Module 12 Google Earth - Bringing your map to life, with a tool non-techie laypersons can use!

Our goal this week was to export our Dot Density map to a KMZ file and then use that information in addition to some placemarks to create a recorded tour of South Florida.  Although we had previously created the Dot Density map, we needed to save it to a new map and perform some tweaks.  In order for the dots to be visible, we made the background hollow.  My urban land layer was already off and my surface water was symbolized by category; my legend needed to be recreated in a more simplistic fashion in order to work with Google Earth.  Once those tasks were complete, I exported the Map to a .kmz using the Map to KML tool.  I then exported the Dot Density layer to a .kmz by using the Layer to KML tool.  Once complete I exited my map, opened Google Earth Pro and imported my two .kmz files.  I moved the dot density layer to my map layer, adjusted the color and then saved using the save place as function.

 
I then created a layer for my tour stops making sure to place them in a location that would be suitable for my zoom areas and to label them clearly.  I also made sure to turn on the 3D buidlings layer so that they would be visible.  When I began to record my tour, I found the mouse to be too difficult to control for smooth video; consequently, I did some research and used the information found here https://support.google.com/earth/answer/148115 to obtain the keyboard shortcuts.  When rotating using the mouse, shift and arrow keys, the speed was too fast; I added the alt key simultaneously with the shift key and cut my speed by half.  After a couple of practice runs I recorded my final video.

This lab focused on 3D mapping.  We used ESRI training as the basis of our learning and practice and them implemented some of what we had learned with our own exercise.  Our learning targets were 3D visualization techniques and converting 2D data to 3D.  Using the ESRI tools we learned to create base heights for rater and feature data, how to apply vertical exaggeration, how to improve your map utilizing illumination and back ground color, how to extrude above and below ground features containing elevation values and how to extrude parcel values.  Having completed the ESRI basics it was time to apply some of our skills to converting 2D data to 3D.  We used existing data containing building footprints and a raster surface with elevation data.  We created random points from our building footprint layer using the CID field.  We then added surface information to this sample points layer by using the raster file z values.  We then took our sample points table and used the summary statistics tool to generate a single elevation value for each building by using the z value mean.  We were then able to join the table attributes from our sample point statistics table to our BostonBldgs layer; we exported this data as a file and personal geodatabasefeature class.  We then used ArcScene to extrude our buildings using the Z value.  After completing, we exported this layer to a KMZ file using the Layer to KML tool.  Here was the result in google earth:

Both 2D and 3D have many individualized uses.   2D data can require less expensive software or add-on tools, but can be limiting with respect to ease of understanding when excess layers are incorporated into maps, especially in multivariate situations.  2D also requires the end user to be able to visualize any three dimensional information about the data being conveyed.  For instance, a user unfamiliar with topography might not understand what the contour lines represent.  The same user, however, could readily see the elevation changes with a 3D map.  Creating 3D maps from 2D information can be more expensive and more time consuming, but the ease of visualization, eye-catching depictions, and user interaction can convey more information to the end user.  The ability to better visualize environmental impacts, urban growth, and applied 3D information to value components makes 3D mapping a great tool for planning and presentation.  

Module 12 Geocoding, Network Analyst, Model Builder - where is it, what's the most efficient/cheapest way to get there, and saving time with models

This week's lab consisted of three parts.  The first two focused on Geocoding and using the Network Analyst while the 3rd portion utilized ESRI training to implement the use of model building for multiple processes.  I really enjoyed this lab, although I seem to enjoy them all.  Can I get paid to do labs?  The Geocoding portion walked us through the five essential steps for good geocoding: download the Tiger Lines and if not already projected or in a projection different than what you are using, project them; Import the .shp into your map along with the table which needs addressing and any other base map information you might want; create an address locator (or use one you've already saved or provided by ESRI depending on your need); Perform the Geocode; REVIEW AND REMATCH so that you get the optimal matched rate.  We were able to explore the "candidate" option and the "pick from map" option.  The candidate options may not be the best for your location, and if you want to be sure, verify via an outside mapping source such as Bing Maps or other published information.  While the process may be a bit painstaking it is quite important to be sure you have the correct geocoded locations.  We took our geocoding results and imported them into a new map along with our Tiger Line information and our county boundary.  We were then able to add our Network Analyst extension and tool bar to create a route analysis.  There are many options within the Network Analyst which affect how your route will be analyzed.  We chose to use street addresses with our MyAddressLocator style, edit location names when adding a new location and loading locations using location fields.  For the Location Snap Options we chose to snap locations when adding a new location and moving a location.  Once our options were set we were ready to add some stops to our route.  I randomly selected three stations and created the route.  Once I saw it, I realized my order caused me to double-back; ArcDesktop makes this easy to change and I adjusted my stops to eliminate the issue.   The Network Analyst tool allows for various parameters to be applied to your route analysis.  We set our impedance to minutes and I chose Monday at 8am as my time of day and day of week.  We allowed U-Turns but enabled Oneway, Turnrestrictions and UnbuiltRoadsRestrictions.  We made sure to put miles as the distance unit and time attributes as minutes and away we went.  Once we solved the route, we were able to generate turn by turn directions and export the data.  Like I said earlier, this was really cool and fun.

