White Week 2

Chapter 1). 

GIS analysis is a process for investigating geographic patterns in data and interpreting the relationships between associated features. At the core of GIS analysis is a similar starting point to analysis in all fields. The book says the first step is to frame a question. In scientific research, we have research questions. Really when doing any sort of report, essay, project, we have a research question. In politics and government I learned a lot about framing which is the gathering or presentation of information under a specific context in an effort to dictate how it is understood. I think the critical point about this step is to develop your question in as much specificity as possible, that way you have a direct approach to the analysis, a concrete method to go about, and a particular plan for presenting the results. This is something we have learned generally here at OWU overall in terms of limiting the generality of a research question and instead making it as precise as possible. Another super important thing to consider at this stage is the target audience and the context of usage. For the understanding your data stage, I think that it is important to recognize that this goes beyond just the header of knowing your features but also includes the capacity to build new data from existing sets. This taps into the fact that GIS can be used not only to analyze current geographic patterns and data, but also to construct new ones. For the methods, a key point is that how the data will be used fundamentally influences how you obtain and formulate that data. Once a method is chosen, I think it is super helpful to know that you can compare and contrast different analyses in order to proceed with the information that is most fitting in terms of presentation and accuracy at large. I think these preliminary steps and considerations will be exceptionally useful in making the process overall more straightforward. Moving on to geographic features I think that the distinction between continuous and discrete features is significant in that discrete features represent an actual value and a specific location like businesses represented by the number of employees. On the other hand, continuous variables have a range and can be measured at any given location and encompass the entire mapping space like temperature. In context, there may be a business with a large number of employees and then an area with no business at all. And so with these discrete features there are gaps involved. Something important to remember with continuous information is that it can be spaced regularly or irregularly. For example atmospheric pressure readings for environmental monitoring are recorded at the same time every hour per se and so there are these set intervals at play. Continuous data can also be irregularly spaced which essentially means there is no uniform interval of spacing/measurement involved. I was a bit confused by the term interpolation but from my understanding it uses discrete data and known points to approximate values for potentially unknown locations involved with continuous data. The point then is to formulate a continuous mapping space which can be essential for mapping some continuous phenomena. Another main distinction I took away from the discussion of continuous and discrete data is that boundaries are modeled and interpreted differently showing degrees of similarity for continuous data and showing legal borders if you will for discrete data. While features can also be summarized by area, oftentimes data comes summarized by area (data found within set boundaries). I think it’s cool that we can perform basic statistics to summarize any additional data by area, then merging the data tables and mapping to identify connections. Moving on to representing geographic features, in my head I aim to think of the vector model as the x, y coordinate model. This is based on the rows of data tables. Locations get coordinates, lines get coordinate pairs and areas get borders. The raster model contains features that are shown by cells spaced or layered across the map in a continuous space. Discrete features and data summarized by are are generally modeled by vector. Continuous variables are modeled by vector and raster but continuous numerical values are shown using raster modeling. Due to the map projection translation process, the distortion of features is something to consider when mapping larger areas. For the types of attribute values a cool thing I learned is that we can assign ranks based off of other attribute features. Rasta modeling comes into play here for this multi criteria ranking and multi layer data mapping. Ranks put features in order when values are hard to quantity like if I want to look at the scenic or recreational value of a body of water through a city. The main point for counts and amounts is that a count is the total number of forests on a map whereas an amount can be the number of trees within a forest. Ratios are good for showing evenness in terms of the distribution of features. The number of people in an area divided by the number of households is the average number of people per household. Categories and ranks are discrete whereas counts, amounts, and ratios are continuous. For doing the selecting, calculating, and summarizing components of working with data tables, I think I get most of it, I will just definitely need some hands-on practice to make sure I do.  

Question:

Is there a way to manage the distortion of features when mapping larger areas or is it just something to consider when evaluating the map and when presenting?

Chapter 2).

