Chapter 4: Mapping Density

Mapping density helps show where the concentration of features is the greatest, and is useful for looking at patterns instead of the locations of features by themselves, for both areas with many features or features per unit of space. When deciding what to map, you should think about the features you’re mapping, as well as any information you might need (density surface), using either data that has already been summarized or by mapping density or feature values yourself. The two ways of mapping density are by a defined area, such as a dot map, if the data is already summarized, or by a density surface, using a raster layer in which each cell gets a density value based on the number of features within a radius of the cell, if you have individual locations, sample points, or lines. A density surface is created by using raster layers, where GIS calculates a density value for each layer. A neighborhood is defined, and the total number of features is divided by the area, which is then assigned to a cell. This creates an average of the features per area. Larger cell sizes create a coarser surface that processes faster, while smaller cells create a smoother surface that processes slower. To calculate cell size, you need to convert units to cell units, then divide that by the number of cells, and take the square root of that number. The search radius is the number of features divided by a correspondingly larger area, in which a larger search radius will produce more generalized patterns, and a smaller search radius will produce less generalized patterns. Calculation methods for cells are either simple (creates overlapping rings), or weighted (creates a smoother surface). Units chosen to create a cell should correspond with the features and what you hope to get out of the map.

 

Chapter 5: Finding What’s Inside

Mapping inside an area shows what is occurring inside an area, and is useful for comparison. You should consider whether you will need a single area or multiple areas. A single area is useful for monitoring activity and summarizing information, while multiple areas allow for them to be compared. Features can be discrete (unique and identifiable) or continuous (seamless, a summary). A count, list, or summary should be used as information. Three ways of finding what’s inside an area are drawing areas and features, selecting features inside an area, and overlaying the areas and features. When making a map, Locations and lines should be used for individual locations or linear features, discrete areas for seeing parcels inside a single area, and continuous features for drawing the areas symbolized by category or quantity. Selecting features inside an area is used for specifying the features and the area, and GIS then flags features in a specified area. The amount of features in an area can be counted in the following ways: 

• Count – total number of features in an area
• Frequency – number of features with a given value, or range of values
• Sum – overall total or total by category
• Average – total / # of features
• Median – middle value of a dataset
• Standard deviation – the average amount that values are from the mean

 

Finally, overlaying areas and features is used for finding discrete features within each area. 

 

Chapter 6: Finding What’s Nearby

Mapping what is nearby an area or feature allows GIS to find what is occurring within a set distance of a feature, and also find out what is within traveling distance. In defining your analysis, you should be able to define what is near, expressed as distance, time, or cost of traveling to or from that location. Of those options, mapping travel is most precise. You should also be aware of whether you’ll need to take into account the Earth’s curvature (geodesic method) or not (planar method). Information needed to map what is nearby should be a list (ex, a parcel ID and address), a count (by category), or a summary statistic (total amount, total/category, or a statistical summary). Distance and cost ranges can either be an inclusive ring, which is a circular area, or distinct bands, which are essentially multiple inclusive rings stacked on top of each other. There are three ways to find what’s nearby: 

1. Straight-line distance: Specify the source feature and distance, and GIS locates the area or features nearby
2. Distance or cost over a network: GIS finds segments within range or specified source locations and a distance or cost within each linear feature
3. Cost over a surface: GIS creates a new layer showing travel cost based on a specified location of the source features and a travel cost

Straight-line distance can be used by creating a buffer defining a boundary and what’s inside it, selecting features to find features within a distance, calculating feature-feature distance, or by creating a distance surface. The equation to find distance is as follows: square root of (x1 – x2)^2 + (y1 – y2)^2. To create a buffer, specify the source feature and the buffer distance, and GIS will draw a line around a certain distance from the feature.

Chapter 1: Introducing GIS Analysis

GIS is a powerful tool for analyzing and visualizing data. It is used to map where things are, show concentrations (most/least), analyze density, finding what’s inside a specific area, and track changes over time. At the core of GIS analysis is the process of asking questions, selecting appropriate methods based on available and required data, processing that data, and interpreting the results in the form of maps, tables, or charts. 

GIS data comes in several forms. Features can be discrete, meaning their locations can be pinpointed, or continuous, which can be measured anywhere. Features can also be summarized by area. These features are represented using either vector (coordinates) or raster (layers) data. The accuracy of representation depends on map projections (globally) and coordinate systems (specified area). 

Each geographic feature in GIS has attribute values, which describe its characteristics. These values are classified into types such as categories, ranks, counts, amounts, and ratios (proportions and densities). This classification helps in selecting the appropriate analysis technique. Ultimately, GIS allows users to reveal spatial patterns and relationships that may not be obvious, making it an essential tool for decision-making in a wide range of fields. 

Chapter 2: Mapping Where Things Are

Mapping where things are is a foundational function of GIS that helps identify geographic patterns and relationships. Before creating a map, it is crucial to decide what information needs to be shown and why. GIS analysis allows users to pinpoint where features exist or where they don’t, identify their types, and determine their distribution. However, map design should balance detail and clarity, where too much detail can overwhelm viewers, while too little might leave out crucial data. 