The last par of our lab focused on Model Building to visualize, automate and share geoprocessing workflows.  Although we did not delve too deeply, we were exposed to the basic elements of model building.  I can see how building models for specific processes which you might need to repeat would be extremely helpful.  It is very easy to create a basic task skeleton and then add or alter the parameters.  I might get crazy with this, who knows.  The color coded shapes of the various elements make it easy to distinguish input data, output data, tools having run and tools not ready to run.  You can verify your model and quickly locate where your error lies.  Often, if you find and correct an error early in the process chain, the remainder of your model will run flawlessly.  I was really psyched with this tool and called my partner into the office to show her; she saw the functionality in the tool, but was not nearly as excited as I was and returned to training the puppy.  At any rate, I look forward to learning more about this tool and creating more complex models.  Here's the one we created for illustrating gas mains near schools which needed to be closely monitored for leaks:

Module 10 Dot Mapping.......

        This week’s module introduced us to Dot Mapping.  Dot maps are used to represent the location of one or more instances of a geographic occurrence.  The dot symbol represents a specific quantity of the occurrence and is placed in the relative location of the occurrence.  When creating a dot map, conceptual or raw-count data should always be used.  The dot map allows for a quick visualization of the quantity and location of graphic occurrences.  It is also useful for comparing distributions of related incidences.  Within this lab we also learned how to spatially join tabular data, select the appropriate dot size and unit value and use a masking layer to improve our dot placement.  We were also given the opportunity to improve upon ArcMap’s legend and create a more improved and informative dot legend.   For this task, I exported my map to pdf and then used Bluebeam Revu to create the rectangles and insert the appropriate quantity of dots.  I then exported a snapshot of those rectangles to ArcMap and added the necessary text.

Week 10-11 Spatial Analysis of vector and Raster Data and Vector Analysis Part 2 - Buffers and Overlays

This weeks lab gave us practice using tow common modeling tools, buffer and overlay.  We combined that with using ArcPy to run multiple scripts which was a great time saving trick.  We practiced analyzing our vector data using spatial queries, practiced several of the 6 types of overlay features and applied the multipart to single part tool.  The first portion of the lab gave us opportunity to practice these items.  In the final portion of the lab we were asked to finalize our map by adding the conservation layer and determining which park sites met all the criteria and did not fall within a conservation area.  Since the lab had walked us through creating our buffer_union_export feature class, I was able to use the erase tool from the Overlay toolset within the Analysis Tools toolbox to create a new feature class of the buffer_union_export features which fell outside of the conservation_areas feature class.  Once this was accomplished, we needed to tidy up our maps and add all the essential map elements to produce:

Module 8 - Flow Mapping ....stylized distributive flow....I can go with that

This week’s module was an introduction into flow line mapping.  There are many types of flow maps, Distributive, Network, Radial, Continuous and telecommunications to name a few of the most used.   This lab, depicting immigration into the United States for the year 2007 is a type of distributive flow map.  We mapped the quantitative data found in the U. S. Homeland Security Office of Immigration Statistics 2007 Yearbook of immigration statistics.  Since we did not know the actual migratory paths, we used a stylized placement approach to represent the general direction of flow and indicated quantities through the use of proportional flow arrows.  The line weight of the flow arrows was calculated by taking the square root of the population value of each Continent; we then chose a maximum value line width, in this case 15.  With those two pieces of information we applied the following formula using the highest population value as our denominator, in this case Asia with 619.28 resulting in the following formula for each of our continents where X=continents square root population value: Width of line symbol=15*(X/619.28).  This calculation gave us a line weight for each continent that was proportional to the immigration value for that region.  This made it easy to draw conclusions regarding relative quantity of immigrants per continent. 

