Author: ajlashway

Here is my work from the chapters this week:

Week 5 Write-Up

And then the Delaware Data information:

Week 5 – Delaware Data Inventory

On to the final! Good luck everyone

Taking this idea from Savannah and linking a pdf with all of my work from those first 5 chapters.

Lashway Week 4 Write-Up

Chapter 5

Notes:

You can use an area boundary to define the features inside. These can be created on top of features, can be used to select features inside the area/summarize selected features, and combine the area boundary and features in order to create summary data.

Single areas can be sectioned off to let you monitor activity or summarize information. For example, a stream buffer that is off-limits for logging. Then there are multiple areas, that can compare what’s within several different areas in a contiguous fashion. Examples of these contiguous areas are zip codes and watersheds.

You can change what you’re analyzing using different feature attributes (as discussed in previous chapters). Sometimes features will bleed out of the area; there are a couple different ways to deal with this. You can only include features fully contained, include features that partially extend outside (would use counts), or include only portions that are inside of the area (would use amounts). This decision all depends on what you’re mapping and the level of precision required.

 

Vectors are typically used with continuous data and can result in slivers, which can be smoothed out with the GIS. You need to keep in mind the extent of the data, the degree of accuracy you’re dealing with, and only have very small slivers removed automatically. Anything slightly bigger should be removed manually to ensure that important data isn’t lost.

Vector is more precise, but requires more time and processing power; it requires the summarization of category values in the final table. Raster is more efficient, but can be less accurate. The accuracy will depend on the cell size, and slivers can still be created using raster.

 

Definitions:

• Frequency– the number of features with a given value or within a range of values inside the area.
  • Represented with a bar chart or pie chart.
• Sum– overall total or total by category.

 

Chapter 6

Notes:

You can use GIS to find out what’s nearby and how that’s relevant to the data set and audience you’re creating a map for. When dealing with distance, you must define “closeness,” as it’s very subjective. You need to quantify what is “near” and what is “far.”.

Buffers can be used to give features more definition. They can be used to add a literal buffer along stream banks to forbid logging, or just to simplify complicated data sets. Network layers connect edges through the GIS to allow different usages of distance and cost, and can be used in conjunction with buffers.

 

Definitions:

• Travel costs– the effort or other detriment associated with one path/area over another.
• Planar method– calculating distance assuming the surface of the earth is flat.
  • Used for short distances or small areas (county, city).
• Geodesic method– taking into account the curvature of the earth.
  • Used for long distances (continent, earth as a whole).
• Inclusive rings– bands of data ranges used to see relative changes at varying scales.
• Distinct bands– for comparing distance with other characteristics.
• Straight-line Distance– specify the source feature and distance, then uthe GIS finds the area or surrounding features.
  • Primarily used to create boundaries.
• Distance or Cost Over Network– specify source locations and a distance or travel cost along each linear feature.
  • Used to find what’s within travel distance or cost over a fixed network.
• Cost Over a Surface– specify location of source features and travel cost, and creates a new layer showing the travel cost from each feature.
  • It calculates the overland travel cost.

 

Chapter 7

Notes:

Maps can also be made to change in order to document past conditions and/or predict future events. You can go date by date, or hop between a certain/set period of time in a pattern (every two days, every other month, every 3 hours). Make sure to keep note of how exactly time is changing and its relationship with the feature(s).

Time patterns can be used to track movements over time. You can use lines between points to better emphasize findings as well. The distance between points can represent various speeds. For example, two dots that are closer together show a slower amount of movement of a hurricane over a 3-hour period than dots that are further apart after the same amount of time has passed.

Coloration and shading to emphasize change with continuous features. Equal time intervals being used for each feature is critical to seeing an accurate rate of change. Events mapped over time typically use color grades that represent different (but equal in length) time periods. If there are several events reoccurring at the same locations, you can use pie chart markers in place of simple dots.

