Lab2

Q1: Why does it matter what datum your waypoints are recorded in?
The datum provides a point for which geospatial locations are referenced, and describes the origin and orientation of coordinate systems used to identify specific points on the earth’s surface. The datum is critical for determining how well the recorded data set will match up with the preexisting data.

Q2: What coordinate system are the campus data layers in? What coordinate system is the GPX file you added in?
The coordinate system of the campus data is:
NAD_1983_HARN_StatePlane_Oregon_South_FIPS_3602_Feet_Intl

The projection used is:

Lambert_Conformal_Conic

The coordinate system of the GPX file is:

GCS_WGS_1984

Q3: What distance did you use to buffer the call boxes and why?
I used a distance of 60ft to buffer the call box. Coming to this decision required me to think both about my personal opinions regarding safety, while also thinking about the greater UO campus population as a whole. In terms of mitigating crime and discouraging illicit behavior, 60ft maintains visibility of the box from multiple angles and directions. Additionally, for the victim, the box is close enough to run to if immediate help is needed. I don’t think any value less than 60ft could include the complete efficacy of the box, however, as distance increases (>60ft), the crime is increasingly disconnected and concealed.

Q4: What operations would you perform if you wanted to identify areas of sidewalk that were outside both call box and streetlight buffers?
A join of the streetlight and call box buffers will fully encompass areas that are both unlit AND outside call box safety.

Q5: What areas seem adequately covered by both streetlights and emergency call boxes? Where would you recommend the University install additional lights and call boxes?
Unfortunately, there are not a lot of areas that are covered by both streetlights and emergency call boxes. The safest areas are located around the intersection of various walkways and main entrances of buildings. On a more general level, the South-Western portion of the survey area (around HEDCO and the Music Building) offers safer walk areas, followed closely by the North/North-West portion of Campus. The high concentration of streetlights and call boxes could be a result of the more recent construction of the buildings in this area, compared to central campus around the Student Rec. Center and Straub Hall, which seems to provide the lowest level of safety in terms of lighting and call box accessibility. I would recommend the University install lighting around these aforementioned buildings, around the EMU, and in the North-Western portion of campus near Lillis and McKenzie.
Q6: What are some issues you see in the available crime location data? What compromises or judgment calls do you have to make when entering data of this nature? Why do you think this is so for this particular data set?
The first issue I faced is the location of crimes inputting as “On Campus”. Unfortunately, I could not include these in my final representation because the location was too vague and I did not want to falsify the data by randomly assigning a point on the campus map. This is an issue because these crimes are essentially mute and ineffective for my analysis. Additionally, it is difficult to determine what is “on campus” or not, when many of the crimes occurred in University sponsored areas or locations, but not specifically within the boundaries of ‘campus’.  Nonetheless, I think these crimes are still significant and should be noted. When entering the data, I had to make judgment calls based on the ‘type’ of crime committed. Though we were instructed to follow parameters that would filter harassment and theft crimes, some cases were not as well defined. For example, “attempted theft” or “stalking” are two crimes that are contentious and could arguably be included or excluded. With the intent of being thorough, I included any dubious crimes.  The reason the classification of both location and crime type can be problematic is a result of the lack of consistency in data input. There were probably many different officers working on each case and without a clearly defined categorization system, the data presents minor instances of ambiguity.
Q7: What information would make your report more complete? Can you think of any other data that might be important to include that you did not or were unable to? Include a discussion of the shortcomings of using buffers alone to identify areas of concern 

It would be interesting and useful to include more information regarding the use of the call boxes. I would like to know, how often a call box is used and how many times a call has been an effective tool for criminal mitigation and response. Additionally, to draw a more secure and comprehensive connection between crime and streetlights I would like to have data regarding the actual time of the crimes and the timing of the streetlights. Do the streetlights turn on automatically? Does their timing adjust with seasonality?
Using buffers alone to identify areas of concern is effective for providing a foundation of understanding.

The buffer gives us an idea of the extent of illumination, however, visibility is can be impaired by other factors such as the weather, and the streetlight may not shine as brightly under certain conditions. Additionally, we assume that the lights are consistently shining at their full capability, but if the bulb is dying or inoperable, the streetlight is useless. The main issue of the buffer in relation to the call box is a result of the assumption that all points within the buffer zone are created equal. I implemented the 60ft buffer in consideration of accessibility and visibility of the box, however, this is under the assumption that the box is completely unobstructed from all locations within the buffer zone. Perhaps there are trees or a bike rack and the box may be completely hidden from view or difficult to reach in time of an emergency.

Overview Map of Campus Concerns

The map indicates a high concentration of crimes occurring within central and the north-west portion of campus. Correlated with lighting and crime box data, the crime locations are aligned with the areas on campus that may be considered the most risky. These areas are poorly lit and do not have many opportunities to access a crime box within 60ft of any given location. The south-west area of campus provides the safest walkways and unsurprisingly, there is the lowest crime rate in this region. To further mitigate campus crime and increase the safety for pedestrians, it would be prudent for the school to introduce more call boxes within the aforementioned risk areas on campus. In terms of lighting, the campus is generally well-lit, with a some deficiencies in the northern portion of campus.

1) Lit Areas

Using a 30ft buffer radius, the following maps illustrate the illumination provided by streetlights throughout campus. There is adequate lighting surrounding entrances of buildings, and supplementary lighting aligned with major campus walkways. Insufficiencies are illustrated in the sidewalks that are not illuminated and occur in courtyards, enclosed spaces (especially in the northern areas of campus) and on the west side of the recreation center.

Walkway Lighting for Safe Sidewalks
(Determined by Buffer Zones)

2) Call Box Accessibility

Using a 60ft buffer radius, the following maps illustrate the accessibility of call boxes throughout campus. Location of call boxes seems to be concentrated along the external boundary of campus, however, there are few call boxes within the central region of campus, in between buildings, along the street/walkway to the south of Straub/north of rec center,  and along the western walkway of the rec center/court.

Call Box Accessibility for Safe Sidewalks
(Determined by Buffer Zones)

 Aggregated Map Based on Lighting AND Crime Box Location

3) Crime Occurrence   Crimes, Lighting, Call Boxes and Sidewalk Safety
(Determined by Crime Location and Buffer Zones)