Skidmore Week 2

Chapter 1:
Since the book was published the use and abilities of GIS have grown, from making maps to analysis that can solve problems. GIS analysis is finding patterns within your data, starting with a question. To decide what method to use to obtain your data you will need to understand the data you are working with. To understand GIS analysis you need to understand the geographic features you will be working with; discrete, continuous phenomena, or summarized. Discrete locations can be pinpointed with their exact location. Continuous blanket the entire area of your map, these can either be continuous or non-continuous data. If the data is non-continuous the software will use interpolation to fill in the gaps. Summarized data represent data within a given area, not specific points given as the density of individual features. These features can be shown using vectors which are defined as a location within a table that can be connected to create areas or a raster model which uses a matrix of cells that can be increased or decreased in size. A map projection system converts data from global or rounded data to a flat or 2D plain so it will distort some features. To do the analysis you need to separate your data into categories, ranks, counts and amounts, or ratios. When working with data for a GIS analysis you will need to create tables for your information these come as selecting, calculating, and summarizing.

Chapter 2:
When doing GIS analysis mapping is a large part of the work because it allows you to see patterns forming. Knowing your audience is important when deciding how complex a map should be or the data that is being mapped. Before creating your map you will need to assign values to your data for their location and code on the map. Creating the map requires you to tell GIS what feature you want to be present in the final product. Often used in this process is single-type mapping where the data is only shown by one symbol on the map. Once you have done the above steps GIS then takes the data and symbols with the geographical location and creates a map that matches your inputs. Instead of having all data present on one layer, you can create subsets so that there are categories within that correspond with different symbols. When creating different categories you can also change the size of different symbols based on key values within your table. If you are doing this though you will want to keep in mind your audience because most people cannot distinguish more than 7 different categories. One key thing is included features that are recognizable for people unfamiliar with the area such as street names. Even if you have found a pattern in your map you still need to find if it is statically backed.

Chapter 3:
When mapping man people want to find the most and the least amount found in the patterns. To decide how to map these you will need to know whether your data is continuous or non-continuous. To understand your data you will want to keep in mind your audience and whether you are presenting the data. If you want to find a deeper analysis you will have to explore your data further than the patterns on the map. Counts show how many of a thing are found on a map whereas amounts are the value associated with each item. Ratios are used to smooth out data and are useful when summarizing data within an area. Ranks are used when the mapped data is hard to quantify and show relative values. Once you understand the data you are working with you will need to assign values and symbols to the items which come with the trade-off of showing things accurately or generalizing. Individual items when mapping lead to complex data that is hard to read for certain audiences, whereas creating classes generalizes that data allowing for an easy analysis for some. When creating classes you can either do it yourself which is normally for more specific data or use previous breaks mentioned these include, natural breaks, quantile, equal interval, or standard deviation. To decide on which of these methods you need to use you will typically create a bar chart to see how the data is distributed across the x-plain. In doing these methods outliers may be found that can cause a problem with your analysis these can be a problem with your data set, wrong, or involved. The final step in creating classes is how many are you going to include and how will you show these on your map. If you have properly done the correct method in separating them this step should be easy and some GIS software will automatically make the classes continuous. Now that you have quantified your data you will need to map it, the normal reaction is to make the mapped complex but that should not happen. Graduated symbols are typically used for volumes or rank in linear systems, whereas graduated colors are mapped to show continuous data within the specified area. Charts are used typically for a quick study of the area and not complex data. Contour lines show the rate of change from one set of data to the next and are typically used for spatial data. The most complex form of this data visualization is the 3D form which is typically used to allow the audience to better visualize the surface of the data. The most important part of 3D data is the viewer location since larger sets of data will block the view of other data and allow for worse analysis. The next two z-factor which exaggerate the data for easier visual separation of data and the light source which is the location of the source help the audience better view the patterns.

Chapter 4:
Mapping density rather than individual locations better allows you to see concentrations. You can either map points and lines or summarized data with density maps. You can either map density features such as businesses or feature locations such as employees per location. Density defined by area is where you map each location then divide and then summarize the data within a given polygon size to create density or by a surface that uses a raster model to create density per cell. A dot density map is a method of density defined by are but rather than a shaded color within an area, it uses dots to represent your data which better allows for a generalized area to be more accurately shown specifically population density. In a raster model cell size is the most important decision and that does not change is density surface. Along with cell size, another thing to keep in mind is the search radius which defines how many features will be calculated within each cell. There are two types of calculations used in the GIS simple method which uses rings around the cell and the weighted method which uses a mathematical function. Areal units are what define the legend of the map rather than cell units. When graphing density surface it can either be shown in graduated colors or contours.