GIS4043 - Week 12 - Geocoding

Optimal Route Between Three Lake County EMS Stations

This week's lab was about Geocoding, Using Network Analyst, and Model Building.  We began by downloading data from the U.S. Census Bureau site.    Specifically, it was a Tiger Line shapefile for Lake County, FL; with this file we created a feature class for our geodatabase, Lake_Roads.   We also imported address information for Lake County EMS stations from a provided Excel table.  From our geodatabase Lake_Roads feature class we could create an Address Locator.  Finally, we could geocode the addresses for the EMS stations from the table we imported!  This was a frustrating experience but I learned how important it is to geocode!   After this process I realized how difficult it can be to correctly geocode/match addresses.  Thankfully, two-thirds of the addresses matched via our Address Locator.   Finding address matches for the remaining one-third required some zooming and hunting skills and the assistance of Google Maps and Bing Maps.  Some of the station addresses did not match due to field naming nomenclature.   A county road had the same number as a state road.   A state road was listed as a state highway.  Sometimes only one side of the street was numbered.  Even some of the rematch candidates were not good options.   I usually picked an address match from the map after a Google Maps and Bing Maps search.

With all of our station addresses correctly matched we then selected three stations to analyze with Network Analyst.  Using this tool we created an optimal route of travel between the three stations based on the amount of travel time.  U-turns were allowable anywhere but one-way and turn restrictions had to be followed.  I selected a station location in each region of Lake County to create my analysis set (Northeast-#141, Northwest-#241, and South-#341).  Using the Network Analyst Tool, Route, I was able to create a route based on travel time between the three distances.   I chose to start my route in the Northeast at Station #141 and then travel to Station #241 in the Northwestern part of the county and then finally end at Station #341 in the South.  I also tried changing the sequence of stops but the order sequence described above produced the optimal route which covers 32 miles in 43 minutes when started on a Monday at 8am.

In the final section of the lab, we were introduced to ESRI's Model Builder.  The training model was built to determine the locations of gas leak areas around schools in Fort Pierce.  The Model Builder helped me see the big picture from above the tree line of what we've been doing in some of our previous labs.  Using a model to manage geoprocessing operations makes sense.   Once a model is built you can copy or modify it for another use simply by making changes to either inputs or tools and share it.  We learned the basic elements of Model Builder; what the different shapes represented and whether they appeared filled with white (not ready to run), color (ready to run), or with a shadow (already run).

ESRI Gas Leak Model

GIS3015 - Module 10 - Dot Mapping

In this week's module we learned about Dot Mapping.  Dot Maps are used to show conceptual raw total data for enumeration units.   Dot size, value, and placement is important to show that the data occurrences are not uniform throughout the enumeration unit.  One dot is assigned a certain amount of occurrences of the data.  Dot maps differ from proportional symbol maps because they use multiple dot symbols to show variation in the spatial distribution of the data.  Ancillary information is used to create a detailed map of the raw phenomena.   Attributes that are either limiting or related can aid in the correct placement of dots.  Selecting the correct dot size and value can affect the perceived distribution of the phenomena.  If dots are too small, then the data appears to have a sparse distribution and if they are too large then the distribution appears too dense.  A ‘nomograph’ can be used to determine dot size and value.

This week's assignment tasked us to create a dot map displaying the population data of the 23 southern counties of Florida.  The main focus of this map was the dots.   The dot density menu under the Symbology tab was used to create the dots and determine their size, value, and placement.  Dot sizes between 1 and 5 are normally used and I selected a size of 3.5.   This dot size seemed to create dots that appeared to begin to coalesce without overlapping.  The dot value I selected was 1 dot represented 20,000 people.   This value in combination with the dot size appeared to best represent the data.   Using the dot symbology properties menu we fixed the placement of dots so they would remain static and also applied a mask.   Masking allowed us to exclude dots from being placed in the surface water layer.   There were two options but selecting to place the dots only in the Urban Land layer provided the correct dot placement.  I ran into some trouble with the masking option as it seemed change after I had rearranged the ordering of layers in my map later on.  

