Wednesday, July 26, 2017

Module 9 GIS For Local Government


This week we looked at property appraisal website to locate data and information about a particular parcel. Then we addressed a client's requests to create a pdf map book. The target parcel was the client's parcel and the surrounding land use is of concern when determining the possible effects that a fly in community could have on the surrounding areas.To create the map I used the grid index tool to create a grid index on a series of pages in my map layout. Viewing the data this way can provide more detailed information about the parcel and surrounding areas. I also symbolized the various zoning codes in the surrounding areas. Then I added a locator in set map. I added the parcel layers and the index layer, copy and pasting the index layer, I set one to be the page reference. This can be done under layer properties, query definition tab when data driven pages are enabled. I edited the map layout and added essential map elements. Last, I exported the finished map as a PDF. 
Next we created a Zoning report for the map. To do this, I opened the qtmi_parcels table options, then create report. From there I was able to choose which attributes to add to the report, select the format, and export the report to a pdf to be given to the client.
I learned a lot about tasks and methods utilized by local government. Thanks for reading and have a great week!

Friday, July 21, 2017

Urban Planning Participation Assignment


For this assignment we researched some property appraiser sites to find property value and information, then created a map of a subdivision plot using Arcmap, exporting the necessary easements, and creating a deliverable map of the subdivision.

Q1: Does your property appraiser offer a web mapping site? If so, what is the web address? If not, what is the method in which you may obtain the data?
2.       Q2: What was the selling price of this property? What was the previous selling price of this property (if applicable)? Take a screen shot of the description provided to include with this answer.
Sale price 630,000. Last sale price: 90,000

3.       Q3: What is the assessed land value? Based on land record data, is the assessed land value higher or lower than the last sale price? Include a screen shot.
110,000, assessed land value is higher than the last sale price. See screen shot above.
4.       Q4: Share additional information about this piece of land that you find interesting. Many times, a link to the deed will be available providing more insight to the sale.

4.The property is located across the street from the waterfront on the sound side of Pensacola Beach.

Q5: Which accounts do you think need review based on land value and what you’ve learned about assessment? Please answer this question within your blog post.
The land values for the plots symbolized in blue and yellow seem way out of line with the other land values. Some of these are due to location or size, inaccessible from the road or plot does not meet minimum building requirements. But all should be assessed.


Friday, July 14, 2017

GIS Day Celebration

Celebrating GIS Day
For my GIS Day event, I decided to celebrate by sharing some of my Summer GIS projects from my Applications in GIS course with family and friends. My family volunteers with an organization called Family Promises and since it is our week to participate, I also got to teach a few of the kids and teens about GIS work and the value of GIS in our daily lives. I did so at my parent’s house, I gave a short talk about GIS including natural hazards and preparedness (the focus of my current course), then I individually showed everyone some maps and topics that I have been learning about. I may or may not have had to bribe them with pizza but in all everyone was really interested and excited to learn about the topics. My biggest fan, a.k.a. my 3-year-old is enthusiastic about maps. He was especially excited to help me share.



Thursday, July 13, 2017

Week 7 MEDS Protect

Week 7 MEDS Protect 

This week we analyzed the hazards and potential threats surrounding the Boston Marathon finshline. For the first map we located critical infrastructure. I created a buffer layer and performed analysis to locate the 10 closest hospital locations as well as a 500 foot buffer around the finish line and each of the selected hospital locations. Next, I created a buffer layer to establish security checkpoints at intersections surrounding the finish line. Analyzing these infrastructure and security data can help establish a faster emergency response saving lives in an emergency situation.
For the second map,  I used lidar data to generate a hillshade and viewshed layer from a set of surveillance points surrounding the finishline. I then used the line of sight tool to create a line of sight from each point and edited the location and elevation of each point until I had a layer of surveillance points with relatively unobstructed view. Next I generated a line of sight profile graph for point 14 on my map. Last I added the data to Arcscene and set the base heights for each layer. This created a 3D scene of the finishline area to work with. By selecting the line of sight points, I was able to copy and paste them into Arcscene to create a 3D view of the surveillance area. Thanks for reading and have a great week.

Sunday, July 2, 2017

MEDS Prepare Lab


MEDS Prepare Lab Week 6

This week we learned about Minimum Essential Data Sets (MEDS) developed by the Homeland Security Infrastructure Program. The Department of Homeland Security sets goals of promoting preparedness through three areas: "The quality, accuracy and currency of all types of data about a place, and geospatial analysis using GIS to provide situational awareness at all stags of homeland security operations". Geographic data within the MEDS is grouped into 8 categories, including Transportation, Boundaries, Hydrography, Orthoimagery, Structures, Geographic Names, Land Cover, and Elevation data. 
To get started creating the above data set I created the layer groups for each category. I added the boundary and transportation data. For the roads layer, I added the cfcc codes by performing a table join. I then used select by attributes to export the roads data into 3 separate categories for secondary, primary, and local roads, and I specified a minimum scale for each category. I added the hydrography data layers and the land cover raster. i used the extract by mask tool to clip the land cover raster to the extent of the boundary layer, and I imported the colormap for the land cover raster. Next, I added the orthoimagery and elevation rasters, Then I added the Geographic Names group, I viewed the boundary layer county field to see which counties were in the study area and used select by attributes to select them and used the select by location function to select the points within the boundary. I exported the group layers as layer files, this makes sharing easy for others to access the layer. You can use relative paths to each layer's data source, the layer will draw exactly as saved as long as there is access to the data referenced by the layer. This weeks lab was challenging and I ran into many issues involving names and trying to run ArcCatalog funtions with the data open in Arcmap which would result in an error. It is a best bet to save your mxd and close Arcmap before attempting to edit data in ArcCatalog. Thanks for reading and have a great week.

Thursday, June 29, 2017

Washington D.C. Crime Analysis



This week we performed a crime analysis for Washington D.C.. For the first map we created a multiple zone buffer layer and performed a spatial join to add the crime data. Then we added a percent field to find the percentage of crime in each zone and created the buffer zone graph. I then added the percent field and performed the spatial join on the police stations layer, to symbolize the police station by proximity to crimes using graduated symbols. 
For the second map we used the kernel density tool to locate crime hotspots. I added 3 data frames and select by attribute query to select the individual offenses, then ran the kernel density tool to create the crime hotspot outputs. I set the transparency to 65 percent and added the block groups layer to the background and symbolized it by population density. You can see that most of the high population areas have corresponding crime hotspots. Thanks for reading and have a great week.

Tuesday, June 20, 2017

Hurricanes: Tropical Storm Sandy Damage Assessment


This week we studied hurricanes and created damage assessments for properties in Ocean County, New Jersey. First we created a map tracking Hurricane Sandy. From the Tracking points table, we created points on the map and symbolized them based on the storm category and included labels showing wind speed and barometric pressure. I also included an inset map of states affected by the storm. Next, we created a damage assessment, including pre-storm and post-storm imagery. I edited the domains for the DamageAssessment geodatabase and coding for the severity of the damage. Utilizing the swipe tool, I compared the 2 images and added points for each parcel on the post-storm layer and chose the appropriate structure and wind damage and inundation. I symbolized the points from destroyed in red, to minor damage in yellow. Then I added the 2 locator inset maps for reference and added the damage assessment table to the map as well as essential map elements. This week's lab was challenging and very informative section in our natural hazards studies. Thanks for reading and have a great week.