 

Definitions:

• Change in Location– see how features behave so you can predict where they’ll go.
  • Ex; bird migrations, hurricanes
• Change in Character or Magnitude– shows how conditions in a given location have changed.
  • Ex; land cover change in a watershed
• Travel– change between two or more dates or times.
• Before & After– conditions preceding and following an event.
• Cycle– change over a reoccurring time period.
  • Ex; day, month, year

Chapter 1

Notes:

Map projection will be dependent on the scale of data, level of precision required, and where the data is located.

Definitions:

• Discrete data– points or lines in space where a given feature is either there, or isn’t; there are ‘gaps’ in the map. Typically uses a vector model.

• ex; streams, parcels of land, businesses

• Continuous data– data covers the entire map, and you can determine the value for any given point. These are typically numeric values in raster, but can also be mapped using vector.
  • ex; temperature/heat maps, precipitation, soil type
• Summarized data–  a given value applies to an entire area, not a specific location. Typically uses a vector model.
  • ex; number of businesses in a zip code, total length of streams in a watershed.
• Vector model– features are shapes defined by “x, y” locations in space.
  • Can be discrete locations, events, lines, or areas.
  • Uses geographic coordinates (x, y).
  • Lines are a series of coordinate pairs.
  • Areas are closed polygons.
• Raster model– features are a matrix of cells in continuous space.
  • Consists of multiple layers (typically), with each layer representing one attribute.
  • Can use varying cell size (examples on page 11).
    • Small cell sizes result in a more defined map, but requires more storage space. Large cell sizes will show patterns, but they lose the level of detail achieved with smaller sizes.
• Attribute values– identify what the feature is, describe it, or represent some magnitude associated with the feature.
  • Types: categories, ranks, counts, amounts, ratios
• Categories– groups of similar things
  • ex; roads: freeways, highways, local roads
  • ex; crimes: burglaries, thefts, assaults
• Ranks– put features in order from high to low. Most often used when direct measurements are difficult, or if the quantity represents a combination of features.
  • ex; “scenic value” of rivers; area in mountain gorge ranks higher than area near a dairy farm
  • You can rank based on different attribute values
    • ex; soils of a certain type ranked the same in relation to suitability for growing a particular crop.
• Counts & Amounts– shows you total numbers. Count is the actual number of features. Amount can be any quantity associated with the feature.
  • ex; amount: number of employees at a given business
  • They let you see the actual value of each feature as well as its magnitude compared with other features.
• Ratios– shows the relationship between 2 quantities, created by dividing 1 quantity by another for each feature. They more accurately show the distribution of features.
  • ex; dividing (# of people in each tract)/(# of households)=(average # of people/household)
  • Proportions– show what part of a total each value is.
    • ex; number of 18-30 year olds/total population
    • They are often shown as percentages
  • Densities– show the distribution of features or values per unit area
    • ex; population of county/land area in miles squared= people/square mile
• Selecting– used to specify features to work with, or to assign new attribute values to specific features.
  • select ATTRIBUTE = VALUE
  • Can also use (>), (<), and unequal (<>)
• Calculating– used to assign NEW values to features in the data table.
  • select FIELD = VALUE → calculate ATTRIBUTE = VALUE
• Summarizing– [summarize] the values for specific attributes to get statistics.
  • ex; create a new table → list a value for each type → add count of features

Questions:

Would the census population data from GEOG 112 be considered summarized data?

Why would you use a rank based on an attribute rather than just using the secondary attribute?

Chapter 2

Notes:

The amount of information shown on a particular map depends on what the map will be used for. You need to know the intended audience for the map and its purpose before starting, and plan accordingly.

The category values discussed in the previous chapter may have subtypes that add varying levels of detail. The same base map can then be expanded upon, depending on its purpose and the intended audience at the moment.

Even if you’re intending on focusing on a certain set of data, having surrounded data can help to contextualize the information and resulting patterns. If the data is discrete, showing these data sets on separate maps may make information more digestible. If the data is continuous, displaying all or a couple of categories on the same map is favorable in many cases. When it comes to categories and how many should be displayed, 7 is a good rule of thumb for a maximum. However, the distribution of features and scale of the map can affect this. You can display more features if they’re scattered than if they’re clustered together.

