Author: drpatel

Mitchell Ch.4-6 300 Word Summary

 

Ch.4

This chapter focuses on mapping density, explaining why it is useful, how to decide what to map, and the two main ways of creating density maps. Density mapping helps identify and visualize areas where features are concentrated, making it easier to compare regions of different sizes by standardizing values to a common unit of area. This is especially helpful when working with large sets of features, such as crime incidents or businesses, because raw maps of locations can make patterns hard to see. For example, mapping burglary reports over time in different parts of a city can show where concentrations are highest and how they shift. Before making a density map, you should first decide which features you are mapping and what information you want the map to reveal, since this choice determines the method you use. The first method is mapping density for defined areas, which uses boundaries such as census tracts or neighborhoods. Within these areas, you can create dot density maps, where each dot represents a fixed number of features randomly placed to give a visual impression of concentration. Alternatively, you can calculate density by dividing the number of features by the area size and shading each unit accordingly. The second method is creating a density surface, which uses statistical methods to spread feature counts across space, producing a continuous surface that shows how concentrations rise and fall. This approach highlights hotspots and gradual variations without being limited to arbitrary boundaries. Each method has advantages: dot and calculated area maps are straightforward and good for comparisons between administrative units, while density surfaces provide a more detailed picture of distribution. Together, they show how density mapping is a powerful way to uncover spatial patterns and make more informed decisions.

 

 

Ch.5

This chapter was on mapping what’s inside a designated area. To map what’s inside you can use an area boundary to select features (kinda like click and drag I think). When mapping what’s inside the book says to consider how many areas are selected and what features they contain. Single areas let you monitor activity or information on the area. Mapping several areas at once lets you compare and contrast each area. Discrete features are unique, identifiable features kinda like landmarks where you can list, count, or assign a numeric attribute to them. Continuous features represent seamless geographic phenomena like topography or what an area’s composition is. The information within the analysis helps to identify the method to use such as a list, count,  or summary. What’s important is that you only list features that are within the borders of your area and nothing intersecting more areas. Drawing areas need a dataset to work and are good for finding how many features are inside or outside your area. Selecting the features helps you quantify and summarize features in an area but you need a dataset containing features and areas. Overlaying is for finding what features are in each area and is used for summary statistics. This method requires a dataset of areas and features. These 3 methods that measure specific things for an area and each have their own unique attributes and disadvantages. When making a map it’s key to decide what features are inside and outside an area. When making a map you can make features apparent by categorizing or quantifying them and then drawing the area on top in a thick line. When mapping several you should list them to make it easier for others to follow. No matter the method, always make sure that you know what features are inside your area, the method fits the prerogative, and that others can follow along. 

 

 

 

Ch.6

Defining what’s nearby helps you scope what features are within an area or set distance. Defining the analysis is done by measuring straight-line distance, measure distance or cost over a network, or measure cost over a surface. Measuring this is based on your definition of nearby and as a tip you can set a defined distance as a limiter to what you consider nearby. Identifying the information you need from the analysis is key. After establishing what the distance is from the source you wanted you can choose a list, count, or summary to a feature attribute. To find what’s nearby you can use a straight line distance you set. There are 3 ways to find what’s nearby strait-line distance, cost over a network, or cost over a surface. Strait line distance is done by setting the source feature and the distance you want to limit your scope too (GIS finds the area and the surrounding features within). Cost over a network allows you to specify the source locations and a distance to a feature linear of the source (GIS automatically finds what’s within these parameters). Cost over a surface allows you to specify the travel cost  and the source locations. GIS will then automatically create new layers for each source’s travel cost. Making a straight line distance is done by creating a buffer to define a boundary and likewise what’s inside it, calculate feature-to-feature distance to find and assign distance to locations near a source, or by selecting features to find features within a given distance. Creating a distance surface allows you to create a raster of continuous distances from the source. As quoted from the book “You can use the distance layer to create buffers at specific distances, and then assign distance to individual features surrounding the source or find how much of a continuous feature”. When it comes to scoping whats within a feature one thing is for sure mapping and setting what your distance will be and how you wish to carry it out matter.

Mitchell Chapter 1-3 Summaries

 

Chapter 1

 

The book starts off with a question how important GIS is and what it’s used for/applications. The book additionally tells you what tasks in GIS are common such as mapping where things are, mapping the most and least, mapping density, finding what’s inside, finding what’s nearby, and mapping change. The most important things to consider when performing a GIS task are how it will be used and who will use it according to the book and personally I agree. Essentially when you conduct a GIS survey you should choose how much effort would be appropriate for the task. If its for a court case on ENVS policy on deer hunting you should find all the data to who and in what county someone killed more deer than is allowed but if it’s a survey then maybe you list the total for the state in general. Additionally for GIS there is a 5 step method to analysis processing framing the question, understanding the data, choosing the method, processing the data, and evaluating the results.These steps ensure that analysis is systematic and produces reliable outcomes. Another key point in the chapter is the distinction between different types of geographic features: discrete features such as businesses, parcels, or rivers; continuous phenomena such as temperature or rainfall; and data summarized by area like population totals within a county. Each of these can be represented using either a vector model, which stores features as coordinate-based points, lines, or polygons, or a raster model, which represents space as a grid of cells, each with its own value. Attributes linked to features are also critical in analysis, and they can be categorized as nominal (categories), ordinal (ranks), interval/ratio (counts or amounts), or ratios that standardize values like population density. Finally, the chapter emphasizes basic operations like selecting, calculating, and summarizing attributes in tables, which allow analysts to extract new information from raw data.”

