GIS 4048 Final Project: Conservation Subdivision Parcel Selection

A group of developers requested assistance with locating vacant parcels in Orange County, Florida, that are suitable for the development of a conservation subdivision. Conservation subdivisions reserve about 50-70% of the buildable land for open space and group the homes on the remaining portion. Conservation subdivisions have higher home values and reduced infrastructure costs (including lower stormwater management needs), benefit wildlife, and provide open space to residents (Allen, et al., 2013). It is a win-win situation. The objectives of the project were to obtain a list of suitable parcels, calculate Euclidean distances based on clients' preferences (near major roads and conservation lands, away from airports, energy plants, and landfills), perform an intersection to remove parcels that could not be used for subdivision development, conduct weighted analyses, determine three vacant parcels that meet the criteria, and provide the results (maps, spreadsheet, and report of parcel information with owner contact information) to the clients.

Such an extensive task seemed quite daunting at first. Deciding what to take on as a project alone was time-consuming. This was a good, practical experience as I learned that finding necessary, accurate, and complete data can be quite difficult at times. Having polygons for the parcels instead of a single point for each parcel would have been more informative for the clients. Along the way there were several accidental discoveries about ArcMap's quirks which I hope to avoid with future projects. Although it was extremely time-consuming, I really enjoyed working on this project. I haven’t done any subdivision work for decades and was excited to discover the concept of conservation subdivisions. A PowerPoint presentation describing the project is available here: Conservation Subdivision Parcel Selection

Examples of Output Generated for Clients

Resources:

Allen, S., Moore, S., Moorman, L., Moorman, C., Peterson, N., & Hess, G. (n.d.).  Conservation Subdivision Handbook (North Carolina Forest Service and North Carolina State University Publication No. AG-742). Retrieved from http://www.ces.ncsu.edu/forestry/pdf/ag/ag742.pdf.

Allen, S., Moorman, C., Peterson, M.N., Hess, G., & Moore, S. (2013). Predicting success incorporating conservation subdivisions into land use planning. Land Use Policy, 33, 31–35. http://dx.doi.org/10.1016/j.landusepol.2012.12.001. (Article in its entirety is available at http://www4.ncsu.edu/~mnpeters/documents/Allen_etal_2013_LUP_000.pdf .)

GIS 4048 Urban Planning: GIS for Local Government

The study of local government continued with this lab which was composed of two scenarios.

Scenario 1 involved students as GIS Technicians employed by the Marion County Property Appraiser's office. The county property appraiser's website and online map were utilized to provide information to a local developer who was interested in the impacts a Fly-In Community would have on property owners adjacent to a specific parcel of land in the county. His request for a preliminary zoning report of the site and adjacent areas was met with a PDF map book and a PDF of contact information for the owners of parcels within 1/4 mile of the subject parcel. The map book was created with data driven pages and included an index map. Each page of the map book focused on one particular area that was overviewed in the index map. Although this project had to do with zoning, creating something reminiscent of DeLorme Atlas and Gazetteers was very gratifying. The fact that the pages are so easily editable is very exciting.

After verifying the certification date of the data, the appraiser's website was navigated to develop a familiarity with it. The Marion County Property Appraiser had very detailed information for the subject parcel. (Something that would have made my own dog quite jealous was discovering the assessed value of $6,500 on the property's doghouse.) This particular website allows the buffering of parcels and downloading of data in .csv format. This data was used to add parcel owners' names to the data already provided using Join in ArcMap. The colors for the different zoning classifications were selected to correspond to the actual colors used in the county property appraiser's zoning map. This would develop a familiarity and provide a reference to the client. Each parcel within a 1/4 mile of the subject parcel was assigned a Map Key identifier. These numbers were also used in the corresponding Parcel Report PDF. Compiling the map book involved using this data and a selection of parcels within 1/4 mile of the subject parcel coupled with the zoning information and streets for reference. The index map for the map book was created to identify which portion of the overall map was the focus of a particular page in the map book. Labeling in data-driven pages is something that I found to be more complicated than labeling  layers in a standard map. With more practice that, too, should become second nature.

