Munroe – Week 2

Chapter 1

Chapter 1 focuses on understanding GIS analysis and better framing data to fit the needs of your map. This chapter’s structure gives us a bottom-up approach to GIS, starting with the basis of geographic features, as this shows us how our data will be represented. Mitchell talks through a few examples, such as discrete features, continuous phenomena, and features summarized by area. Further, he talks about the difference between vector and raster models. The vector model centers feature as “a row in a table, and feature shapes defined by x,y locations in space” and areas defined by borders represented as closed polygons. The raster model is different, displaying features as “a matrix of cells in continuous space.” Any part can be displayed using either model, but it’s important to be conscious of which will be more visually appealing to the viewer. Discrete features and data summarized by area as usually represented with the vector model, while continuous numeric values are defined using the raster model. Endless categories can be represented by either the raster or vector model. I’m already pretty familiar with coordinate systems, with experience from GEOG112, so this section was not of great need. Mitchell finishes the chapter by talking about understanding geographic attributes, specifically attribute values. He lists categories, ranks, counts, amounts, and ratios. Types are used to group similar things and can be represented using numeric codes or texts. Classes put the features in relative order when direct measures are complex. Counts and amounts are used to show total numbers, while ratios establish relationships between quantities, usually resulting in a percentage. This is all data-driven and extremely important, as maps are just products of data. Mitchell finishes the chapter by talking about data tables, which helps us understand how to convey selected attribute values properly. 

Chapter 2

Chapter 2 begins by mentioning the means by which you’re making your map. This reminds me once again of GEOG112 and KryKrygier’smic book regarding the information and audience your map needs to have to be successful and engaging. Mitchell progresses to the basics of making your map, starting with mapping a single type, where you draw all features using the same symbol. Then, he moves on to mapping by category, where you draw features using a different symbol for each category value. Mitchell makes a point that when choosing how many categories to project, it’it’sportant to look at the visual appeal of each map with its scale to judge which would be better for your audience. If you have over seven categories, it may be useful to summarize certain categories to fit together, as to not distract the audience or deter away from the meaning of the map. If youyou’reing symbols to display categories, it’it’sso important to prioritize colors over symbols, as colors are more effective to be visualized and grouped together. For example, when showing maps that have distinct categories like soil and geology maps, combining the projected features with their prospective color to a two-or-three letter code can help the viewer better see the projection, even with an included table showing the values. The same goes for implementing reference features into the map. The ultimate goal with mapping information clearly is for the viewer to recognize and establish patterns. This helps prove the data that youyou’vepped and shows that youyou’vepped something meaningful and necessary. It should be clear and evident with what youyou’reying to prove to the audience. My goal throughout this course when using ArcMap will be centered around this point, as I want my work to be successful, evident and clear with useful information being pulled from the data visualized on the map.

Chapter 3

Chapter 3 is focused on quantitative data, as mapping the most and the least helps us compare relationships between places. This helps show where help, intervention or policy is needed or of least concern. One way to do this is by shading. The darker an area, the higher the quantity of data is being reported from that location. To do this, data is summarized, usually using ratios, and set into categories with the highest percentage being darker in value versus the lowest being lighter in value. Mitchell speaks specifically on the purpose of the map, begging the question if youyou’reploring the data or presenting a map. If youyou’reploring data, youyou’retively looking for patterns and relationships versus presenting a map where you already know the pattern and relationship youyou’reying to prove. Keeping this in mind will help you build and promote a map of true purpose. Mitchell then dives back to the first chapter, recapping on quantitative data being interpreted as counts and amounts, ratios or percentages, each specific in their own characteristics and useful for differing scenarios. The chapter then turns to creating classes, specifically how to group your data to represent values accurately and efficiently. Once youyou’veeated classes and a corresponding legend, choosing an appropriate color scheme is necessary as it will help put the spotlight on your data. As mentioned before, higher percentages being darker and lower percentages being lighter is a recommended option. Mitchell mentions natural breaks, quantile, equal interval and standard deviation. Further, he mentions the different options to show quantities like graduated symbols, graduated colors, charts, contours and 3D perspective views, each is very specific and Mitchell dives into each to show the accurate ways of using them to show data. It’It’sportant not to go overboard, though, because you still want the viewer to be able to comprehend the data easily and come away from the map with the accurate interpretation and information.

Chapter 4

Chapter 4 turns to mapping density, in particular how to show where certain objects or data are concentrated which is great for census tracts or counties varying in sizes. Mitchell recommends starting with the question “Do”you want to map features or feature values?” D”nsity of features uses the example of locations of business, versus the features values which has an example of number of employees at each business location. The density will obviously shift, with more workers at certain locations and more businesses in another location. Because you want your map to be easy to comprehend, it’it’sportant to ask this question before beginning the process of making your map. Moving along, if you map by defined area, you create a shaded density map with area boundaries. If you choose to map by density surface, you create a map that almost looks like a weather radar, with density sprawling over area boundaries. To create these calculations, you first have to define and create categories. Relying on information from chapters 2 and 3, you can extrapolate your data to fit your tables and then take those quantities and create corresponding classes, specifically with a graduated color scheme. You can also create a density surface using GIS, where GIS calculates a density value for each cell in the layer which shows where point of line features are concentrated. To do this, you need information about cell size, search radius, calculation method and units. The cell size determines how coarse or fine the patterns will appear, while the search radius will construct how generalized the patterns in the density surface will be. There are two calculation methods you can use, the first being simple which counts only those features within the search radius of each cell while the weighted method uses a mathematical function to give more importance to features closer to the center of the cell and units will let you specify the areal units in which you want the density values calculated. You can also imply contours, but that makes the map more rigid and helps show the values of the legend easier. After completion, the density surface will replicate a weather radar map and can help the viewer find where the selected data is more likely to be found.