Sunday, March 11, 2018

University of Wisconsin-Eau Claire Garfield Ave. Project and Alternate Pedestrian Pathways

In the spring of 2017 work began on the University of Wisconsin-Eau Claire's Garfield Ave. in order to make the campus a little more safe and student and visitor friendly. The figure below shows the initial state of Garfield Ave. before construction and the proposed look for campus when construction is complete.
Figure 1. The initial state of Garfield Ave. on the UWEC campus. Notice the walking bridge stops at Garfield Ave. making students cross the street and the flow of pedestrian and biking traffic very inefficient and dangerous.

Figure 2. The proposed finished product of the project. The street is fully gone with pedestrian and bike only traffic. All rights go to the University of Wisconsin-Eau Claire and their partners for this design model.

In the summer of 2017 and the fall of  2017 the main walking path that connected the walking bridge to the heart of the campus and to academic buildings such as Schneider Hall, Phillips Science Hall, and Davies Student Center was closed off. Pedestrian and bike traffic was directed to walk towards Hibbard Hall and past Zorn Arena and Centennial Hall. Many students and professors were upset to by the diversion and supposed extra walking time. From experience, the alternative path extended the walk from my house to Phillips Hall an extra 5 minutes. This led to the question, "Was the alternate path actually longer or shorter than the normal path and did it have a different time impact?"

In order to ensure accuracy, the path was measured from the start of the walking bridge by the stairs by Haas Fine Arts Center to the doors of Phillips Hall. Initial data was provided by Dr. Caitlin Curtis of the UWEC Geography Department as seen in Figure 3.
Figure 3.  The initial data for this project. The features in this feature class are only point and linear data. Further analysis of this data will need to be altered to be in the correct form.

I proceeded to find the distance for each path, as marked below, using the distance tool. This is the easiest way to find the difference between each route but it also has large user error. The second way to find the difference was to create a geometric network making the lines to edges and the point data as simple junctions. I created a weight distance using more accurate distances from survey data that can be found online after a simple Google search. Using the Utilities Network Analysis toolbar, paths for each route were created and the distance weight applied. The distances between the two methods were averaged.

























Figure 4. The top left outlines the alternate path that was used during construction. The top right outlines the normal path taken.

The averaged result between the two methods had the normal path be shorter with an approximate distance of 430 meters. The alternative path was approximately 85 meters (515 meters).

Although this method gives us the distance difference it does not tell us anything about the time each path took. The average human walks about 3.1 miles per hour. Using Excel, calculations were performed to get the time (in minutes) for each distance. 

Figure 5. Table showing the calculations done to get the time, in minutes, for each route. The normal path (430 meters) is clearly shorter than the alternative path.

Although these calculations are useful, it would be even more useful to use the network analysis tools in ArcMap to create a shortest route map to back up these results. Time values were added into the attribute table for the paths shapefile. A new network database was created. The default travel method is automobiles but we need to create a pedestrian method forcing the calculations to use the time values. The shortest route method was selected and solved.
Figure 6. The resulting map of the shortest route method clearly showing the normal path is the fastest.

The results of this analysis is that the Normal Path is not only shorter but faster. These two components were backed up by multiple methods and proved students and professors complaints correct.

All Data for this is property of the University of Wisconsin-Eau Claire and the partners and affiliates. Additional data was found via Google search.

Sunday, February 25, 2018

GIS II: Lab 3, Watershed Analysis

Goals and Background 

The goal of this lab was to use spatial analyst tools to understand watershed analysis and create a map of watersheds in the Adirondack region of New York State. Watershed analysis is a very important concept to understand and be able to use as it is applied in water management, water quality, and conservation to name a few. 

Methods

To begin, data from Cornell University and New York State GIS Clearinghouse was downloaded from the internet. This include a hydrology shapefile an the Adirondak State park polygon. ArcToolbox was heavily utilized in this lab. A 20 km buffer was added to the state park polygon as seen in Figure 1. 
Figure 1. A map showing a 20 km buffer around the state park polygon. The blue lines are the hydrology shapefile from Cornell. These are 2D and have no topographic relation.

An issue with this lab was that not all data was in the same projection. To correct this issue the project tool under data management was used and the projection of the state park shapefile wsa imported. The projection of this shapefile was NAD 1983 Zone 18 N. 