For this map I chose to not include outlines or boundaries on the map.   I felt  the map's purpose was to show the spatial distribution of the dots and the boundaries would interrupt the visual flow of the map.   My first South Florida layer containing the dots had a hollow fill and was at the top of my Table of Contents (TOC).  I chose to show the Urban Land layer so the map viewer could see that people congregated in urban areas.  I added a second South Florida layer to show the areas of South Florida that were not surface water or urban land.   Per the lab requirements I categorized the surface water layer to show rivers, lakes, and wetlands.  I also provided an inset map of Florida for reference.

I chose to create a legend detailing the dot value.  In order to create this additional legend I used the Draw Tool in ArcMap to first create three rectangles.   I then placed an appropriate number of points in each box to represent different population values.  I created text with the same tool to label each square and then grouped the individual items as one rectangular unit.  I chose to use the same urban land color as the background for my dot value legend.   I had a few trial/error moments using the Draw Tool but overall it was relatively easy to use.   I also used this tool to type my major city names and leader lines.   Originally, I labelled the cities using the label tab for the Major Cities layer but found that the labels were covering dots on the map.   I chose a hollow circular symbol for the cities so the dots would not be hidden.

Overall, I enjoyed making this map and reading/viewing other dot maps when learning about this method, especially maps such as those created by the Cooper Center (http://demographics.coopercenter.org/DotMap/index.html).  Visually, dot maps can be a great way to communicate with the viewer.  Pattern variations of a phenomena over a range of time is easily seen with dot maps (http://www.nationalgeographic.com/clean-water-access-around-the-world/#select/TOT/total).

GIS4043 - Week 10 - Vector Analysis Part 2

Potential Campsites In DeSoto National Forest, MS

In the second phase of our understanding Vector Analysis we were tasked with taking features from a provided geodatabase and creating a map of potential campsites.  We applied different analysis tools to a vector layer (Roads) and a polygon layer (Water) to create buffer zones or areas within a certain range of a feature.  The first analysis tool was the Buffer tool.   Per the lab instructions we created road buffer zones of various distances (100 meters - 300 meters).   We later used only the 300 meter road buffer zone layer to further develop our map.  In order to create these road buffer layers we used two methods, the first method used a fixed distance or linear unit to define the buffer zone.   Later we created road buffer zone layers by using Python scripts (arcpy) to create multiple processes at once.   By simply using copy/paste we could create buffer zones for the road layer of different set distances (100, 200, 250, 300).  We also used the Buffer tool to create a variable distance buffer for the Water layer.   We had two types of water features, rivers and lakes.   The lab required different buffer distances for each water type.   Rivers had a buffer distance of 500M and Lakes had a buffer distance of 150M.  This method required that we create a new field in the Water attribute table called buffdist (buffer distance) and assign the different distance values according to the water type (river, lake).

Next, we used Overlay tools to combine feature attributes of input layers to create new output layers.  Specifically, the lab used the Union tool to preserve all of the features in both input layers to create a new output layer.   Our input layers were the Water_Buffer layer we created with the varying buffer distances per water type, and the Roads_Buff300M layer which was our road buffer of 300 meters.  This union created an output layer with nine records.   From this output layer we then narrowed our selection by choosing records that only resided within our road buffer (300M) and our water buffer (150M and/or 500M).   This new Buffer_Union_Export layer became our input layer to create Possible_Sites, a layer of potential campsites.   After adding the provided conservation_areas feature class to the map, I then used the "Erase" overlay tool to exclude any conservation areas from our Buffer_Union_Export layer.   This newly created Possible_Sites layer was complete however, we could not view individual polygons (campsites) because features had been grouped together into a multipart layer.   We used the Data Management Tool, multipart to singlepart to create single polygon records, campsites.   Using this final singlepart layer I created a final map highlighting these potential campsites that met our site requirements of being located within 300 meters of a road and within our water buffer zone (150 meters of a lake and/or 500 meters of a river).  I used a World Terrain basemap underneath the feature data as well as for an inset map for reference of the location, DeSoto National Forest, Mississippi.