After completion of the data-driven pages and addition of final touches to the map, the map was exported to a PDF file to be provided to the client. Also provided to the client was a corresponding report of the parcels (identified by Map Key) with parcel ID, owner's name and address, zoning code, and acreage. This report was in PDF form. Generating a report from the attributes will be a handy skill to have. One page of the multi-page map book is included here:

Preliminary Zoning Report - Parcel No. 14580-000-00 and Adjacent Areas
Sheet B4, Page 6 of 12 in Map Book 

The second scenario of the lab exercise involved providing Gulf County Board of County Commissioners with a PDF list of vacant, county-owned parcels greater than 20 acres which they could consider for the construction of a future Extension office. Completion of this task required merging two parcels and then using editing tools to separate out a portion of the new parcel using a legal description. The new parcel's attribute information was then updated along with the new acreages for each of the two parcels. Selecting by Attributes yielded 75 parcels owned by Gulf County. A Definition Query utilizing Query Builder yielded 11 properties of more than 20 acres. Finally, a Vacant-Improved Code (VICD) Table was joined to the layer, and from this three vacant parcels were located. The results were organized in an attribute report which was exported as a PDF to be provided to the Board of County Commissioners.

Urban Planning - GIS for Local Government: Participation Assignment

This final participation assignment involved performing as a GIS technician with a Property Appraiser's office.  The first part involved learning a bit about the way a property appraiser's office manages property data with GIS. This was followed by learning how to display parcel data in a fashion that allows for quick interpretation and comparison of data.

My home county is Escambia County, Florida, which has a property appraiser’s web mapping site at http://www.escpa.org/CAMAGIS/ along with a mobile version available as well (http://www.escpa.org/mobile/map/mapmain.html). The county also has data downloads available at http://www.escpa.org/MapMain.aspx.

June 2014 Property Sales

A search of Escambia County real estate records can be done by specific dates at http://www.escpa.org/cama/SaleSearch.aspx. A recent search for the month of June 2014 revealed that the selling price for the highest-selling property in Escambia County, Florida, in June 2014 was a whopping $6,000 on 6/18/2014. All other properties that were transferred in June 2014 had sale prices of $100.  That covers many different types of transfers, including quitclaim deeds, probate, etc. In Oct 2007 the aforementioned property was transferred by quitclaim deed for $100.

For the property mentioned above, the assessed land value is $4,560. With improvements, the total assessed value is $6,888. Usually I see assessed values lower than recent sales values, but this particular assessment is higher than the most recent sale price.

Sales Records and Assessed Values for One Property

Additional information about this property makes note that the improvements on the land consist of a mobile home. Also, this property had a tax deed transfer in 1999 for delinquent taxes. That this was the highest selling property (at the time I accessed the information) for a whole month in Escambia County is interesting.  Some of the $100 “sales” (quitclaim transfers of property) were because of probate situations; others might have been transfers as the result of divorce settlements.

The second part of the participation assignment involved generating a map showing the assessed land values for a subdivision in Escambia County, Florida. IMHO, showing easements across properties is a good idea in that it indicates the extent of a factor which could negatively impact the worth of a property. Easements may give rights to access, construction, and maintenance of utility lines along with other purposes. Given two otherwise identical lots, the lot which has a utility easement across it should be valued less since the owner would not have full use of that area.

West Ridge Place - Land Assessment Values (2011)
Escambia County, Florida

Several lots in West Ridge Place subdivision should be considered for re-assessment:

• Lot # 090310165 is assessed more than $6,000 higher than all other lots in its vicinity.
• Lot # 090310290 should be re-assessed as well to be more in alignment. Like Lot # 090310105 across the street, this lot is impacted by three different easements. A lower assessed value may be justified.
• If Lot # 090310410 is not an easement of some kind, then it should be re-assessed for being significantly lower in assessed value even though it is quite a bit larger than other lots in the subdivision.

Because there is no road frontage on Mobile Highway or Chester Drive for Lot # 090310420, it appears to be one of the easements mentioned in the introductory material.

To someone who is interested in monitoring the home values of her own neighborhood, much of the experience of roaming around a property appraiser's web site was quite familiar already. I know how much the recent sales were, who owns the vacant house on the corner, who owns the house that's being used as a drive-up drug store (we're not talking Walgreens here), etc. Life in the 'hood. There's always something going on...