Mapping helps us understand where things are but also much more. Through the patterns of placement that can be devised, we gain insight on why things are where they are. In this sense, it’s more beneficial to look at the distribution of features, the full story rather than individual features or the single story. Like we learned from Schuurman, GIS is used by many different types of people for a vast range of purposes and mapping where things are can serve a totally different role for a police officer than for an ecologist. When thinking about what to map, it is helpful to use symbol types based on what features you are looking at and how the map of those features will be utilized. We can map different types of features to see if there is any overlap. Information depth can vary based on the audience the map is being shown to and the medium through which the map will be presented. When preparing the data to be mapped the assigning of geographic coordinates is essential. Data from any GIS database already has assigned coordinates but if we bring data in from any outside source then we must include a street name or latiutude/longitude to register with GIS to internalize and dispel coordinates for us. Major types and subtypes of features can be obtained from already stored information or created by adding a category in the data table. When actually making the map we can map single types of features or show multiple features by category values. Single type features get the same symbol when mapped which often does still reveal patterns. We can map all features or a subset of features to seek more intricate patterns for individual locations. A main point I got here is that it is good to show all types but if you want to do a subset then just highlight that and make the other types a lighter color shade. Another tip I took away is that using different colors or symbols for each category value of the feature is good for displaying the hierarchy of features and being able to distinguish the types of features. Features can also belong to more than one category and we can show that. There are burglaries overall, then types of burglaries, but also things like the type of buildings entered for a burglary. NO MORE than 7 categories, break it up and do a side by side evaluation. When grouping categories we can assign one record a code for its general category and a code for its detailed category in the database. For locations, use colors to distinguish categories and for linear features use different widths or patterns or lines. Displaying reference features like landmarks or major waterways can be helpful for serving a representative audience in terms of being able to recognize and relate to the map. I learned that a useful tool is to choose simple monochrome base maps of ArcGIS for this mapping reference features component. In terms of analyzing geographic patterns, scale has a big impact and so zooming in and out may be needed. Clustered, uniform, and random are three core types of distributions to look out for. 

Question:

For our work in this class, will base maps be used most of the time, sometimes, or will we always have to include reference features?

Will there be cases where we are obligated to bring in data from outside sources, not GIS data bases, then having to give GIS a basis to formulate coordinates for us? Or will we mostly be dealing with data from ArcGIS?

Chapter 3).

Mapping features based on a quantity associated with each feature adds an additional layer of helpful information. This is essential for thinking about these overarching goals of finding places that align with what we are looking for or identifying relationships between places. Similar to the example the books describes, I thought of one in that mapping crime based on where crime has occurred gives us an understanding of crime overall but mapping crime based on the number of crimes committed at each location give a much more accurate depiction of the levels and frequency of crime. If crime has occurred once or twice in one area, but has occurred 100 times in another area, those details on where crime is concentrated is much more useful.  To represent quantity, location and linear features are represented with graduated symbols while areas are typically shaded to show quantities. Continuous features as defined areas can be shown through graduated colors while continuous surfaces are shown using graduated colors, contours, or a 3-D view. Examples of areas can be zipcodes or watersheds. In terms of further understanding quantities a count is the number of people in a census tract whereas an amount is the number of 30-45 year olds per se. When summarizing an area, it is best to use ratios as areas differ in size and using ratios will best represent the distribution of features. We also need to be aware that we are working with the right ratio. Average is the most common type of ratio and best fitted when comparing areas with a disproportionate amount of features. A little reminder is that when calculating averages we divide quantities that use different measures and when doing proportions we use the same measures. Density as another type of ratio is used to show concentration of features calculated by dicing a value by the area of the feature to get a value per unit of area. When mapping quantities there is this overarching tradeoff that exists in between displaying the data values most accurately and generalizing them to visualize patterns. Counts, amounts, and ratios are typically generalized into classes. The four most common standard classification schemes are natural breaks, quantile, equal interval, and standard deviation. A good tool to use is to plot the values on a chart to understand the distribution and then select a classification scheme. I am a bit confused on how to do these classification schemes and the making of the charts to figure out distributions but I think with some practice that will be fine. I get the general idea of each classification scheme but it will definitely take some practice to be able to work through these. When working through this, it is good to remember to use natural breaks for uneven data and for even data use equal interval or standard deviation. Use of quantile shows relative differences between features. Something I took away as a reminder when juggling all of this is that ArcGIS allows us to easily and quickly change classes, symbols, and so forth. This is helpful when trying to explore the data and seek out patterns. Another pointer is to be aware of outliers that can either be eros is the data set or abnormalities from a small data size. Outliers can be marked as insufficient data as a last resort. When managing the number of classes, changing this will bring out patterns more or make them less clear. In order to make the map most understandable and readable, we can work with the legend and round out min/max values. We may also have to manually go in and edit the class values once the GIS has defined them for us. This goes especially for natural breaks classifications. We can also change the numbed values to high or low if there are meaningless decimals making the map harder to read. When making maps, keep them simple and show only information that effectively displays the patterns. When using graduated colors use darker shades to indicate higher class values. When using graduated symbols the main takeaway is to use symbols that show patterns without obscuring feature locations. I think using charts is hard to read and graduated colors are easier to read and show the details. Employing graduate charts makes this a bit easier to read and shows the relative sizes of each feature. I like this a bit more. For 3D perspective views, I am a bit confused on how to combine the z factor with light source. There is a lot of description on how this is done but I think it will be helpful to see it done or try to actually do it. 

Question:

Do we need to know the internal operations of GIS when performing certain processes that give us the classifications schemes or values that we are looking for? There is some discussion on what the GIS is doing in detail and so I am wondering if that is something we need to understand or pay attention to.