Philadelphia's Mural Scene (1)
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Transcript of Philadelphia's Mural Scene (1)
Philadelphia’s Mural Scene
This project covers a lot of criteria. In this image
are potential sites for new murals. This feature will
be expanded upon in the following pages; however,
this feature is one of many for this project.
Mitchell Walker
ENVS 541
Professor Tomlin
19 Dec 2014
Project Description/Criteria
P 2
The Divine Lorraine has been deteriorating over the years. The
image above was taken in 2004 and the one below in 2010. You can
see this by comparing the two images. However, also depicted is a
that fair amount of development has been underway. Thus,
Develacorp concluded this as prime real-estate, purchased it, and is
in the works of renovating it.
P 3
The city has also offered a tax incentive to Develacorp if they agree to
develop the rather large lot immediately behind the Divine Lorraine.
However, in order to receive this tax-cut, the group must develop a
revenue generating establishment that will greatly benefit the community.
They have met with several organizations, but have found the proposal
offered by the Mural Arts Program most appealing. The proposal consists
of three features:
1) to build an Urban Arts Museum
2) to organize the inner city youths, living in the
communities surrounding their building, to
photograph and name existing murals in need.
3) to find areas to pain 100 new murals by 2017
P 4
Above, in purple, is a drawing
of the new Urban Arts
Museum. The perimeter of
this lot is over 8 times larger
than that of the Divine
Lorraine—which is quite
large. A couple of near by
buildings were used as
templates to create the
shape of the new
Museum, both of
which are labeled to the right.
Above is a completed drawing of the
Urban Arts Museum, placed
immediately behind the Divine Lorraine.
This drawing consists of the actual
structure (in purple and aqua), a large
stairway, and courtyard surrounding a
fountain. The goal is to fill this museum
with every mural in Philadelphia—
roughly more than 3,600—along with
other works that will hopefully become
murals on sites identified by our group.
P 5
Using Regression to Find Ideal
Locations to Paint Several
New Murals
P 6
In the center of the above map is the development
site for the Urban Arts Museum. The yellow circle
surrounding it represents a mile out from the exact
center of the site. The eight regions—overlapped by
the mile sphere to varying extents—are zip code
areas. These areas originally contained the
communities of our interest—due to their proximity
to the development site; however, due to our finding
very few potential mural sites in the loser regions, we
reduced our search to the top four zip code areas.
Above, in blue, are 8,565 buildings within the
(original) region of our focus. To find what
buildings are ideal for murals, and where to
paint them, a considerably extensive
regression was formulated. Ultimately, we
narrowed our selection down considerably by
means explained on the following pages.
Buildings
P 7
To the right are vacant lot and yard areas
colored—appropriately—green. These areas
were taken from a raster layer which created
these area shapes based on their color captured
from a photograph. As you can see in the
baseball field (right; top) the only thing
separating the significance of the diamond and
backfield from the infield is the color contrast,
of which the photo to raster deemed as
separate. Regardless, the green areas of this
photo works sufficiently for the task at hand
because our group is trying to identify vacant or
open lots in the vicinity of buildings (preferably
houses).
The reason is that these vacant areas could
potentially offer space for the mural artists and
youths in our program to work and, when
completed, offer a clear line of vision for
pedestrians and commuters passing on, and
near, the area’s respective roadway. The ideal
vacant area would be something like the areas
circled in the center of the image because both
lots are near to two potential mural sites (i.e.
between two walls), vacant lots, and a road.
P 8
Moving forward, below is a depiction of the model built to perform our regression. We
began with centerlines, buildings, and lots all clipped to our (zip code) areas of focus, along with
buildings, we have found to be the ideal size, with a perimeter of 200-1,000 feet (orange; right).
From here, a buffer (= to 1 ft) was used on all our ideal sized buildings and the vacant lots, which
was then intersected, placing a padding on our buildings to place a mural site. A multilayer buffer
(of 30 ft in purple-50 ft in orange dashed marks; both right) was then used to erase insufficient pads
from our buildings, which then placed on our buildings the potential mural sites (highlighted in
aqua). Separate multilayer buffers (30-32 ft and 46-50 ft; both in black set to 50% transparency, and
also right) were then used to erase parts of these pads, separating them from the ideal site locations.
This was used to separate pads that ran around the front or back of our buildings. Any residuals
were then either selected and erased by the original buffer from the buildings or one of the various
multi-buffers selected/used. Additionally, any ideal building within a 20 ft buffer from another—
non-ideal—building (white hash marks), or 20 ft from another mural site (orange hash marks), were
not selected in their attribute table and removed following the use of a clip tool in which said area
was not included. Lastly, any sites that didn’t meet a certain area size requirement were then
selected out.
P 9
This regression successfully reduced the original
1,360 ideal sized buildings (in yellow; left) to the
378 ideal mural locations high-lighted in aqua
below. These mural locations are better observed
in the portion of the map blown up at bottom
center. I removed the buffer from the mural
pads merely to make them more visible. As you
can also see, there are not many such locations in
the lower region of the map, leading us to then
only use the top four zip code areas (19121,
19122, 19123, and 19130).
