GIS at Dickinson College

GIS News, Events and Student Work blog

Author: vandenba (Page 1 of 4)

Vandenburg Poster

Crime Rate Analysis at Hunting Park in Philadelphia, PA

Hunting Park is a community park in North Philadelphia, PA that has a history of crime in and around the park.  In an attempt to mitigate crimes and bring more community members into the park, The Food Trust and The Fairmount Park Conservancy have partnered with the community to undertake a park revitalization.  The master plan was presented in October 2009, and phase 1 began in 2010 and went until 2013.  The Food Trust and The Fairmount Park Conservancy have requested crime analysis to further determine the impacts of the revitalization on the crime rates in the park and surrounding area.

Three types of crime displacement were examined to determine the effectiveness of the Hunting Park Revitalization Project to minimize crime rates in and around the park.  These included geographical and temporal displacement, as well as changes in crime type.  A half mile buffer was established around the park to encompass the park and surrounding community.  Only crimes that occurred within this buffer were accounted for in analysis.  The total number of crimes was graphed from 2006 to 2013, showing a decline overall and an 89% decrease from 2009 to 2013.  Changes in types of crimes were graphed from 2009 to 2013, showing a decrease in prostitution and drug violations, which were prevalent in the park before the revitalization.  A predictive model was created, and a hot spot analysis was completed to show where most of the crimes were occurring and to see if this was statistically significant.  The crime dispersal from the park as well as the eastern and northern neighborhoods is apparent, while there continues to be a crime hot spot to the west of the park.

From analysis of the crime data between 2006 and 2013, it appears that the park revitalization has led to a decrease of crime in the park itself as well as the surrounding community.  The revitalization is still in progress, and further studies are needed to determine how the crimes in the community will change in the subsequent years.  However, this analysis supports the community’s theory that crime has gone down in the park, particularly visible crimes of concern.

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Come to the 2014 GIS Poster Symposium!

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Government Shutdown

The government shutdown is taking it’s toll on the GIS lab.  We’ve had to re-write labs for the GIS class since many of the where we download data are run by the government.  We also haven’t been able to post-process data due to shutdowns of government base stations.  All of us are hoping that we can access this information again soon!  IMG_5315

ESRI Postgrad Career

Interested in working with ESRI after graduation?  Check out their most recent career newsletter:

http://www.esri.com/careers/students/career-newsletter

Mapping Farm Pasture Posts

Two weeks ago, a few GIS students went out to the Dickinson College Farm to collect data on where the pasture posts are located.  This ties in with a project that David Golden is doing for an independent study project, which will eventually create an online program that can map how the animals at the farm move through the pasture during rotational grazing.  There are already control points at the farm (though it did take a long time to find where they were), so we set up the total station and had a lovely Saturday morning full of data collection.

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It’s been a long day in the GIS lab…

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The Presidential Election in Terms of Unemployment and Population Density

Fall 2012 presentation by Kendall Beals, David Cruz, Annie Dyroff, Elic Weitzel

Introduction

In the last eight years, unemployment has become a prominent issue in the U.S. There is also a heavy emphasis on unemployment as it relates to the political sphere, and in particular the presidential elections. With the constant bombardment of statistics put forth in the news and media, it is often difficult to understand the true connections between unemployment and voting trends. This project focuses on gaining a spatial understanding of unemployment as it relates to population density and voting trends. We were interested in identifying whether there is a trend between unemployment and election returns of 2008 and 2011, and whether this is influenced by living in an urban or rural area.

Methods
Data

Unemployment data for both years was obtained from the Bureau of Labor Statistics. Population density data was obtained from the US Census Bureau. Presidential Election data was obtained from The Guardian (http://www.guardiannews.com/) Basic county data was obtained from ESRI.

