amount of people living there, and a darker color would represent a higher population. If I were to use a count, I woult count something such as the amount of customers going into a business and make a larger circle around the business if there were more customers.

Chapter 2: Mapping where Things Are:
Looking for patterns can be key for helping me understand the are that I am mapping. Something such as a crime map can help me understand what the biggest issues of the area can be in terms of crime, maybe see what parts of the city meet more crimes vs lesser crimes, which could explain where police usually are, or if crimes get reported in general. The main decision is deciding what needs mapped, what to display and how to display them. There are different purposes for different types of maps, such as the example with the police department, a business needing to know its demographics, and other considerations need to be made. How the map gets used is something that becomes a key issue when thinking about how to create the map, while a city zoning map would be useful for bringing up to a committee meeting, it may not be as useful for other purposes such as the case of where crime occurs. The level of detail is needed to be put into consideration for what type of audience will be seeing my map, will it be for a general audience, or some seasoned professionals? Some key considerations for my maps to make sure that I know I have geographic coordinates, and hopefully have both a category & value for every main part of my map. When I assign a map feature with a type, I must have a code within the feature. These types should be stored prior to adding them to the map so I do not have to go back and add them later. Some categories can be hierarchical, and will have a ubtype, such as general zoning vs mixed usage. When I make my nmap, I need to know what features I would like to display and figure out what the symbols I will be wanting to use. With a single type, I will represent all features with the same symbol, like If I wanted to represent sales by delivery, I would represent each sale with a dot. I can also separate data and map only certain types of data, such as amazon deliveries vs uspc deliveries. I am also able to map by category, this is typically done with different colors, but I will only want to do up to 7 different categories at one time as most people wouldn’t be able to distinguish more than those 7. If I were to display more then 7, it would be more wise to group those categories together on a secondary map that is easier to digest such as the one on Page 40. If my map does its job and presents the information pretty clearly, there should hopefully be a pattern that is able to be more easily seen and understood.

Chapter 3: Mapping the Most and Least

The reason people map the most and least of something is to find a pattern or find qualities of features such as those within real estate. Some things I am able to map with Most -> least is any feature associated with discrete, continuous,m or data summarized by an area. The discrete features are typically represented by graduated symbols while areas are often shaded to represent quantities. Continuous phenomena are typically displayed using graduated colors while even a 3d perspective view can be used to represent the continuous surfaces. Data summarized by data is typically displayed with shading the area via the value while using a chart to use it as a representation of the data. While forming the map, I need to keep the indented audience & purpose both in mind. If I were to just use the map for presentation, I will want to explain what the data points mean, however if I were to be trying to explore the data, the map would provide a good baseline of a direction to go in when it comes to patterns and ideas. There are many numerical ideas that I will want to keep in mind, such as amounts, counts, ratios, or rankings of various areas. I will want to find the best way(s) to represent these through the data. This is typically done in the method of using gradients or small -> large shapes. Once I figure out the quantities and type of quantities, I will want to figure out how to classify the data. If they were to be individually presented, I will not need to group them. If I were to group points of data together, I will want to use classes by assigning them the same symbol. I would want to use standard classification schemes if I were to want to group similar values in order to look for patterns. I’ll be able to figure out the best scheme for creating the class break by looking at the distribution of the values of data. One of the best ways I am able to do this is by creating different graphs or charts using the data. But that being said, it is incredibly important to keep things concise and understandable.

1. Hello! My name is Aiden Veerjee, Junior and I come from Johnstown Ohio. My major is in Quantitative Economics and I have a minor in Geography. I am in Alpha Sigma Phi, I am in the Economics & Business student board, and I am the Comptroller for WCSA (Student Government), I am also the President for our school’s Investment Club. I am also interested in Chess and Fencing, I’ve been doing a lot of reading & working out.

2. One of my professors for Economics had brought up that he had worked with a student who used GIS for a research project for modeling labor, but I had not fully realized that a lot of different disciplines had used GIS in various ways. I was especially surprised to the extent of sales that the GIS technology has achieved. I had only seen Geography mostly as the looking at political & land-maps, but not as much of the data within certain spaces & the specializing of resources / data. Before this chapter I was more unaware of the source of people using GIS systems, I had originally thought it was a more modern invention with the modernizing of computers, more internet access, and the general monetizing of data analysis. I had beenvery surprised to know that GIS had been used for a lot longer than just the twenty-first century. One of the biggest questions that I hope to answer by taking this class is how well can models actually reference or represent the different environments that it composes of. It is one thing to say that the Earth is 75% water, and another to say where that water is, what regions have access to the water, and how said water is being used. I hope that by taking the course it will help me understand methods with answering questions such as this one. I had known about how solid data can be interpreted in many different ways and different people can come to different conclusions even when the data remains the same. Even with the way that data is collected or classified can lead to loads of different issues, such as one example that the text cites with how that mountains & hills are determined. Drawing boundaries can also lead to some issues, and I would like to see how or if these issues can get rectified.

3. One topic that I have found interest in having to do with GIS is real estate. Real estate is a field that can take a lot of usage in collaboration with systems such as GIS. I had found some data on the agricultural districts within certain counties, I had wanted to see how close agriculture is to densely populated areas like cities and suburbs. I had thought before getting into the data that the agricultural districts would be further away from densely populated areas. Unsurprisingly, this had been reflected as true within Wake County NC. The blue within the image represents an agricultural district. I was surprised by some districts having a higher proximity to minor roads rather than big highways. Two different ways that this data can be used is to predict where good farmland might be for future farming. This can also be used as information for County taxation as since there is a very small amount of farmland, there may be less taxes & revenue gained through taxing agriculture.

Sources:
Wake County Map