Monday, October 16, 2017

GIS 4035 Week 7 Lab Enhancement

For this week's lab we worked with both ERDAS and ArcMap  to look at image enhancement and filters. We started with looking at for rasters and how to download them.

The we applied filters in ERDAS using a raster and changing from low pass to high pass filters, then we used ArcMap and the focal statistics tool to get a 7x7 raster to look at a large low pass filter in comparison to the original raster, you could see less detail but land cover seemed to be more simplified with this filter. Below is a screen shot showing this step.
Next, we applied the focal statistics tool using range and 3x3 for a cell size and the image was much darker, this helps detect edges, and the overall effect is that similar pixels are given low values. The cool thing with this image is that the buildings seem to have white borders making it easier to digitize specific places or tell urban from natural features in the land cover. The shape can be changed from rectangle to radius or other shape depending on what you are trying to remotely sense in the image.
For the last part we did a series of enhancements on the raster including the scientific tool for Fourier Transform editor that allowed us to reduce visual striping to clean up the image. Then we used convolution editing to apply kernel filters to sharpen the image. These in combination with the focal statistics tools allow a user to enhance and modify images to best show the features they need to see to analyze or process a map further. I also used the filter tool under neighborhood with the spatial analyst extension to apply a low filter to better see the resolution. Below is my map:

Thursday, October 12, 2017

GISS 5935 Week 7 Lab TIN and DEM Surfaces Analysis

This week we discussed the differences and advantages or applications of TIN files versus DEM files and then we used both file types in Arc Scene. ArcMap lets you used Spatial Analyst and 3D Analyst and  Arc Scene allows you to drape the files over each other and exaggerate vertical features for visual contrast.

TIN files are based on nodes and edges and create vector data that retains break points, it is best for preserving accurate highs and lows. DEM rater files can be faster and easier to work with and calculate slope and aspect from, and they are more readily available but because of cell size smoothing, they can generalize features to within the unit value of the raster cell size.

I've created TIN files as a surveyor and I like the accuracy they provide, but as a GIS professional, the tools available for working with raster data make DTM and DEM data preferable.

The difference is what determines a contour interval's placement between DEM and TIN files was interesting to me because TIN files seem to base the contours on nodes where the DEM uses any high point even if it is a face, edge, or a node when seen draped over a TIN. Below the black and red contours are over a tin node and the blue contours are from the DEM file and some are on edges but some are on nodes, but the TIN and DEM contours do not line up because they are based on different geometry.

Wednesday, October 11, 2017

GIS 4035 Week 6 Lab

In this week's lab we worked with ERDAS more and worked with attribute tables and how to select certain attributes, like the image below. Here, we selected just fine and humus and changed the formula values to only display these soil classification types in the map image. We also learned how to look at data, or metadata, such as: projections and coordinate information, bit size, resolution, and statistics information that tells you about the brightness of each layer. 

In lecture we learned about different types of scanners like whisk broom versus push broom and hyerpectral and multi-spectral scanners. This is important because a hyper spectral scanner can have 100's of bands a higher resolution. Landsat is more common, but is a multi-spectral scanner with about 3-10 bands. You could even have ultra-spectral scanners with 1000's of bands possible.

Scanners can be active or passive and can be in a near polar orbit which is sun synchronous or geostationary, like most satellites. Spectral resolution helps you tell different bands apart, the narrower the bandwidth, the higher the resolution. You can have temporal resolution that relates to the time or frequency that the same area is captured in images, a camera has low temporal resolution because the shutter speed is low and limits how many pictures you can take in a few seconds.

Saturday, October 7, 2017

GIS 5935 Location Allocation in Week 6

This week we looked at location-allocation modeling and analysis. ESRI had videos with examples like designing a route to evacuate people from a flood or show obesity rates in students in LA. For lab we found best store locations based on market share and to maximize attendance and worked through these in Exercise 9 of the Network Analyst tutorial.

We read about how market share between Wal-Mart, Target, and Kmart are divided and we read in out text about this process and how to model travel.