Analysis

All data was done by county and projected with Albers Conical Equal Area, GCS 1983. Unemployment data was based on the percent of people in the civilian labor force that were unemployed. Change in unemployment was determined by calculating percent change in unemployment for each county from 2008 to 2011. Election returns were sorted by the percentage of votes President Obama received in each county. Population density data was analyzed on the basis of prior classifications as to the number of people in each county that lived in a “urban classified” or “rural classified” area of the county.

Results

This map shows a clear pattern of the central part of the US as containing the most amount of urban areas, with the coastal areas being more populated.

This shows the percent of the population that is unemployed in each county in the United States.  Over the whole United States, the unemployment rate ranged from 1.3-32.0%.  While there is a large range in unemployment, the majority of counties fell within the 1.3-8.9% unemployment range with the average being 5.28%.  By analyzing the map visually, one can see that the Midwest region of the United states appears to have a fairly low unemployment rate compared with the coastal areas of the united states such as California, and the New England States.

There is a clear pattern of the central part of the U.S. voting republican. These areas also align with being the most rural areas of the country. In contrast, areas along the coast tended to vote more democratic. These areas also tend to more urbanized.


This shows that majority of the country has roughly a 1.3 to 10% unemployment. There is a distinct area in the midwest with low unemployment. Other than actually having jobs, this can be due to large amount of farmland or land with low population in those counties.

This map shows the election results for the Romney(Red) vs Obama(Blue) Election. This map shows that most of the coastal areas as well as major cities tended to vote more democratic. The midwest as well as most of central united states showed more diversity in votes and are leaning more to vote Republican.

This shows the change in unemployment in the United State from 2008 to 2011.  The unemployment percentages only range from -0.603-1.78%.  The range is very small because this map shows the percent change in the unemployment percentages for each county.  By visually assessing the map, it appears as though most of the country has had at least some increase in unemployment.  However, the upper middle portion of the United States appears to be a concentration of low unemployment increase.  Looking at the metadata for the counties 3 out of the four counties with the lowest increase in unemployment were all located in North Dakota.  These counties were all located in the area that appeared to be very low in unemployment increase.  Another visual observation about the data is that there is a concentration of high unemployment increase in the area coving most of Utah, Wyoming, and Idaho.

This map shows a selected set of counties with a high percent change in unemployment between 2008 and 2011 (greater than 0.85%) and a high rural population (greater than 95%).  The majority of the counties meeting both of these requirements voted for Romney in the 2012 election (91% for Romney, 9% for Obama).  These results appear to be quite significant.

This map shows a selected set of counties with a high percent change in unemployment between 2008 and 2011 (greater than 0.85%) and a high urban population (greater than 80%).  The majority of the counties meeting both of these requirements voted for Romney in the 2012 election (65% for Romney, 35% for Obama).  These results appear to be quite significant and show a trend contrary to common perceptions about the voting tendencies of urban areas.  It would appear based on this selected data set that a high increase in unemployment had more sway over voters than living in an urban area.

Conclusions

From our analyses, we can deduce much about unemployment and how it relates to population density and voting results. It is interesting that unemployment increased less than two percent for all counties. Unemployment was found to be higher in more urban areas, which are also predominantly democratic-leaning counties. Low unemployment was more common in rural areas, which are also predominantly republican-leaning counties. However, it was interesting to note that there was a slight increase in unemployment in republican-leaning counties from 2008 to 2011. The trends that we can take away from this are that low unemployment correlates with more likely to vote republican and urban tends to be more likely to vote democratic. Also, areas with a significant increase in unemployment are more likely to vote republican.

 

Map Jokes

Need a break from working on final map projects?  Check out these amusing (and very corny) map jokes from gishumor.com!

  • What is the tidiest element on a map? The neatline.
  • Why are maps like fish? They both have scales.
  • Why do paper maps never win at poker?  Because they always fold.
  • What do you get when you cross a cowboy with a mapmaker?  A cow-tographer.
  • What did the mapmaker send his sweetheart on Valentine’s Day?  A dozen compass roses.
  • Why does west longitude need to be cheered up?  Because it is always negative.
  • Why did the dot go to college?  Because it wanted to be a graduated symbol.
  • What do you call a map of outhouses in the woods?  A shaded relief map.
  • What do you call a USGS quadrangle with green water, blue forests, and all the names spelled backwards?  A topo-illogical map.
  • What kind of maps do spiders make?  Web-based maps.
  • What projection do lost sheep use to find their way home?  The Lamb-ert Conic Conformal projection.

