Macao, China

I used approximately 20 control points and a spline transformation to fit this digitized historical map to current day aerial photography of Macao, China.

The map was originally created by James Cook and published posthumously in 1785. I downloaded the map from the David Rumsey collection.

You can also view my georeferenced image as an ArcGIS Online Web Mapping Application.

Paul Revere House

You can also view this data on Google Earth. (Requires Google Earth to be installed on your computer. Download Here!)

Great Chicago Fire

The Great Chicago Fire of 1871 destroyed a quarter of the city, and most of the business district. This map shows the origin and extent of the fire, city boundaries in 1869 and 1890, and surviving landmarks built prior to 1871 and between 1871-1890.

View this data in ArcGIS Explorer Online:

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Project 2 Report Week

This presentation is also available here.
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GIS 5100 Final Project

Presentation
Slide-by-Slide Summary
Process Summary
Graduate Paper

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.

Homeland Security 3: Protect

HLS3 Presentation

Process Summary:

Section 2.1: Prepare Protect Scenario Map 1

Created new ArcMap document using Military portrait template.

Added COUA_MEDS_Boundaries, Orthoimagery, and Geographic Names (GNIS) layers created in previous lab.

Zoomed to extent of orthoimagery layer.

Started editing, and created point for NORAD.  Opened attribute table and named point. Saved edits, and stopped editing.

Selected newly created NORAD point. Created 3 mile buffer around point.

Saved map document as ED_Colorado_2S1.mxd.

Section 2.2: Locate Critical Infrastructure

Selected features from COUA_GNIS file that were completely within the buffer created in section 2.1 (49 features.)

Summarized selected features by Feature_Cl.  Output table: ED_GNIS_Buffer_Features.dbf. added to map document.

Section 2.3: Protect Critical Infrastructure

Selected the one airport from the selected features in section 2.2.

Created 500 foot buffer around airport. Changed transparency of buffer layer to 50%. Labeled features by Feature_Na field.

Added Transportation layer created in previous lab. Turned on primary, secondary and local roads layers. Labeled roads by name.

Selected roads that intersect with the Airport buffer.  Using intersect tool, generated points where Local roads intersected with buffer boundary.

Saved map document as ED_Colorado_2S3a.mxd.

Turned on 3D Analyst extension. Added Elevation layer to map document.

Used Hillshade (3D Analyst) tool to generate ED_HS613074PM raster from elevation layer. Azimuth: 270, Altitude: 39.

Moved orthoimagery layer above hillshade layer in table of contents, and set transparency to 60%.

Saved file as ED_Colorado_2S3b.mxd.

Created new shapefile ED_surveil_pts in results folder. Edited coordinate system to match elevation layer, and added to map. Symbolized as 12 point red triangle.

Started editing, and added 15 points to ED_surveil_pts layer over NORAD layer.  Saved edits, stopped editing, and saved map as ED_Colorado_1S3c.mxd.

Turned off orthoimagery layer.

Using Viewshed (3D Analyst) tool, selected elevation layer as input surface, and ED_surveil_pts as observer points. Saved output as ED_viewshed0, added to map, with transparency 50%.

Added OFFSETA  short integer field to ED_surveil_pts attribute table, and gave each field a value of 10.

Repeated Viewshed analysis, and saved output raster as ED_viewshed10, with 50% transparency.

Saved map document as ED_Colorado_1S3d.mxd.

From the 3D Analyst toolbar, selected Create Line of Sight tool. Observer offset: 10, Target Height: 1. Created line of sight lines with this tool.

Selected one of the lines of sight created. Created Profile Graph from 3D Analyst toolbar button. Saved graph as ED_NORAD_LOS.

Launched ArcScene. Added Elevation layer, selected properties, and checked “Floating on a Custom Surface” button under Base Heights tab.

Added Orthoimagery layer, selected properties, and checked “Floating on a Custom Surface” button on Base Heights tab. Set transparency of layer to 60%. Copied and pasted line of sight lines from ArcMap to ArcScene. (Only one line would paste.)

Saved ArcScene document as ED_Colorado_2S3.sxd. Saved ArcMap document as ED_Colorado_2s3f.mxd.

Washington DC Crime

The DC Metropolitan Police Department is interested in patterns of crime relative to the location of existing police stations, to see if current patrols are effective or if adjustments may be necessary.