The first step in preparing data is assigning geographic coordinates or addresses to features. Each feature must also be assigned a category value that identifies its type. When making maps, there are many different approaches. You can map a single type using the same symbol, focus on a subset of features, or map by category, using distinct symbols for different types. If features belong to multiple categories, it’s important to visually distinguish each group, but it is suggested not to use more than 7 categories on one map. If more than 7 categories are needed, they should be grouped to avoid clutter. 

Choosing symbols is essential for clear communication. Individual locations can be shown using color coded markers, linear features can vary in width or pattern, and areas may be differentiated using raster layers or shading. Text labels can also help in identifying areas. Including reference features like roads, rivers, or landmarks adds context, which can make a map more meaningful to the audience. 

When analyzing geographic patterns, zooming out can help identify broader trends. Combining spatial patternswith background knowledge often reveals why features are arranged in a certain way. Well designed maps, supported by prepared and categorized data, allow GIS users to communicate spatial relationships effectively to an audience.

Chapter 3: Mapping the Most and Least

Mapping the most and least is a method in GIS used to explore how quantities vary across locations. This approach allows users to see relationships between places, revealing patterns not visible in raw data. This technique is especially useful for comparing counts, amounts, ratios, ranks, and densities across geographical areas. When mapping quantities, it is crucial to consider the audience, whether the map is exploratory or intended for presentation, influences the choice of using data or visual maps.

Understanding quantities is important. Counts represent the actual number of features, while amounts are total values associated with features. Ratios compare two values, while proportions and averages divide values to show relationships. Densities show distribution over space. Ranks order features from high to low, either through text (high, medium, low) or scales (1-10). 

These quantities are often grouped into classes to make patterns easier to interpret. Creating classes of data helps readers compare areas more quickly, though this can reduce the precision of the data. There are several classification methods:

• Natural breaks (Jenks): classes are based on natural groupings of data values
• Quantile: Each class contains an equal number of features
• Equal interval: the difference between high and low values is the same
• Standard deviation: features are placed in classes based on how far away from the mean they are

When making a map, various visualization techniques can be used:

• Graduated symbols

• • • Features: locations, lines, areas
    • Values: counts/amounts, ratios, ranks

• Graduated colors

• • • Features: areas, continuous phenomena
    • Values: ratios, ranks

• Charts

• • • Features: locations, areas
    • Values: counts/amounts, ratios

• Contours

• • • Features: continuous phenomena
    • Values: amounts, ratios

• 3D perspective views

• • Features: continuous phenomena, locations, areas
  • Values: counts/amounts, ratios

Using these tools, GIS helps reveal spatial patterns in quantitative data.

Hi all, my name is Luke Miller. I am currently a junior majoring in environmental science with a minor in Spanish, and I also play lacrosse. 

From the syllabus quiz and reading, I learned that the uses of GIS are vast and varied, being used for environmental, geological, economic, and even medical purposes. Along with this, GIS does not have its own fixed and secure identity, as the user determines its value and how they choose to use it, even within specific fields. For example, a marine biologist might use GIS for completely different reasons than a wildlife biologist. Another variance in the uses of GIS is that it deals with both the “why?” and “how?”, and can be used in different ways based on what the user seeks to answer or find. I enjoyed reading about the first use of GIS, which was to optimize the construction of a highway in a way that would minimally interfere with the environment, which made me wonder what percentage of highways in the US were built using GIS, as I find many of them to be inconvenient. Another concept I learned about was that spatial analysis is different from mapping, as it extracts more data from preexisting data, whereas mapping is just a presentation of existing data. I was also intrigued by the idea that people “reason” using imagery and are able to better understand spatial analysis using imagery. It is for this reason that GIS has become increasingly popular over the years, in that its applications make complex relationships easier to understand and visualize. Finally, I found Bruno Latour’s concept to be interesting in that scientific knowledge and technology must first be disputed to become legitimate. Once they are legitimized, they are then assumed to always be true. This is true with GIS, in that because it has become a legitimized technology, no one questions the validity of its findings, which is both a good and bad thing. 

 

I found the application of GIS to assess the water quality of lakes in my home state of Minnesota to be interesting. In a 2002 study, researchers took existing maps of lakes over a 25-year period and used GIS to correlate them with information about the surrounding areas, such as pollution, to determine the most prevalent causes of water pollution in that specific area. 

Another use of GIS I found to be interesting was a study conducted in 2009 in Indiana. This study investigated the prevalence of ticks, which are the main cause of Lyme disease, found on deer harvested from 2005-2007. All deer in this study were entered into a GIS database to find where deer ticks were most prevalent. 

 

Sources

Brezonik, P. L., Kloiber, S. M., Olmanson, L. G., & Bauer, M. E. (2002, May). Satellite and GIS Tools to Assess Lake Quality. Water Resources Center; The University of Minnesota. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=7c6ca4d3d77ef0b0417e3f8304f1e66427809323

Keefe, L. M., Moro, M. H., Vinasco, J., Hill, C., Wu, C. C., & Raizman, E. A. (2009). The Use of Harvested White-Tailed Deer (Odocoileus virginianus) and Geographic Information System (GIS) Methods to Characterize Distribution and Locate Spatial Clusters ofBorrelia burgdorferiand Its VectorIxodes scapularisin Indiana. Vector-Borne and Zoonotic Diseases, 9(6), 671–680. https://doi.org/10.1089/vbz.2008.0162