Driving Distances Associated with CSA Food Distribution

Claire Persichetti, Spring 2012

Send a geoGreeting!

Want a break from endless hours spent in ArcMap?  Have some fun with aerial imagery with geoGreetings!  The site uses aerial images that look like letters so you can type and send a message.  Check it out!

http://www.geogreeting.com/view.html?yqsCUsCUpExaUX5U#t

Where Did Your Thanksgiving Dinner Come From?

Do you know where your turkey or cranberries came from for your Thanksgiving?  ESRI has put together a map of where the staple foods from your holiday meal might’ve come from.

http://storymaps.esri.com/stories/2012/thanksgiving/#

The Earth as Art

Check out some amazing satellite images!

The Earth as Art: Satellite Images of Our Planet from Orbit

GIS Technician

The Greater Bridgeport Regional Council (GBRC) is seeking a well-qualified candidate to fill the position of GIS Technician. Under the supervision of the Executive Director, the position of GIS Technician provides staff support through the preparation of maps and demographic reports.

Proficient in GIS (ESRI ArcGIS 10.x and extensions). Knowledge of:
o Coordinate systems, cartographic principles, and demographics
o Creating and editing geodatbases & shapefiles
o Experience with AutoCAD is a plus
o Able to perform operations in Access and Excel
o Knowledge of Google Earth (KML/ KMZ files)
o Operate scanners, plotters, and photocopiers
o Operate GPS units (ArcPad experience is a plus)

Skills & Qualifications :

The position requires one year of recent GIS/ IT experience. Proficiency in using the Office Suite of programs (Word, Excel, Access, and PowerPoint) is a must. Strong organizational and communication skills are also required. This is a part-time position, with no benefits.

How To Apply :

Mail or email letter of interest, resume, and pertinent work experience to:

Brian Bidolli, Executive Director
Greater Bridgeport Regional Planning Agency
525 Water Street, Suite 1
Bridgeport, CT 06604
bbidolli@gbrct.org

Position will remain opened until filled.

GBRC is an Equal Opportunity Employer

Geocaching- The Modern Day Treasure Hunt

Geocaching is a world-wide recreational outdoor activity where treasures or “caches” are hidden around the globe, and it’s your job to find them!  The website www.geocaching.com has coordinates for caches around the world.  Just sign up, type in a location to find coordintaes near you, and type the coordinates into a handheld GPS or smart phone.  Then you can navigate to the location and look for the treasure!  Geocaches are usually hidden in a waterproof container that has a log book and a bunch of small items.  When you find it, you can sign the log book and take a treasure (as long as you put something back of equal or greater value).  No two caches are the same- they vary in how difficult they are to find, where they’re located, and what they look like.  There are currently 1.9 million geocaches hidden in over 200 countries and 7 continents, including some around Dickinson!  If you don’t have a smart phone, you can check out a GPS from the media center.  So go out there and find treasure!

Mapping Cape Henlopen

This year, Professor Jeff Nemitz took his Oceanography class on a weekend-long field trip to Cape Henlopen to map different features of the area.  This class goes every couple of years to map the changing features of the beach and wildlife.  Amanda, one of the GIS lab interns, designed a data dictionary on the Juno GPS’s to help the class more easily record data in the field.  The class split up into groups and took GPS data and did experiments throughout the weekend.  Different groups mapped and took attribute data of the shoreline, jetties, spit, tidal flats, dune vegetation, dude tansects, and ghost crabs holes.  Their final projects will rely on an analysis of the mapped field data, as well as comparing the data to maps from previous years.

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