This assignment was a challenge for me mainly because of some ArcMap difficulties in exporting and saving files to an existing database.   I learned a work around to schema lock error messages (create shapefile and import shapefile into geodatabase).  I also had to take time to thoughtfully understand the overlay tools and the output layers they actually created from the input layer(s).   Creating a description field really helped me visually see what features were combined, excluded, etc.

GIS3015 - Module 9 - Flow Line Mapping

Worldwide Immigration to the United States By Region in 2007

Hard to believe we have just completed our 9th module in GIS3015!   This week's lab covered Flow Maps.  Our lecture and text discussed the different types of flow maps; distributive, network, radial, continuous, and telecommunication.  For our lab assignment we mapped migration data with a distributive flow map depicting the movement of people between geographic regions.  The entire world is mapped and depicts the flow of people migrating to the United States from other regions across the world.   The precise route is not as important as the direction and magnitude of the migration.  To calculate the varying width/weight of each region’s flow line I chose a maximum line width of 14 (Asia) and applied the formula provided in the lab instructions (Width of line symbol = (maximum line width) x (SQRT value / SQRT maximum value)).  After crunching the numbers the respective line widths for each continent were calculated (North America - 13.169, Europe – 7.858, South America – 7.378, Africa – 6.957, Oceania – 1.766).  When creating the flow line to depict North Americans’ migration to the U.S. I chose to use converging lines to represent the migration populations from the southern part of North America and the northern part of North America.  The flow lines representing Mexico and the Caribbean region were given a weight of 11.852 and Canada and the northern area of North America were weighted 1.317 for a total North American flow weight of 13.169 per the line width formula.  

My first stylistic change to the provided Base Map was to change the color of the worldwide regions.  Using the layer panel, I selected each continent and used the Edit Color, Recolor Artwork to change the continent colors to RGB values of a qualitative color scheme for 6 classes found on Color Brewer.  I chose to keep the color scheme for the United States, provided in the B Base Map, because I felt it adequately contrasted the continent color scheme I had selected. When creating flow lines from each region I originally chose a black stroke.  To keep the flow lines as the main focus of the map, without them becoming overpowering, I chose to modify the transparency opacity value to 40%.   This modification essentially “greyed” the flow lines but also enabled the viewer to see flow line overlap.  By ordering my flow lines in their respective layer I ensured that the smaller South American flow line appeared “on top” of the North American flow line.  Because the flow lines are the main map focal point, I applied a drop shadow to each line.  Finally, I chose to use a 3D Extrude and Bevel Wireframe effect on my map title.   I did not like the 3D effect look for this map so it was more of a fill effect than 3D and I applied a Border Frame from the Brush Library to my Neatline.  

I had to watch a few videos to understand how to use the pen tool and type tools correctly.  With each use of Adobe Illustrator, I discover new features.

GIS3015 - Module 8 - Isarithmic Mapping

In this week's lab we were tasked with learning about Isarithmic maps which are used to depict smooth, continuous phenomena across an area using different symbology methods.  We also learned about the two different types of data appropriate for this type of mapping, true point data and conceptual point data.  Next, we covered four different methods of data interpolation that are used to derive intermediate points to create the dataset (Inverse Distance Weight [IDW], Kriging, Splining, and Triangulation).

Our lab used precipitation data for the State of Washington that was derived and interpolated using the PRISM (Parameter-elevation Relationships on Independent Slopes Model) analytical model.  This model uses point data and an underlying grid such as a Digital Elevation Model (DEM) to generate gridded estimates of monthly or annual precipitation.  Our precipitation dataset was created from the application of this method on point data collected from weather monitoring stations and the calculated climate elevation regression for each grid cell within the DEM.  The created dataset accounts for physiographic factors that may influence climate patterns.

