Data Classification

This week, our lab assignment centered on implementing and assessing a variety of data classification methods. This involves taking a data set - in this particular case the percentage of population in each US census tract in Escambia County, Florida that was reported as black in the 2000 census - and determining the best way to divide the range of data values into categories that can be symbolized on a map. Four different classifications were used.

• Equal Interval classification simply divides the entire range of values from minimum to maximum into identically sized sub-ranges without regard to how the data is clustered along the number line.
• Standard Deviation classification bases its classes on the standard deviation from the mean value of the data.
• Natural Breaks classification uses an algorithm to find an 'ideal' set of ranges that respects how the data is aggregated along the number line.
• Quantile classification creates sub-ranges that each contain the same number of values in each, or at least as close as possible based on how well the number of sub-ranges factors into the number of values. In this case, number of classifications was five, so it is referred to on the map as Quintile.

Two maps were created. The first displays all four of these data classification methods in separate maps for comparison purposes. The second map only features one map that uses the best option for classifying the particular data set being inspected. In this case, the Natural Breaks method was chosen. The data values supplied from the census data showed a heavy positive skew - a majority of values were clustered in the lower quarter of the possible range of values, with a long tail extending to the left. Such a heavy skew indicates that this data does not conform to a normal (or gaussian) distribution. That makes the Standard Deviation classification a poor fit for the data. The skewing also makes the Equal Interval classification a poor choice. Note in the map below that most of the census tracts have the same classification on the equal interval map. The Natural Breaks and Quantile methods are both suitable for this data, but the latter results in the lower ranges being extremely narrow. That is potentially misleading to a map user who doesn't look closely at the legend. The Natural Breaks classification does have narrow ranges on the left side of the number line as well, but not to the same extremes.

Four Classification Methods Compared

Population Percentage using Natural Breaks classification.

Acquiring Data & Standardizing Projection Coordinate Systems

Continuing last week's focus on projection systems, this week we were required to acquire map data from a variety of sources and integrate them into a single map document after ensuring that all the shape files and raster data involved were projected onto the same projection coordinate system. This also included adapting a spreadsheet that contained spatial and attribute data so that it could be successfully imported and projected by ArcGIS. This file had latitude and longitude coordinates for petroleum storage tanks in Escambia County in degrees/minutes/seconds format, which ArcGIS cannot recognize. So, within the spreadsheet, columns were added that converted the d/m/s coordinates into decimal degrees, which ArcGIS can use. Once this was done, the file could be imported and then converted into a shape file. This was then used along with aerial photos and vector files - for Florida's counties, major roads, and the Florida Department of Environmental Protection's digital orthophoto quadrant map - to create the map below, which shows the locations and types of petroleum tanks in two quadrants located in Escambia County.

Petroleum Storage Tanks in South Escambia County, FL

Getting Started with Spatial Statistics

This week featured an introduction to best practices for preparing a map and dataset for a spatial statistical analysis. This included evaluating the spatial distribution of data collected (as per the map linked below), including calculating the mean center and median center of the data points. If they are in close proximity, it is an indicator that the data may be normally distributed, whereas if they are not the data likely has some sort of spatial skewing going on. The oval representing the directional distribution of the data points below is calculated based on 1 standard deviation of the data point's location calculated along the x and the y axes. In this case, the data tends to be spread out further along a more or less east to west axis, although the axis have a slight tilt towards the southwest from the northeast. Normally distributed data would also tend to have about 68% of its data points lying within such an ellipse.

From there, we looked at ways to determine how the values of the data are distributed. Both histograms and qq plots were looked at as ways to evaluate whether the data values are normally distributed and also whether there are any outliers in the data. From there, we looked at ways to explore the variation in our data, with an eye toward making sure that the data has a locational element to it. This involved voronoi maps and semivariogram clouds. These also act, again, as methods that can help point out outliers in the data that may need to be removed from the dataset.

Finally, we were exposed to methods that can be used to do a trend analysis to look for any spatially oriented trends inherent in the data.

An Analysis of the Spatial Distribution of Temperature Data in Western Europe

Projections & Projection Coordinate Systems

This week's assignment dealt with identifying and transforming projections and projection coordinate systems of map data so that data from different sources with differing projections (and sometimes unidentified projections) can be integrated accurately. My primary task was to take a shapefile with data provided with in an Albers Equal Area PCS format and to transform it (locally only, within the .mxd file I used for the project) to two other projection coordinate systems (while all three maintained the same geographic coordinate system and datum).

The resulting map shows all three coordinate sytems' version of Florida and its counties. Four counties were selected, and their areas calculated in each coordinate system in square miles, highlighting the differences between the systems. As two of the systems were created for the north and western portions of Florida (UTM 16 & State Plane Florida, North), they tended to have more distortion of areas calculated for counties in the southeast as compared to those in the northwest of the state.

Florida Displayed in Three Different Coordinate Systems

Mapping Typography

This week's cartography exercise focused on the complexities involved in using text labels in mapping. I was given an Adobe Illustrator file that contained only an unlabeled mapped area and a separate list of diverse features to locate and label on said map. These features included islands, bodies of water, cities, an airport and some recreational features. It was my task to label these features according to established typography guidelines for maps and to add the other necessary map elements that would make the map legible to the map user. I placed text labels according to the type of feature labeled, e.g. placing labels for hydrographic features within spaces representing those features, in all caps, spanning the feature when appropriate and colored them carefully so that they stood out legibly from the blue background of the map area, but did not overwhelm the rest of the map or the labels for the land based features. Care was taken with label placement, type size, type positioning and choice of type family. The resulting map, below, provides a general overview of the layout of one chain of the lower Florida Keys, Marathon.

The Islands of Marathon, FL

ArcGIS Online & Map Packages.

This week's exercise for Intro to GIS involved making use of ESRI's online map sharing functionality. I updated my existing ESRI account to ESRI's online training materials and worked through the basics of two of ESRI's online courses. The first exercise (yosemite_operational_data) involved creating a small map that I posted up on ESRI's web mapping site. The second (tree_basemap) covered the creation of a map package that was uploaded to ESRI's site and made public. Such packages allow the sharing of maps, including their operational data, so that they can be used accessed and used by peers or by the public to do spatial analyses, update attributes or simply navigate via the map.

yosemite_operational_data

tree_basemap