GIS4048 Location Decisions – Homing in on Alachua County

This module's lab exercise had students performing as consultants for a professional couple relocating to Gainesville in Alachua, Florida. Their goal is to find a home that is not only convenient to both workplaces (North Florida Regional Medical Center and University of Florida) but also is in a neighborhood of primarily owner occupants in their 40s. To accomplish this required demographic information from the US Census as well as school and medical facility datasets.

Analysis of Potential Home Locations by Different Criteria

With this information, the Euclidean Distance ("as the crow flies") tool first was used to generate distance zones around the hospital and then again around the university. Each of these raster layers was then reclassified to defined intervals to obtain ranked values. Identical color ramps were selected for easier comparison between data frames. The age and ownership layers were generated by adding fields to the applicable attribute tables and then using the field calculator to determine percentages. Again, the color ramps were selected so that the least ideal areas were shown in cool colors and the most ideal colors were shown with warm colors for easier comparison. These four data frames are shown on the first map with 3 census tracts meeting the ideal condition highlighted in each data frame.

The second map is composed of data frames created using the Weighted Overlay tool.  A model was utilized for this part with each factor given equal weight in the model. The usefulness of a model was demonstrated when the imaginary couple decided that they really didn't want to deal with Alachua County traffic after all and would prefer to find a home closer to work. The model was adjusted, giving higher weights for proximity to work and lower weights to the age and ownership criteria.  Changing values like this to accommodate a client's reconsiderations took very little time or effort.

Weighted Analyses of Potential Home Locations

A base map was included as an additional, separate map. After trying to pack all the above information into two maps, it became obvious that a larger format would have been a wise choice. Using weighted analysis is applicable not only to location selection for a residence, but also to determine feasibility studies including suitability of sites for farming, construction of a high-rise or ski slope, and so on.

GIS4048 Module 7: Homeland Security - MEDS Protect

Three Mile Buffer Zone around Boston Marathon Finish Line
with Nearest Hospitals and Finish Line Perimeter Security Checkpoints

Lessons learned from the 2013 Boston Marathon bombing have prompted increased security measures by the Department of Homeland Security. These measures include increased security at ingress and egress points as well as improved surveillance around the event site for monitoring purposes. The first map created for this exercise shows a 3-mile buffer zone around the Boston Marathon finish line, a 500-foot buffer around the finish line, the locations of the ten nearest, currently operational hospitals with emergency rooms, and their 500-foot radius protective buffer zones within which increased security measures prior to, during, and following the marathon can be planned. Fifteen security checkpoints on local and secondary roads leading to the finish line are shown at the outer limit of the 500-foot buffer zone around the finish line as shown on the smaller inset map. Identification verification and backpack checks could take place at those locations.

The second map focused on specific locations suggested for placement of surveillance equipment within the immediate vicinity of the finish line itself. LAS Dataset 3D View and the orthoimagery layer were used to determine where to place the 15 potential surveillance points. Prior to point selection, hillshade was generated for 2:30 pm on April 15, 2013. Shadows impact surveillance equipment's capabilities, and hillshade provided a baseline for the day of the marathon. A difficulty encountered during selection of surveillance locations was determining a set of locations that would provide complete coverage of the finish line vicinity as determined by using the Viewshed tool. Viewshed is an indicator of visibility from other vantage points, in this case, surveillance locations. Believing that cameras placed along roof lines or on the sides of buildings would provide that coverage, initial points were placed in that manner. However, to obtain fairly contiguous coverage, the points had to be adjusted not only vertically but also horizontally. Not knowing the heights of the buildings in the area was a disadvantage, but taking advantage of Google street view as well as researching commercial building heights in general provided some guidelines to estimate reasonable, attainable heights for surveillance cameras. Using 3D GIS techniques to determine locations of surveillance points is significantly more economical, effective, and efficient time-wise than physically selecting, inspecting, and adjusting potential surveillance points.

Suggested Locations for Surveillance Points near Boston Marathon Finish Line

The biggest roadblock that I encountered in trying to assemble a comprehensive security analysis for the marathon came with the inability to complete the line-of-sight portion of the lab. Even with meticulous attention paid to the line-by-line instructions and completing the work in one session (having been forewarned), I could not get a line of sight to show up between any pair of selected points (any surveillance point and the finish line). Redoing the map from the very beginning several times, including completely re-downloading the data again did not improve the situation. This resulted in an incomplete map as a profile graph could not be made for a nonexistent line of sight. Having a profile graph would have enabled a surveillance team to further evaluate potential surveillance points. For instance, the horizontal location of the obstruction can be determined by the aerial view, but how the obstruction possibly could be lessened by the vertical adjustment of the surveillance camera can only be determined by a side view shown with a profile graph.