Critical to the use of low maps is figure ground.  The flow lines should be the most important element in the visual hierarchy.  I made these a dark color which was separate from any other color on the map, applied appropriate proportional symbology and experimented with stylization effects to find one that provided additional visual hierarchy without being overpowering or distracting; my final choice was a subtle drop shadow effect.  The topic of projection was already solved for us in this case, but none the less is an important decision when creating your flow map.  I had no issue with the provided continent color schemes or with the U.S. Choropleth map and, therefore, chose not to alter them.  I did increase the size of my map area to fill the paper as well as enlarge my inset choropleth map for additional clarity.  In both instances I was careful to group the scales with their associated maps so that they remained proportional.  I labeled the continents for clarity, added a title, author, date, source and projection information.  At this point I decided to apply a fill color to my background.  I used a blue tone that was slightly dark to not only represent the oceans, but to also make the continents and flow arrows stand out more.  I then turned my attention to the legend.  I used a contiguous color range legend for the choropleth inset map to show the percent of total immigrants per state.  I used a second legend for the main map to connect the immigration values by continent to the U.S.  I thought keeping with the “arrow” theme would be attractive; consequently, I created a series of arrows that gradually reduced themselves in length as the quantity of immigrants decreased.  I match the colors of these arrows to their continents and, since I had made them wide enough, included the continent name and immigration value inside each arrow.

Module at Isarithmic Mapping - Come on in the weather is fine....as are the hills, valleys, depressions...

                This week we learned about Isarithmic Mapping.  Within that scope, we were introduced to various methods of interpolation, including PRISM, worked with continuous raster data and learned how to symbolize and produce a complete and accurate legend.  We worked with both continuous tone and hypsometric symbology both utilizing hillshade relief.  Additionally we learned to create contours to overlay on our hypsometric symboloized map.  The map illustrated here was our final product and depicted the annual precipitation for the State of Washington using climate data from 1981-2010.  The Prism method interpolated the data using the 30 year precipitation data and a DEM of the state for elevations.  The various algorithms take location, elevation, coastal proximity, topographic orientation, vertical atmospheric layer, topographic position and orthographic effectiveness of the terrain in relation to the monitoring locations (stations), to develop a suitable model for our map.  We used the Integer Spatial Analyst tool to convert the floating raster values to integers so that we could create “crisp” contours and be able to truncate values as whole numbers.  Renaming the layer to something less cryptic later facilitated making the legend more functional.  We classed the data using a manual break method with 10 breaks (break values were provided within the lab).  After classing the data, we chose a precipitation color ramp and applied a hillshade effect.  This produced an attractive map with visible relief.  In order to make the relief even more visible, our next task was to add contour lines.  This was accomplished using the Contour List Spatial Analyst tool.  We assigned contour values which matched our break values.  Although one would typically notate the contour interval in the legend, this step was unnecessary in this case since our contours followed our hypsometric steps.  I symbolized the contours using the temperature contour line available within the weather style manager.  The final steps were to add essential map elements to create a finished, easy to understand, visually appealing map.

Week 7 & 8 Data Input, Editing and Data Search - Finding your ducks and keeping them in the row.......

     This midterm lab was designed to pull together our learning thus far.  Using an assigned Florida County, we were to determine the project requirements, obtain appropriate data, select a suitable graphic projection and display nine data formats using logical layering so that the final product(s) were visually compelling and easily to interpret.
     In addition to the five mandatory vector datasets and two raster datasets, I chose to use Invasive Plants and Wetlands for my additional Environmental dataset component.    All of the data was readily available either on Labins or FGDL with the exception of the hydrography information.  The hydrography data on FGDL, broken down by county, excluded Broward which was my assigned county.  I ended up using information from SFWMD; this contained hydrographic information for the entire state and took a bit of manipulation to eliminate the unwanted attributes within my clipped area.  Once I had obtained all the required data, I imported and projected (as needed) the data into an mxd file.  I used this file to determine how many maps I needed to create and the best approach to combining items within maps for good presentation.  I saved this file to my first map, removed unwanted data, symbolized remaining data and added required map elements.  I then saved two additional copies of this file so that I could remove the first set of data and add the data required for the current map.  I then symbolized the data, updated any source information and then moved on to my final map.  This process helped me to keep my map layouts identical and to reduce recreating the inset map and other essential elements; instead, I simply needed to perform minor editing.  I have attached my three maps and followed them with an outline of my process.