This is a basemap of the area showing roads, police stations, census blocks, and incidents of crime during August 2009.

Types of crimes committed were shown in the chart on the above map.  This is an expanded view of that chart.

We were interested in the proximity of crime to police stations.  This map shows buffers indicating 0.5 mi., 1 mi., and 2 mi. from the police stations. Two-thirds of the crime incidents during August 2009 occurred within one mile of the police stations.

It might make sense to put new stations at some distance from existing stations, and near identified areas of crime incidents. Of all possible locations, the southern-most spot, located outside of the Seventh Police District, is most needed.

Drilling down the data further from the last map, here is crime associated with the most proximal police station. This graph shows the number of crimes committed in August 2009, by nearest police station to which the crime incident occurred.


12.39% of the crime during this month occurred near the Seventh Police District.

The DC police department is also interested in patterns of aggravated assault, sex abuse crimes and homicides.

Burglaries occur far more often (by census block population) than sex abuse crimes or homicides.

In the District of Columbia there are drug-free zones within 1,000 ft. of schools. There are additional, mandatory, tough penalties for arrests related to drug crimes (use, purchase, or sale of illegal drugs) in these zones.

King Elementary school had the highest number of crimes (89) occurring within it's surrounding 1000 ft. buffer. Only one of these crimes was a drug-related offense.

Participation Activity - HLS1

Exploratory Spatial Data Analysis Techniques for Examining Urban Crime

The significant costs of crime, both monetary and social, drive interest in the use and development of techniques to investigate and understand criminal activity. Geographic information systems, along with crime mapping software, have proved to be a powerful tool, especially in the area of environmental criminology. Environmental criminology is concerned with determining whether the physical characteristics of an area promote or prevent crime.

GIS has been used to create displays of crime in specific areas, so they could be visually analyzed for patterns and trends.   It provides a platform for which relationships between layers of data can be easily queried, and inferences developed.

The possibilities for the use of GIS in crime spatial data analysis are far greater than just creating visual aids.  GIS can also be a stage for modeling future possible crime in a given area.  Further, the use of spatial statistical methods in analysis makes identifying statistical significant patterns of crime straightforward, and the output reliable for use by stakeholders.

Tornados Participation assignment

Geographic Information Systems and GIS analysts play a key role in the response to a natural disaster.  After a disaster has occurred, they quickly and simultaneously identify locations for decision makers to establish ground command posts and create a base map of the extent of the disaster in the community for these decision makers.  Communicating the scope of the disaster in the form of large visual aids and individual maps to government and public safety officials both on the ground and facilitating the relief elsewhere, is an important step in determining what supplies may be needed so they can start the process of obtaining them.  GIS analysts can also produce information on continuing primary hazards, such as fires created in the wake of a tornado, so that people in harms way can be warned or resources can be deployed to deal with the hazard.

Once this base map has been established, the process of analyzing the disaster area and immediate surroundings begins.  Analysts will put road, infrastructure, and building data into the GIS from before the event.  Because the GIS can be updated as reports of damage come in, the GIS will evolve as a tool in the recovery process too so communities know what will have to be replaced, and how this waste will have to be handled.  In the response process, schools and other public buildings on the edge of the disaster path with the proper access to basic resources can be identified as probable shelter locations.  The roads and transportation information will inform how additional supplies can be brought to these shelter locations and how people can travel to them.  Analysis of the primary disaster area for residential locations will generate paper maps, which can help recovery personnel locate any persons who couldn’t or didn’t evacuate before the disaster, and also avoid infrastructure hazards such as gas leaks from infrastructure damaged.  The GIS will also be used to prioritize reestablishing necessary public services such as hospitals and sanitation/waste disposal based upon how badly damaged the facilities are, how much they are needed, and their proximity to where people are sheltered.

The GIS is ultimately a necessary organizing tool, with analysts as operators, for response to natural disasters and will evolve into a tool to facilitate rapid recovery of the community. 

I created a very basic map of the Joplin tornado path here: ArcGIS Map Viewer.

Tornados!

This is a basemap of Tuscaloosa County, Alabama,  showing roads and schools.

The tornado took a bisecting path across the county.  This map shows a 0.5 and 1 mile buffer around the tornado path, as well as schools and roads located within the buffer.