A 3D version of the map was created in ArcScene using the finish line raster as a surface layer. The orthoimagery layer was draped over it as well. Because I did not have any lines of sight to put in the ArcScene portion of the lab, I added the finishline and suggested surveillance points, hoping that the surveillance points would be placed at their offset heights. That did not happen. Again, the actual line of sights are an essential part of a security analysis like this.

Even though I was unable to complete the lab as intended, the experience was extremely worthwhile. The numerous repetitions of certain steps have helped reinforce key aspects of the lab.

GIS4048 Module 6: Homeland Security - Prepare MEDS

The focus of this week's module was the compilation of a Minimum Essential Data Set (MEDS) to support a standards-based geospatial model for GIS systems as developed by the Department of Homeland Security in the interest of prevention and protection of areas during a time of crisis. Having a standards-based geospatial model founded on a MEDS provides a uniform basis for any community or organization before and during a time of crisis, whether security- or hazard-related. The uniformity of the system ensures that at the time of crisis, the system will be available and useful to anyone involved with crisis planning or recovery. MEDS have already been created for large metropolitan areas through the cooperation of local, state, and federal government agencies.

Data for MEDS are compiled from several different internet sources; shapefiles, rasters, tables, and geodatabases are included. Layers to be included in MEDS are: orthoimagery, elevation, hydrography, transportation, boundaries, structures, land cover, and geographic names. Data must be in North America Datum of 1983 to meet MEDS specifications.

The task for this particular exercise was to identify and put together an essential dataset in preparation for the homeland security crisis of the Boston Marathon bombing. All of the necessary data for this module was downloaded from National Map Viewer and provided. After organizing the map document with a default geodatabase and stipulating the data frame's units (meters), group layers were created to organize the required MEDS layers. Each created group layer was populated with the applicable data layers. The Boston Metropolitan Statistical Area (BMSA) layers were added to the Boundaries group layer. (BMSA includes Boston, Brookline, Cambridge, Chelsea, Everett, Quincy, Revere, Somerville, and Winthrop along with a 10-mile buffer around the outer extent.) The transportation group layer was populated with three road layers (local, primary, and secondary) which were created by using Joins and Relates to join the attributes from the CFCC table to the BMSA Roads layer and then selecting by atttributes to create the three aforementioned layers. The selections were exported data (matching the coordinate system of the receiving geodatabase) and then added to the map as layers which were then adjusted for symbology and labels. Adjustments were made to the Scale Range for each transportation layer as well as the transportation labels so that clarity would reign supreme with any changes in scale.

The Hydrography group layer was populated with three feature classes that had been provided, and Orthoimagery and Elevation group layers were populated with their respective raster datasets. Working with the Landcover raster was quite a lot of fun. After using Extract by Mask to limit the area covered by the raster to only that of the BMSA, the symbology was altered using more meaningful colors imported with a color map created from the National Land Cover Database 2006 Legend. Those colors portray the types of landcover much more adequately than the random colors. Labels for the landcovers were also changed to match the color map labels.

In order to add geographic names from the Geographic Names Information System data file, the text file format was first changed from CSVDelimited to Delimited(|) format to create columns of information. The x,y coordinates of the table were then used as input to create a feature class. After saving the output to the geodatabase, the layer was added to Geographic Names group layer and then projected to the State Plane system (the re-projected layer was also added to the Geographic Names group layer). From this re-projected layer, only the features that were entirely within the 6 counties of the BMSA were selected using Select by Attributes and then Select by Location. This final selection of geographic names was exported as data, added to the map, and moved into the Geographic Names group layer as well before removing the other layer.

The creation of these seven group layers was essentially the staging portion of the geospatial model to follow. In order to make the layers accessible to others who might need to use them, each group layer was saved as a layer file. With the preparation part complete, the next step is to protect ~ the focus of next week's module.