Then in Lab we did our own location-allocation model. Below is a screen capture showing the final results of the new market areas. I used my spatial join of the demand points and the unassigned market area and then changed the symbology to better show the 22 market areas as they changed before and after the analysis.

Tuesday, October 3, 2017

GIS 4035 Remote Sensing Lab 5

         This week we learned about EMR and how wavelength and frequencies interact, and maybe even change aerial photos. Such things as Rayliegh scattering of light, that makes the sky look blue, can change the visible spectrum of light. Red, blue, and green bands are visible but you can have ultra-violet or infrared light that can be seen with certain technology. The longer the wavelength, the lower the frequency of EMR. Short wavelengths, like blue light, are affected by scattering from the atmosphere. The sun can absorb, scatter, or reflect light as it interacts with the atmosphere.

         For the lab we used ERDAS images in .tiff format to look at large scale data. We changed the raster color options, and worked with symbology and viewing options to better see the raster images.

              Learning ERDAS and how to navigate it was exciting, it is similar in style and organization to Word and ArcMap, with many options that I'm sure get easier once you know what they are. Below is a screenshot of Exercise 3 at the end of just the raster that we are using to make a map in ArcMap.

     Below is my finished map showing the classes of the raster image that I clipped from the above file. I had put the image in acres before moving it into ArcMap, and so I used Miles on the scale bar for easy conversion and Acres and the Acre value in the legend.

Monday, October 2, 2017

GIS Internship Press Release Week 6/7

This week we had a really fun assignment, and I love writing and this was my first time with a press release. I wanted an action statement for my title and then to cover who, what, where, and why quickly in the first paragraph, then I included a quote (from myself) and an image as a visual aide. I kept it to one page and gave supervisor names for good measure. This would be fun to include in a portfolio!

GIS Internship Press Release

New GIS Intern Helps Local Business Owners Find City Ordinances

By: Ingrid Jourgensen

Monday October 2, 2017

   In the summer of 2016 a local resident and GIS graduate student, Ingrid Jourgensen, joined the City of Casper to help organize and map local zoning ordinances. This project began a few years ago in an effort to update and digitize both city and county records. Both ordinances and resolutions dating from 1978 had been compiled by the year into a single adobe document, even hand written resolutions had been scanned. These documents contained all resolutions and ordinance pertaining to such topics as: alcohol sales, taxes, business regulations, and zoning. Many updates had been made to zoning over the years in Casper, Wyoming making it hard to find the current ordinance for a property.

“It was a time consuming job, I had to catalog and edit 300 page files dating back almost 40 years to get the information that is most valuable to local land owners and Constance  Lake and Denise Wyskup provided valuable tools and insight to this project”, Ingrid said.

Now residents can go to the City of Casper’s GeoSmart webpage and click on zoning and find zoning boundaries for the city and county at: You can also click on any zoning boundary and it will pull up all ordinances related to that zoning classification. Ingrid is a local resident of Evansville, Wyoming and is currently a student at the University of Western Florida in Pensacola studying GIS.

Image captured October 2, 2017 courtesy of GeoSmart, showing zoning boundaries and classes for the Casper area.

Sunday, October 1, 2017

GIS 5935 Special Topics Lab 5 VRP

This week we did exercises 7 and 8 from the Network Analyst tutorials on ESRI's help page, these were great for step by step instructions on how to add criteria. We added driver route preferences, overtime rules, breaks, and extra points and depots outside of the regular stores. Here is an example screen shot of my route for Truck #1 highlighted in blue for the San Francisco area.

A vehicle routing problem is complex and involves matching orders, trucks, and drivers to routes within time constraints and costs. It also includes data about trucks, drivers, timing, depots, and allows for constraints or limits to be set to match a workday or a budget or other company shipping goals.

For the final part of the lab we used Florida highways and did a vrp comparison where we changed the parameters for time violations. Allowing time violations (which affected 16 of the routes) on 2 of the trucks improved customer service but increased cost by a few hundred dollars while keeping the revenue the same.