P 10
Legend
! Photo Needed
! Already Have Photo
Now, because we are trying to get our youth
program off to a productive start, the next part of
the project is to find the area most suitable to focus
on first. To do this, we wanted to find which area
has the most murals that have yet to be
photographed. Also, because many of the murals
have still not been given titles, we feel that this issue
should be corrected, and that the children from the
actual areas should participate in naming this pieces,
as well as helping us take the photos that will then
be displayed in the museum. Directly above is a map
portraying actual murals geocoded by their address
and zip code. However, because this does not
include those not yet named I scored the murals
based on their current state: titled and
photographed = 1, photographed or titled = 2, and
neither being equal to 3.
P 11
Because the vast majority of the murals do not have either a title of a photo the interpolation of
the score is in reverse, showing that 3 of the four have very few, if any, murals with titles or
photos of them. As a result, all we know is that the one zip code area substantially pierced by the
interpolation—which is also the area where the museum is to be built—is the one area we should
most likely avoid. Still, because the remaining areas have so many murals that need our attention,
more calculating was needed to decipher which area will give us the most murals to work with.
P 12
Score
194
102
50
64
Mural Score per Zip Code Region
19121 19122 19123 19130
190
180
170
160
150
140
130
120
110
100
90
80
70
60
50
40
30
20
10
0
With that, we spatially merged the
highest scoring murals to their
respective zip code regions,
followed by the second highest
scores, and so forth. We also
joined the count of the potential
mural sites, so that our youth
program team can also seek out
these sites in person. It would be
kind of like a scavenger hunt—just
a lot more interesting. Regardless,
the end result leans strongly
towards zip code region 19121,
which is the region we have
decided to begin our youth
program outreach. P 13
The Potential Liabilities our
Program Might Face Due to Crime
P 14
Crime by Season
! Fall
! Spring
Because we were concerned about safety, first and foremost, we began researching crime in this area long
before we knew that we would be focusing on the one zip code area. Hence, all the original zip code regions
are back. Still, we thought that it would be interesting to see what the annual crime looked like in all of these
regions for reference purposes. Additionally, because our program will only be conducted during the Fall and
the Spring, we filtered all crimes that occurred in the alternate seasons. The result is below and, at first
glance, it might look rather alarming—regardless of my attempts at softening the map’s imagery with spring
and fall colors. Still, this is over the course of a year, so lets investigate further before we conclude that our
youth program is too big of a liability.
P 15
So because Fall (top) and Spring were quite
similar after using the Kernel Density tool,
I ran them again via Kernel Density, only
together this time—producing the map top
center.
I also buffered the points and
spatially merged them on to
centerlines that were also
buffered. After adjusting the
map’s symbology, to produce the
join count, the map above
emerged.
To the right is the top (right) map
again, but with its base-heights
adjusted. To the left, its extrusion is
based on its count value.
1.
2.
3.
4.
5.
P 16
I also took the map portraying the crime for both Spring and Fall (on the previous
page) and converted to a TIN (via Raster to TIN) to produce the map directly left.
This was an essential step to take in order to portray this map showing ramped
sidewalks and roads based on the crime in their respective areas, below. Essentially,
by using this TIN as the base-height setting it anchored the floating base-height
“bands”(depicted in the 4th from the previous page) to the ground—while keeping
the extrusion set to the count values. Ultimately, as you can readily observe, the
only red ramped roads/sidewalks (i.e. the high crime areas) are ironically located
around City Hall, far from the zone we are interested in starting our youth Mural
Arts Program (the zone in pink directly below).
Number of CrimesReported
0 - 2
3 - 9
10 - 23
24 - 57
58 - 113
P 17
However, if the heinousness of the crime is factored into our risk assessment, our challenge
becomes more considerable. As you can see, the zip code area we chose to begin our youth
program has the most cases of high crimes (e.g. namely thefts and assaults with fire-arm) near to
the murals we need to photo and/or name:. Still, because our team is focused on improving
situations, and not running from them, our chosen region is still ideal for our mission. Still, some
considerable alterations regarding our strategies to retrieve our mural photos must be made.
Distance from High Crimein feet
! -1.000000 - 462.360656
! 462.360657 - 1258.932979
! 1258.932980 - 2067.138280
! 2067.138281 - 2761.775637
! 2761.775638 - 4336.597682
P 18
Data Cited
Buildings. GIS Service Group. City of Philadelphia. OpenPhillyData. Shapefile. 1 Dec 2007.
Web. 20 Oct 2014.
Crime Incidents. Philadelphia Police Department. City of Philadelphia. OpenPhillyData. CSV. 12 Dec 2012. Web. 14 Dec 2014.
Curb Edges. Department of Streets. City of Philadelphia. OpenPhillyData. Shapefile. 1 Jan 2012.
Web. Oct 2014.
Mural Addresses. Mural Arts Program. Philadelphia NIS Mural Base. Web. 10 Dec 2014.
<http://apollo.cml.upenn.edu/murals/mbQueryRequest.asp>. Nov 2014.
My Map, ArcGIS. ESRI. Web. 11 Nov 2014
Philadelphia 2004 aerial photography Area 3 and 4. City of Philadelphia. Pennsylvania Spatial Data Access. 2004. Web. 8 Dec 2014.
Philadelphia Land Cover Raster. Parks and Recreation. City of Philadelphia. 19 Oct 2011. OpenPhillyData. Web. 12 Dec 2014.
Philadelphia zipcodes. City of Philadelphia. Pennsylvania Spatial Data Access. 2012. Web. 8 Dec 2014.
P 19