GIS4048 Module 5: Homeland Security - Washington, DC Crime

The purpose of this module was to perform as a District of Columbia Metropolitan Police Department GIS Analyst who is responsible for crime analysis. The goal was to help allocate resources by analyzing January 2011 crime statistics for DC. Specifically, the project involved determining the following:

• whether current patrols are effective as is or need to be altered based on crime patterns in proximity to police stations
• patterns of aggravated assault, homicide, and theft-related crimes

The first map depicts each existing police station's location and percentage of the District of Columbia's total crime for the month. The locations of individual crimes were included for a visual comparison of crimes in relation to existing police stations. Proposed police station locations were based on analysis of crimes in proximity to existing stations, proximity of stations to other stations, and availability of land or existing building for a new station.

Proposed police station locations were based on the data showing that most crimes occur 1/2 to 1 mile from a police station. This was done by applying a multiple ring buffer around each station before performing a spatial join between the crime data layer and the buffers. The 6th and 7th District police stations both had relatively high percentages of crime occuring close to them. Having lived in southeast Washington in the past, I know that that area's reputation for higher crime rates in general is common knowledge to District residents. (One of my roommates there was mugged not once, but twice. In the same night.) The proposed location south of the 7th District police station was selected because of the 7th’s highest rate of crime occurring close to it along with the numerous crimes that have occurred in extreme southern DC south of the 7th District where no police stations are located currently. (A large empty parcel is situated at Mississippi and 10th Place SE). Wherever an additional station to reduce crime around the 7th District is located, it should be east of the Anacostia River to minimize response time.

Also in southeast Washington, the 6th District has a relatively high crime rate occurring near it as well, but perhaps personnel could be transferred from the 6th Substation, 1st District Station, or 1st District Substation, all of which have significantly lower crime rates than the 6th District.

Additionally, DC would benefit from having another station located south of the 3rd District police station (12.56% of total crimes) and west of the Asian Liaison Unit with its 9.1% crime rate occurring in its neighborhood. There is a lot of overlap of police station buffer zones north of the 3rd District station and east of the Asian Liaison Unit, but the areas south of the 3rd District and west of the Asian Liaison Unit have a significant number of crimes and there is no other police station from that area all the way out to the DC boundary. The area of 20th St. NW and L St NW has commercial buildings which might have sufficient space available to accommodate a substation. Percentages of crime, police station locations, and proposed station locations area shown on the first map:

Analysis of Crimes Committed in District of Columbia in January 2011

The density maps of burglaries, homicides, and sex abuse crimes were created using the kernel density method with a radius of 605 (area in square kilometers). The density maps reveal similar patterns to those shown with the previous map: higher concentrations of crime in the southeast quadrant of DC as well as central DC.

Density Maps of Burglaries, Sex Abuse, and Homicides
in Washington DC (January 2011 data)

In general, theft in its various forms, burglary, and robbery were the most common crimes. Because these maps were created by a DC MPD department GIS analyst and are intended for internal use, a locator map was not included. The first map is a bit busy with the locations of individual crimes shown, but seeing those locations of specific crimes helps determine patterns, and the visual aspect reinforces the statistics and written analysis. Some portions of the District of Columbia are noticeably free of crime incidents. Among other reasons, this may be due to those areas being green spaces where fewer incidents of robbery and theft-related crimes would be expected. Another reason is that there are many federal parks (Presidents’ Park, The National Mall, The Ellipse, etc.) in DC which are patrolled by US Park Police. I suspect that crimes handled by US Park Police are not included in the DC police department statistics.

This module was very extensive and very informative. Again, the value of GIS and the wealth of information that can be illustrated with GIS was demonstrated. Throughout this module, much experimentation was done to find optimal color schemes, labeling techniques, layer order, transparency, and so forth to determine the most optimal combination to illustrate the information clearly and completely. A feature that was especially invaluable for this particular analysis was the Identify feature. As in other modules, this experience has left me with the desire to know how to accomplish other things in ArcGIS.

GIS4048 Module 4: Natural Hazards - Hurricanes

Just in time for the beginning of hurricane season, this module presented an opportunity to work as a contract employee of the Federal Emergency Management Agency (FEMA). Of interest to FEMA in this scenario was an inventory of structures with structure damage, wind damage or inundation as a result of Hurricane Sandy’s landfall in New Jersey.

