Location Decisions

For this lab, we were asked help a set of retired grandparents choose a location in Alachua County, FL to move to so they could be closer to family.

They had several criteria about where they would want to live.  The grandparents wanted to live:

• Close to family and their grandkids
• Close to the North Florida Regional Medical Center (NFRMC)
• Close to the University of Florida
• In a neighborhood with a high percentage of people 65 years old and up
• In a neighborhood with high home values
• Close to bus routes

Based upon these criteria I generated these maps and a report for the grandparents.

This is a basemap of Alachua County, FL, with roads, census tracts, places and public lands indicated.

I created maps showing the physical location and distance surroundings of the landmarks important to this couple.

I also visually summarized house value and age census data by census tracts.

The tough part of this lab was creating overlays to combine data to determine which census tracts best met the grandparents criteria.

I summarized all my analysis in this report for the grandparents.

Local Government Lab

Deliverable 1: EIA Layers highlighting parcel #14580-000-00 from MCPA.

Deliverable 2: Base map of parcels and property owner data for surrounding parcels.

Deliverable 3: Map Book displaying zoning of parcels adjacent to parcel #14580-000-00.

Process Summary:

1.    Went to http://www.pa.marion.fl.us/ and searched for parcel 14580-000-00.

2.    Created a 1 mile buffer around this parcel to show all surrounding parcels within that 1 mile.

3.    Exported information to CSV.

4.    Added EIA Layers:

a.    Zuko Parcel

b.    Soils

c.     2009 1ft Aerial Imagery

d.    Water

e.    Topo USGS Elevation

f.      Streets

g.    Historical Lots

h.    County Zoning

5.    Saved image of map for deliverable 1.

6.    Created blank map and named ECD_Parcels.mxd.

7.    Added Parcels feature class from provided data geodatabase.

8.    Joined Parcels layer to CSV data downloaded in step 3. Output layer: Parcels_Join

9.    Selected parcel #14580-000-00, and exported to new layer Zuko_Parcel.

10. Selected by location all parcels that intersect the Zuko_Parcel.

11. Exported selection to new layer ADJ_Parcels.

12. Added Zoning layer from provided data geodatabase. Clipped zoning layer to ADJ_Parcels and added output layer Zoning_Clip to map.

13. Using Intersect tool, intersected Zoning_Clip with ADJ_Parcels, and added output layer Parcels_Zone to map.

14. Added short integer field MAPKEY to attribute table of Parcels_Zone. Used field calculator to number each record.

15. Created base map for deliverable 2.

16. Saved map document, and saved again as Zoning_DDP.

17. Under Cartography tools and Data Driven Pages in ArcToolbox, chose Grid Index Features. Input features: ADJ_Parcels, checked Use Page Unit and Scale: 12000, saved output as Index12000.

18. Selected Zoning (from Zoning layer) that intersects ADJ_Parcels. Exported to layer Zoning_Index, and symbolized.

19. Added parcels and streets layers and symbolized.

20. Created layout so data frame did not conflict with other map elements.

21. Turned on Data Driven tool bar. Selected Data Driven Page Setup.

22. Enabled Data Driven Pages. Index layer: Index12000. Extent: Center and Maintain Scale.

23. Inserted dynamic text to number all pages in document, and dynamic text for date saved and author.

24. Created locator map and symbolized.

25. Exported map book to PDF for deliverable 3.

Local Government: Participation Post

http://www.esri.com/library/whitepapers/pdfs/gis-for-fire.pdf

ESRI published a white paper discussing how GIS technology can optimize fire department emergency response. Because the time between ignition of a fire and the start of fire suppression is directly related to the amount of loss due to fire, analysis of fire and medical response times to emergency events is crucial. This response time is also identified as one of the most manageable time frames (compared to dispatch, turnout, access and setup time frames). Color map examples in this article illustrate how GIS can be used to analyze fire station placement and coverage, and there is also a brief discussion of how GIS may be utilized as a major component of the emergency dispatch system.

I picked this paper for my post because it recognizes the role of the community in fire department response.  On page 11, this question is raised: “What are the current community expectations for fire protection and emergency service delivery?” In addition to the discussion of national time standards for response in this paper, there is a list of ways the community establishes their own response time/travel time standard for response on page 9:

Some of these are (1) the use of historical fire and EMS response data, (2) demand for service, (3) the level of care that the community wants to provide, and (4) the level of care that the community is able to afford.

Regarding item number 2 (demand for service), I believe this is a huge opportunity to use GIS to give local governments insight and feedback in terms of how well the community thinks emergency resources have been allocated.

This map gives the locations of fire department stations in Naperville, IL, and identifies 1 mile buffers around them.

Local government could use it in discussions with citizens regarding the construction of additional fire department stations.