Because a watershed is a function of the topography there needs to be a spatial reference in place. The 30-arc second DEM of North America was imported from ESRI/ArcMap Online. This DEM was also in a different projection and using the transform tool was corrected to be in the same projection. We are only interested in the area of the park and using the clip tool, and new raster dataset was created and the original DEM removed.

The next step is to understand flow direction. Using the flow direction tool under the hydrology tab, nodes are created. Unfortunately, the output contains sinks, or areas that impede water flow. If this map was to be used for watershed analysis the results would be skewed and incorrect so sinks must be filled. The fill tool is used and new nodes are created for flow direction. The result is very trippy and can be seen in Figure 2. 
 
Figure 2. The new nodes are created for flow direction after sinks are filled.  Faint outlines of rivers and streams can be observed. 

Flow accumulation is the understanding of where water accumulates and creates channels. To understand watersheds, flow accumulation must be considered.  Using the flow accumulation tool, a new raster is created.

A source raster also must be created in order to delineate watersheds. This requires a threshold expression. Smaller thresholds yield more watersheds and contain much more detail of in-flowing streams. Larger thresholds yield less watersheds and are more reliant on the larger stream channels. The results figure has a threshold of 50,000.

Figure 3. Watersheds at a higher threshold.

Using the watershed tool,  watersheds for this area are now delineated. At 50k there are 108 different watersheds. The watersheds raster is then clipped to the park boundary buffer. This can be seen in the results section.

Results

Figure 4. The completed map for watershed analysis. The vectorstreams shapefile shows those streams to which contribute for a watershed. These are different from the initial hydrology shapefile as these streams contain a topography factor, are filled, and show accumulation. 

Sources

All materials from this lab are open for public consumption and are property of the University of Wisconsin-Eau Claire, ESRI, Cornerll University, and the New York State GIS Clearinghouse.


Sunday, February 11, 2018

GIS II: Lab 2, Georeferencing

Goals and Background:

The goal of this lab is to review the process of georeferencing using ArcGIS software. This will be done using the ESRI georeferencing course and data as well as data from the University of Wisconsin-Eau Claire. 

Methods

For the historical Eau Claire map, the Mcenterlines shapefile is added first and has a projected coordinate system of NAD_1983_HARN_Adj_WI,_EauClaire_feet and a projection of Lambert_conformal_conic. The historical map has no an undefined coordinate system and projection. This means that the Mcenterlines shapefile is the referenced data for the historical crop. 

For both of this data, the georeferencing toolbar is used. To make creating links easier, a side by side screen is used as seen in Figure 1 below. 
Figure 1. Side-by-side view of the historical, undefined Eau Claire map (right) and the georeferenced Mcenterlines map (left). The study area is consistent between the two maps.

Results



Figure 2. Historical map of Eau Claire, WI overlaying the modern Mcenterlines shapefile (in green). The historical map is at 50% transparency. Note the change in water features, ward boundaries, and street names. Historical maps, like this one, are often drawn in an obsolete local grid system and are not very accurate. There was also issues with georeferencing the historical map and getting it as close to the modern basemap and Mcenterlines files as possible.

Figure 3. Comparisons of the water features in the Eau Claire, WI area. The purple outlined area represents the location of water features today and the light blue represents the location of water features in 1878. Some of these changes could be anthropologically caused while some may be naturally caused. Anthropogenic causes include dams, bridges, and flood barriers to name a few.


Sources

All data came from ESRI and the University of Wisconsin-Eau Claire.

Sunday, February 4, 2018

GIS II: Lab 1

Goal and Background

The goal of this lab was to reintroduce ArcMap software as taught in GIS 1 and use ArcMap as an analysis tool.

Methods

Using the 'Erie' map from the class data folder the attributes tables was closely examined to understand each record and the relationship between one-another. Using the symbology tab under properties for the Erie map two maps were created for the analysis of tract populations and the number of households per tract.  A map for tract populations was created using the sub-tab 'Categories.'  This data can be normalized but it was not in this instance as this map was going to be used to be compared against the number of households per tract map. Under the sub-tab 'Quantities,' a dot map was created for the second map. 

Results


As expected, there were some similarities between the two maps as these variables are dependent of each other. However, there were some interesting differences such as some tracts have a high population but a few number of households. Further analysis of these maps would be interesting if an median age was recorded and could be analyzed or if a land use feature class was added. These further analyses could reveal or disprove any additional trends.

Sources and References

All data used is from the University of Wisconsin-Eau Claire geography department.