Although only classified as a Category 1 hurricane at landfall on the New Jersey coast, Hurricane Sandy was a catastrophic storm due to its enormous size and other factors which amplified the storm surge. States all along the Atlantic coast were impacted to some degree by Hurricane Sandy. Ten states and the District of Columbia were declared major disaster areas by FEMA, making them eligible for funding from the President's Disaster Relief Fund which is managed by FEMA and other federal agencies.

To meet FEMA’s objectives, the track of Hurricane Sandy was plotted. This was done by adding an Excel file to the map and exporting the data to a geodatabase. The Points to Line tool was used to show Hurricane Sandy’s track. That information is shown along with wind speed and barometric pressure on the map below along with the states and district that were declared disaster areas by FEMA. Graticules were added to this map as well. 4° intervals were selected for the graticules since 2° intervals are too closely spaced and 5° intervals are not as easily interpolated as 4° intervals.

Hurricane Sandy Track and
States/District with Major Disaster Declarations

Geodatabase datasets were created for mosaicking two separate sets of aerial imagery (before and after Hurricane Sandy) and imagery effects tools were utilized for comparing pre- and post-hurricane imagery. Attribute domains were created and used to catalog damage in order to compile an inventory of structures along one block of a street in New Jersey. The difficulty with this part of the exercise was determining the degree of damage. Without seeing elevation views of the structures, the extent of any damage was hard to assess. The following map shows the damage assessment and the pre- and post-imagery of structures which were cataloged along one block. 

Hurricane Sandy Damage Assessment
Coolidge Avenue, Toms River Township, New Jersey (2012)

Information from maps such as these would assist FEMA and other organizations to evaluate the extent of damage after a hurricane and to determine how best to allocate resources.

GIS4048 Module 3: Natural Hazards - Tsunami

The March 2011 tsunami that devastated Japan served as a reminder of the importance of planning for natural and nuclear disasters. GIS provides a comprehensive system for producing maps and graphics to aid in such natural and nuclear disaster planning. This lab assignment tasked students with mapping the evacuation zones around the Fukushima Nuclear Power Point as well as determining the at-risk populations within each of those zones. The lab also included determining evacuation zones based on actual measurements of tsunami "run up" (observed maximum water height above a reference sea level) and identifying at-risk cities, roads, and nuclear power plants in those zones. This information was consolidated in one map:

Evacuation Zones in Vicinity of Fukushima II Nuclear Power Plant

Production of the map began with creating a file geodatabase to keep data organized. Feature datasets were created in the file geodatabase and then populated with imported shapefiles. All of the feature datasets were assigned the same projected coordinate system (WGS 1984 UTM 54N). One Excel file was converted and added to the geodatabase. As it was for several other students, creating raster datasets and mosaicking the DEMs caused plenty of issues. Many thanks go out to Brandon and Aaron for their very helpful discussion board posts which resulted in successful completion of that part of the lab. All of this organized data then was used to complete the second portion of the lab.

The second portion of the lab included creation of another file geodatabase and data creation using selection by location, buffering and clipping, extracting by mask, creating a graph (to determine breakpoints for classes of data), and model building. The model built was very involved and awesome to see in action. The flexibility and ease with which changes could be made to the model were appreciated. Another new technique learned in this lab was how to build label expressions. While many of the processes (especially those used to determine the run-up evacuation zones) are not among those that I use often, with continued practice I expect that they will be easier to use and troubleshoot should the need arise.

Finally, the information was synthesized into one map showing the evacuation zones around the Fukushima Nuclear Power Plant and the evacuation zones for tsunami run-up. This was a lot of information to pack into a single map, but, as the saying goes, "A picture is worth a thousand words". In all honesty, now I am feeling a bit vulnerable about living 75 feet above sea level.

GIS4048 Module 2: Natural Hazards - Lahars

Lahars started off the course’s study of natural hazards. Unfamiliarity with the term lead to some research prior to starting the module requirements. The Javanese word “lahar” used for a volcanic mudflow originated in the 1920s. Its use increased dramatically in the 1980s and 1990s, probably in reaction to the frequent volcanic activity during that time period. In some areas this fast-running mixture of mud caused by rapidly melted snow and ice poses the greatest risk of any volcanic activity. Speeds of 45-50 miles per hour and flow depths of 100 feet where the lahars were confined in valleys have been reported at Mount Rainier. Because of their concrete-like properties, lahars are capable of demolishing most structures. They can also occur during periods when no eruptions are occurring in which case there may be no advance warning of the impending event. For these reasons, the importance of risk assessment and hazard planning is critical. This exercise was created to demonstrate that process.