I.                   Process Summary Details

             A.      Obtain Data and Project/define as needed

a.       Aerial Imagery from Labins.org

                                                               i.      Quarter Quad 1705NW, Gator Lake

1.       2004 RGB Albers, Units in Meters, .sid and .sdw

2.       Coordinate System NAD_1983_HARN

3.       Albers Projection

a.       Projection: Albers

b.      Datum: HPGN(NAD83)

c.       Units: Meters

d.      1st standard parallel: 24 00 00

e.      2nd standard parallel: 31 30 00

f.        Central Meridian: -84 00 00

g.       Latitude of projection's origin: 24 00 00

h.      False easting (meters): 400000.0

i.         False northing (meters): 0.0

j.        Graphics Type: MrSID

k.       World File: sdw

l.         Resolution: 1-Meter

m.    Color: "True Color" (not CIR)

4.       Raster needed spatial reference assigned.  I confirmed the above information matched the values in the Albers Conical Equal Area coordinate system and assigned it using the Raster Dataset Properties box via Catalog window.

a.       Confirmed spatial reference by inserting in a new map and verifying the Data Frame Properties/Coordinate System Tab/Layers and Projection as well as inserted a topographic base map to confirm alignments.

b.      County Boundary Information from FGDL.org

                                                               i.      U.S. Census Bureau’s Florida County Boundaries – Statewide July 2011

1.       Coordinate System GCS_North_American_1983_HARN

2.       Albers Conical Equal Area Projection

a.       Confirmed spatial reference by verifying the Data Frame Properties/Coordinate System Tab/Layers and Projection as well as inserted a topographic base map to confirm alignments.

c.       Major Roads Information from FGDL.org

                                                               i.      FDOT RCI Derived Major Roads – Statewide – January 2016, MAJRDS_Jan16

1.       Coordinate System GCS_North_American_1983_HARN

2.       Albers Conical Equal Area Projection

a.       Used clip tool to show only roads within Broward

b.      Confirmed spatial reference by verifying the Data Frame Properties/Coordinate System Tab/Layers and Projection as well as inserted a topographic base map to confirm alignments.

d.      Cities and Towns – FGDL

                                                               i.      cities_feb04.shp

1.       Coordinate System GCS_North_American_1983_HARN

2.       Albers Conical Equal Area Projection

a.       Used clip tool to show only cities/towns within Broward

b.      Confirmed spatial reference by verifying the Data Frame Properties/Coordinate System Tab/Layers and Projection as well as inserted a topographic base map to confirm alignments.

e.      Invasive Plants – FGDL

                                                               i.      Fnaiip_jun10.shp

1.       Coordinate System GCS_North_American_1983_HARN

2.       Albers Conical Equal Area Projection

a.       Used clip tool to show only invasive plants within Broward

b.      Confirmed spatial reference by verifying the Data Frame Properties/Coordinate System Tab/Layers and Projection as well as inserted a topographic base map to confirm alignments

f.        Public Lands – FGDL

                                                               i.      American Indian Lands – amindianlands_2008.shp

1.       Coordinate System GCS_North_American_1983_HARN

2.       Albers Conical Equal Area Projection

a.       Used clip tool to show only American Indian Lands within Broward

b.      Confirmed spatial reference by verifying the Data Frame Properties/Coordinate System Tab/Layers and Projection as well as inserted a topographic base map to confirm alignments

                                                             ii.      Public Parks – gc_parksbnd_oct15.shp

1.       Coordinate System GCS_North_American_1983_HARN

2.       Albers Conical Equal Area Projection

a.       Used clip tool to show only Public Parks within Broward

b.      Confirmed spatial reference by verifying the Data Frame Properties/Coordinate System Tab/Layers and Projection as well as inserted a topographic base map to confirm alignments

g.       Hydrography - ftp://ftp.sfwmd.gov/pub/gisdata/hywetnwi11.zip

                                                               i.      Geodatabase and layer file; rivers and lakes will need to be separated from the other data for ease of use.

1.       Coordinate System GCS_North_American_1983_HARN

2.       NAD_1983_HARN_StatePlane_Florida_East_FIPS_0901_Feet

a.       Due to the large amount of data, I created a layer package (including the data) of only the Water Bodies portion.  I saved this to my project directory.

b.      Unpackaged in catalog window.  Once imported into my map, I ungrouped the layer set and clipped the data to show only information within Broward County.

c.       I then projected the data from State Plane to Albers Conical Equal Area Projection

h.      Wetlands – FGDL

                                                               i.      nwip_oct14.shp

1.       Coordinate System GCS_North_American_1983_HARN

2.       Albers Conical Equal Area Projection

a.       Used clip tool to show only Wetlands within Broward

b.      Confirmed spatial reference by verifying the Data Frame Properties/Coordinate System Tab/Layers and Projection as well as inserted a topographic base map to confirm alignments

i.         DEM – Labins

                                                               i.      N26w081 and n27w081

1.       Coordinate System GCS_North_American_1983

a.       Use Raster Project to Project to Albers Conical Equal Area Projection

b.      Confirmed spatial reference by verifying the Data Frame Properties/Coordinate System Tab/Layers and Projection as well as inserted a topographic base map to confirm alignments

c.       Use raster clip to clip to limits of Broward County