So, it’s good to experiment with how many categories are being displayed. Getting another set of eyes that aren’t familiar with the data set is probably crucial to ensure the map is understandable. This is also a good way of figuring out how the data is being perceived by the reader. Depending on how categories are grouped, that perception can change dramatically.

Definitions: 

• Single type– when the same symbol is used for all features.
• Reference features– landmarks/locations that can be used to ground a map in a certain area, and convey more meaning to the reader.
  • ex; major roads, locations of cities/towns, stores
  • They should be mapped in light colors or greys to avoid dominating the map.

Chapter 3

Notes:

Mapping based on quantities can give additional context that can give a better picture of what’s being represented. Again, knowing the purpose of the map being created will tell you how to make it and whether quantities will be beneficial.

Discrete data uses graduated symbols or shaded areas, while continuous data uses graduated colors, contours, or 3D perspective views.

When mapping based on quantities, you will want to start off with the basic data set and figure out what patterns are present. Then, make a map that helps highlight these patterns. Each feature included in the data set should only be incorporated in a way that best represents the data.

Definitions:

• Quantities– a data set/set of points that have variation amongst the features.
  • These can be counts or amounts, ratios, or ranks
• Class– a grouping of a range of similar data, typically used when features all (or mostly) have different values, and the data range is large. Classes will make it easier to identify patterns.
  • Natural Breaks– natural groupings of data values present in the individual sets.
  • Quantile– each class contains an equal number of features.
  • Equal Interval– the difference between the high and low values of each class is the same.
  • Standard Deviation– features are broken into classes based on how much their values vary from the mean.

Chapter 4

Notes:

Density mapping is helpful in cases where there are many features. It will be easier to read in some cases than individual points representing each feature. You’ll have to decide two major things: 1) whether to shade defined areas, or create a continuous density surface and 2) decide if you’re focusing on features themselves or on values associated with features.

In general, summarizing data with map density can make patterns more general, but easier to look at and identify specific numbers for overall areas. Map density should be used for already summarized data with defined borders. Density surfaces provide the most detail, but require the most effort by far to put together. These are best for concentrated data.

The level of specificity in a data set/range of area can greatly affect what the resulting map looks like. In density surface mapping, areas between features are estimated through interpolation. Interpolation can cause extreme highs and lows to vanish. So, while patterns are easier to see, there should be another map that shows locations of features to provide context.

Definitions:

• Density– used to show where the highest concentration of features is.

Hi! My name is AJ Lashway, I’m a senior Zoology, Environmental Science, and English (Creative Writing) major. I’m living in BMH this year with a couple friends as well as my cat Jesper! I’m taking this class to try and better understand GIS since it keeps on coming up in my classes and jobs that I’m looking into for after school. I work for the athletic department, so if you go to any field hockey, soccer, or lacrosse games you’ll most likely be hearing my voice over the speakers for announcements 🙂

The Schuurman reading was very interesting, I had no idea there were/are essentially two different factors in GIS (GISystems versus GIScience). The argument as to whether the program is meant to just plot the data or be used to further analyze was very interesting. The reluctance to switch from hand-drawn cartography was surprising as well. With how far technology has come, it seems obvious to let a program take care of all of the monotonous work, but the manpower originally needed to work programs like GIS must’ve made it seem like more trouble than it was worth.

I found an article using GIS to visualize the distribution of native versus invasive species of fishes in the US. By using GIS, they were able to clearly pick out the fact that non-native fish tend to cluster closer to the east and west coast, while natives are primarily in the midwest. This would’ve been more difficult to conceptualize had they just been looking at a table full of numbers.

(Holcombe, T., Stohlgren, T. J., & Jarnevich, C. (2007). Invasive species management and research using GIS.)

GIS is also often applied in urban/city management. The City of Delaware uses CityWorks, which is a program that utilizes GIS in order to view a myriad of projects across the city. This includes access to all of the storm drain information (inlets and outlets), hydrant status, as well as the locations of any active work. Many cities are moving towards developing maps and models that display a broad range of demographics, rather than just basic aspects involved in planning (Hamilton et al., 2005).