Acting as a private consultant hired to identify potential inundation zones in the vicinity of Mount Hood, Oregon, the process involved mosaicking digital elevation models and then using the Hydrology Tools to determine drainage flow. Coupling that output with 2010 Census data and Oregon school data, a population analysis and schools-at-risk identification was performed. State and local officials could use this information for hazard planning and response time to help keep area residents and visitors safe from lahars.

After establishing a geodatabase to keep the project organized and efficient, XY tool was used to show the location of Mount Hood. Drainage areas were created from the mosaicked DEMs using some of the Hydrology Tools (Fill to eliminate sinks, Flow Direction to assign direction of flow to individual cells, and Flow Accumulation to calculate how many other cells flow into each cell) to create a stream network. The need for an attribute table for further work called for changing the fill output from a floating point raster to an integer raster. Using the Con tool (conditional statement) reduced the stream network to only those areas with sufficient flow accumulation to qualify as streams. That output was then converted to polyline vector features – areas most likely to become inundated during a lahar. This whole process - creating a stream network from DEMs - was rather fascinating to complete.

Applying buffers to the streams provided areas of potential inundation by lahars which were then used in the selection process with Census population block groups and schools data. This resulted in the areas on which teams should focus for large lahar event hazard planning. From this information the following map was created:

Mount Hood Lahar Hazard Assessment

Populations at risk for being impacted were calculated for the possible inundation zones around drainage basins within the study area only rather than for the entire dataset. The potentially impacted population number outside the study area is much greater than the number calculated and displayed on the map. The values for this map are calculated for the study area only which has a lower population density than the areas to the west, so the block group population ranges may differ from those presented by others. Overall, lahars from Mount Hood have the potential to impact 42,689 people within the study area based on 2010 Bureau of Census figures. Including the area outside the study area 212,791 people could be impacted.

Although it does result in a more cluttered-looking map, especially in areas with small census block groups, the census block group outlines were retained. This was decided primarily to minimize misinterpretation of the map resulting in an underestimate of the actual population with the potential to be impacted by a large lahar.

This particular lab demonstrated the variability in information that could be produced for a client. For example, the lab requirements were focused on lahars, but in reality a hazard-planning team would also want to know what populations would be at risk for other volcanic hazards. In this particular case, one requirement was to provide the number of schools at risk from lahar inundation. However, just outside one of the mile-wide lahar inundation zones was an additional school located closer to Mount Hood. Naturally, one would expect the hazard-planning team to be made aware of this school, but including that information was outside the scope of the lab requirements. This also illustrates the importance of communicating similar situations to a client for further clarification or revision of the data to be provided.

The ability to go back and review previous geoprocessing steps came in quite handy during this lab!

GIS4048: GIS Application of Interest

Land Survey Information System
(http://www.geocommunicator.gov/blmMap/MapLSIS.jsp)

The Bureau of Land Management's (BLM) GeoCommunicator is a GIS application which I find interesting and helpful. GeoCommunicator consolidates GPS data, property descriptions, scanned data and digital data. GeoCommunicator is an interactive publication website for this data. In its earlier form, this included non-BLM surface management lands which have since been removed along with data not managed by the BLM. This application is categorized by Esri as a Government industry (subheading: Surveying). With this application users are able to determine parcel managers, land descriptions, surface land use, and more. For this to be available, GIS was (and still is) used for gathering, managing, and storing survey measurement data from the Public Land Survey System (PLSS) as well as metes and bounds surveys and for making that survey data available to others. Being a seasonal BLM employee (Cadastral Surveying), I have used this particular application in preparation for survey work in addition to using it personally for recreational planning. What I am looking forward to learning more about is the construction of the application itself, how it was originally put together, how it is updated, corrected and maintained, and whether there are any planned changes to the system.

Layers Sample
http://www.geocommunicator.gov/blmMap/MapSiteMapper.jsp

Resources: 

http://www.geocommunicator.gov/GeoComm/

http://www.geocommunicator.gov/blmMap/MapSiteMapper.jsp