The recent Joplin, MO tornado occurred in Jasper County, MO.  This is a basemap of the area, showing major roads and the location of schools.

The tornado cut a path in the South-West corner of the county.  This map shows the schools and roads impacted by the tornado, both within the tornado path itself and within a 0.5 mile and 1 mile buffer of the path.

A Quicktime animation of tornados occurring on April 27, 2011 is located here.  The points on the map correspond to tornados reported at times throughout the day on April 27th.

The April 27th tornados can also be viewed on a map in Google Earth.  The KMZ file used for the map shown in this screenshot of the Google Earth application is located here. Unfortunately, just clicking on this link will probably not start the Google Earth application (if the application is loaded on your computer), and will instead give you a 404 error.  Sorry!

Tornado Lab process summaries

Tuscaloosa Lab Process Summary

1.     Examined metadata of provided lab files.

2.     Created geodatabase. TornadoGDB.gdb

3.     Added shapefiles to the geodatabase. (Single/Multiple)

4.     Added aerial photos to the geodatabase.

5.     Created basemap of Tuscaloosa County.

a.     Shapefiles: Tuscaloosa_Cty, Schools, PrimaryRds, Aerial images.

6.     Saved file, exported to JPG.

7.     Copied 1^st .mxd file, and renamed Tornado2.mxd.

8.     Opened file, and added TCLBHM_Path shapefile, and changed name in TOC to Tuscaloosa Path.

9.     Selected Multiple Ring Buffer from Analysis Tools in ArcToolbox.

a.     Input: Tuscaloosa Path

b.     Output: Buffer (results folder)

c.     Distances: 0.5, 1

d.     Buffer Unit: Miles

10.  Clipped schools layer to Tornado Buffer.

11.  Performed location query to see how many schools were located within the tornado path, within 0.5 miles of the tornado path, and within one mile of the tornado path.

12.  Added Census Tract layer to map document.

13.  Downloaded population values by census tract from Census bureau website.

14.  Deleted fields in CSV file, and renamed headings to match attribute table of Census Tract layer in map document.

15.  Added this CSV table to map.

16.  Joined Census Tract layer, and Census Data table.

17.  Performed location query to see how many census tracts were impacted by tornado path. (“Target layers are within a distance of source layer feature”, “0 miles”)

18.  Labeled census tracts by population.

19.  Performed location query to see how many roads were impacted by tornado path. (“Target layers are within a distance of source layer feature”, “0 miles”)

20.  Created deliverable map.  Exported file to JPG, saved file and closed.

21.  Opened new map document. In ArcCatalog pane, right clicked on April27_UWF file in the Tornado/Data/Animation.gdb.

22.  Checked XY settings, and set coordinate system to GCS_WGS_1984.

23.  Exported file to Animation.gdb as Tornado_Paths as feature class.

24.  Set symbology of this layer to tornado symbol.

25.  Added US boundary layer.

26.  Enabled Animation toolbar. Selected Time tab, and changed settings.

a.     “Each feature has a single time field”

b.     Time Field: “Time_DATE3

c.     Field Format: <Date / Time>

d.     Display data cumulatively: checked.

27.  Opened animation controls.

a.     By duration: 20 seconds.

28.  Exported to video with default settings.

29.  Uploaded file to I: drive.

30.  Right clicked layer in ArcMap table of contents, and selected properties.

31.  On HTML Popup tab, checked “Show content for this layer using the HTML popup tool”.

32.  In ArcToolbox, under Conversion tools, selected “To KML, Layer to KML.”

33.  Input the Tornado_Paths file.  Saved output as Tornado_Paths.kmz.  Uploaded file to I: drive.

Joplin Lab Process Summary

1.     Downloaded data from some given sources, and also the Missouri Spatial Data Information Service.

2.     Imported layers into JoplinTornado.gdb, reprojecting them into the GCS_North_American_1983 coordinate system in the database if necessary.

3.     Added all layers to blank map document in ArcGIS, and symbolized appropriately.

4.     Created buffers around the tornado path with Multiple Ring Buffer tool at 0.5 miles and 1 miles from the tornado path as in the undergraduate section of this lab.

5.     Clipped schools within the buffer and tornado path as with the undergraduate section of this lab.

6.     Created two map deliverables; one basemap, and one of the tornado path.