 

Continuous Tone Map (Map 1)

Map 1 uses Continuous Tone symbology to represent the data with a stretched precipitation color ramp with the hillshade effect option selected.  This effect creates a "relief" to denote elevation.  During this part of the lab, we also spent some time learning different techniques to create/modify a map legend within ArcMap.  This method can sometimes make it difficult to associate numbers of the legend with locations on the map.

Map 2 uses Hypsometric Tint symbology to represent the data with manual classifications (10 classes).  A "stepped" surface is created to more easily visually interpret the precipitation differences across the State.  Contour lines with defined ranges were also added to provide a three dimensional element to the map.  Map viewers can readily associate the color hues on the map with varying precipitation values.  Prior to changing the symbology we used the "int" spatial analysis tool to convert data in the precipitation raster dataset to integers.  Converting to integers enabled us to create crisp contour ranges represented by whole numbers.  Applying some of the legend techniques from this week's lab as well as the previous week, I created a vertical continuous legend to display the precipitation range values.  

Hypsometric Map with Contour Lines (Map 2)

GIS4043 - Week 7-8 - Data Search

Leon County, FL Map 1 - Cities, Roads, Hydrography

Leon County, FL Map 2 - Public Lands,
Invasive Plants in Elinor Klapp Phipps Park

This mid-term lab required us to find and download nine data layers to create a map or maps of our assigned Florida County.   I was tasked with presenting Leon County, Florida home of the State Capital, Tallahassee.

Using vector data layers I created my first map depicting cities, roads, and major hydrography features of Leon County.  I chose to include an inset map of the State of Florida for reference purposes.  I changed the symbol for the City of Tallahassee to represent a Capital City.

Leon County, FL Map 3 - Elevation and
Strategic Habitat Conservation Areas

In my second map I chose to display the public lands located in Leon County and then focused in on a park located in Tallahassee, Elinor Klapp Phipps Park (EKP).  I used my aerial raster layer, downloaded from Labins.org, to show a closeup view of the EKP Park.  I then added one of my environmental layers (invasive plants) on top of the aerial layer to show the various invasive plants found in the EKP Park.

For my third, and final map I chose to create two data frames.  The left frame displays the elevation of Leon County by using the raster DEM layer from the USGS.  I chose a color scheme that would enable the map audience to easily recognize the differences in elevation.

The right data frame displays my second environmental layer of strategic habitat conservation areas.   This raster layer ranks areas by priority level.  Again, I chose a color scheme that enables easy visual interpretation of the priority levels.

This lab was definitely a challenge.  Finding all of the required layers took some time and patience.  It was also helpful to view the metadata on each download site prior to downloading.  I also added all the layers to one mxd just so I could view each one and see how their data was presented visually as well as in their respective attribute tables.

Once I had my data it was hard at first to come up with my map designs/layouts.   I began with the simplest first (Map 1).  Creating Map 1 was very similar to previous lab assignments.  I started on Map 2 and began to formulate my design after looking at all of the remaining layers.  I created the left data frame of Map 2 but was unsure how and which layers to present on the rest of the map page.  I took a mental break from Map 2 and started Map 3 by adding the DEM layer.  Since I did not have too much experience with DEM and raster data layers I chose to clip this layer to the Leon County frame and display it singly.  I was worried my remaining Map 2 design would look "too busy" so I decided to display the Strategic Habitat Conservation Area layer alongside the DEM layer.   Both layers were raster data and it helped make the map appear symmetric.  I returned to Map 2 and focused in on EKP Park.  My daughters have played in travel soccer tournaments at the nearby Meadows Soccer Complex so I thought, why not.  Of course, the first aerial layer I downloaded had not been located near the park, so selected another layer.  I was right next door, but needed to move one more quad left and select my final DOQQ of q5335se.sid.  I really liked using the aerial layer as it served as a "basemap" for my invasive plants layer.  I was amazed at how many invasive plants were spotted in such a small land area; really made me think!