 

Chapter 2

 

The chapter talks about deciding what to map and what to include in a GIS map. According to the book when mapping a layer you designate a symbol to each type of data. A layer is a collection of geographic data pertaining to one type of information about the place you are mapping. For example one layer could be a street, another could be houses, and finally one can be the cars if you’re mapping traffic data for neighborhoods. The book emphasizes that the map should be tailored to the audience you’re presenting it to. Additionally for every point you plot you should have the cords and optionally the data corresponding to it if necessary. When you map features by types you should include a code that identifies its type of info. Additionally when creating a category you need to specify the layer’s data on the data table and assign the appropriate value to the feature. When mapping a layer you can include multiple different types of info into one layer with a symbol for each or one type per layer. You can additionally categorize information if you choose or hierarchically rank each data under a symbol/shade. Sometimes if a category is complex you can create different maps per category. The book says displaying types of categories may make it easier to see how different categories are related. The book sets a limit to categories as well and emphasizes that if you’re writing multiple categories onto one map then 7 is enough. Additionally the distribution of features affects the data. “When a map shows many small, scattered features instead of large continuous areas, it becomes harder for readers to distinguish between categories. In cases where features are spread out, it is possible to display more categories, but if the features are densely packed, fewer categories should be used for clarity.

 

Chapter 3

 

Chapter 3 of the book focuses on how to interpret data after it has been gathered. It offers practical guidance on what the data can reveal and how to present it effectively. For example, mapping patterns with similar features and categorizing them can help in selecting the most appropriate data for an assignment. The chapter also introduces the concept of continuous phenomena and area. In GIS, an area is defined as the amount of space inside a boundary on a map, typically measured in square units. The book explains how areas can be displayed using graduated colors, contours, or 3D perspective views. Interpreting data in terms of area can involve shading each region based on its value or using charts to show the amount of each category within that area. GIS professionals often summarize individual locations and linear features within areas to help communicate patterns more clearly. A key part of data interpretation involves understanding quantities, where features are symbolized based on their attributes. Chapter 3 discusses the use of counts and amounts, which show the value of each feature and allow for comparison across features. These can apply to both discrete features and continuous phenomena. However, the book notes that using counts alone can skew results if the areas differ in size. To address this, it recommends using ratios-created by dividing one quantity by another-to reveal relationships between values more accurately. The chapter also introduces ranks, which help by assigning a hierarchy to combinations of attributes. Finally, it explains how to set up classes to group counts, amounts, or ratios. Classifying data helps determine the types of quantities being dealt with and allows for clearer, more consistent analysis and presentation.

 

Hello, I’m Dylan Patel and I’m a Jr currently majoring in Environmental Studies. I’m from Anaheim Hills near Disneyland.

 

Learning about GIS was spectacular. I didn’t know that GIS had so many applications in industries like Starbucks to maximize its store placement, epidemiology for identifying infectious disease clusters, and even law enforcement for crime mapping. Of all of these, epidemiology surprised me the most because I always assumed GIS was mainly for cartography and city planning.

I was also intrigued by how, in the 1950s-1960s, there was pushback against digitalization. At the time, computer-generated maps were considered crude compared to the artistry of manual cartography. Yet pioneers like Brian Berry, Waldo Tobler, and Duane Marble in the US, and Tom Waugh and Ray Boyle in the UK, pushed forward with algorithms and code that laid the foundation for spatial analysis. That persistence transformed GIS from “computerized cartography” into a powerful analytical science (Schuurman, 2004).

Connecting GIS to epidemiology is especially fascinating. A famous historical example is Dr. John Snow’s 1854 cholera map in London, where plotting deaths around contaminated water pumps revealed the true source of the outbreak. That map demonstrated how spatial visualization could unlock hidden relationships in health data. Building on that legacy, epidemiologists today use GIS to identify disease clusters, track outbreaks, and assess environmental risk factors. For example, GIS has been used to study cancer incidence in relation to toxic exposures, evaluate where infectious disease cases are concentrated, and plan interventions for public health emergencies like COVID-19. What makes GIS so powerful in this field is its ability to merge multiple layers-population density, environmental hazards, transportation routes, or healthcare access-and reveal patterns that would remain invisible in raw tables or reports. As Rushton (2003) argues, GIS provides health officials with spatial analytic tools that can improve both research and decision-making, ultimately saving lives.

Overall, Chapter 1 made me realize GIS is more than just “maps.” It is both a science and a system-one that influences how we see, organize, and act upon the world. As an environmental studies student, I can see myself using GIS not just for conservation, but also to understand how environmental health risks connect to human populations.

Source: GIS basics page, The Epidemiologist R Handbook (used for the illustrative image) EpiR Handbook

An example of how GIS is used to track the spread of diseases and relative populations.

______

Schuurman, N. (2004). GIS: A Short Introduction. Oxford: Blackwell.

Rushton, G. (2003). Public health, GIS, and spatial analytic tools. Annual Review of Public Health, 24, 43-56.