This document summarizes several projects completed by Francis Accardo using GIS and mapping software. It includes: 1. Maps of the NJ Turnpike and Garden State Parkway converted to a subway map style. 2. A map of pre-colonial Senegal highlighting specific features for a professor's book. 3. A 3D model of Rowan University's library recreated using blueprints and measurements. 4. Maps showing suitable habitat for vicuñas in South America based on factors like rainfall, elevation, and temperature using Idrisi software. 5. A project to identify the most remote place in New Jersey based on criteria like distance from roads, services, and

1. Francis Accardo

2. ** For this assignment I was asked to recreate
the two most prominent toll roads in NJ (The NJ
Turnpike, and the Garden State Parkway) into a
“subway” style map. For this I used an open
source image editing program called InkScape.

3. ** This is a custom map made for one of the Professors in Rowan Universities History Department.
The professor was looking to make a map of Pre-Colonial Senegal highlighting some specific features.
This was for a book that should be published sometime in late 2015 by that professor. I used ArcMap
and detailed the map more using InkScape.

4. ** This is a 3D model made using Google
SketchUp. For this project I chose to recreate
the Rowan University Library with a 3D
visualization. The building is made to scale
using blue prints and hand measurements
made in the field.

5. This figure shows the areas in which
vicuña’s would be found (in red). This
map uses constraints to figure out
which areas are suitable for vicuña’s to
live.
This figure shows the areas in which
vicuña’s would be found using a
quantitative color scale; red=greatest
suitability, black= lowest suitability.
Factors of precipitation, elevation,
vegetation, and temperature are all
given equal weights (25%).
This figure shows the areas in which
vicuña’s would be found using a
quantitative color scale; red=greatest
suitability, black= lowest suitability.
Factors weights include that of
precipitation (10%), elevation, vegetation,
and temperature are all given weights of
(30%).
MCE Module with
Unequal Factor Weights
MCE Module with
Equal Factor Weights
Model Using
Only Constraints
** For this assignment I was asked to find the most suitable habitat for vicuñas. Using factors such as
rainfall amount, elevation, and temperature. Using the program Idrisi, I used different models such as
Multi-Criteria Evaluation (MCE) to show the best area(s) for vicuñas to live.

6. What Makes Anyplace “Remote” ?
For this project, groups were instructed to try and find the most remote place in New Jersey
given a number of criteria. The idea was first proposed by on of NFL films producers Ken Rodgers.
Mr. Rodgers has been a NJ native his whole life and had the curious question of what is the most
remote place on NJ. He had this idea for quite some time, then after meeting and talking with the
chair of Geography Department (Dr. Hasse), his idea became more of a possibility. He soon sent an
email regarding all the criteria he thought could be useful in finding the most remote place. What
the class decided made a place remote was its distance from emergency services (e.g. hospitals/law
enforcement), distance from transportation means (e.g. roads), the least amount of human activity
(e.g. population densities/colleges/vegetation indices), and to not have any services (such as water
or sewer services). Basically, the most remote place would be the one in which there is the least
amount of human perturbation.
Many of the variables came from Mr. Rodgers criteria with the addition of some other
variables as well (added by the class). All of the variables were prepared in class, reviewed by the
instructor, then each team was assigned to pick only five of these variables. The variables our team
decided to use are: Emergency Services (which encompasses fire stations/police
stations/hospitals/EMS’s), Large Roads (Highways/interstates/multi-lane roads), Small Roads (all
other roads excluding Large Roads), Population Densities (population via area codes), and Night
Lights Data (data determined by the amount of emitted light off the Earth’s surface). Each of these
variables all we thought were pertinent on finding what exactly the most remote place is. For
example, being away from roads we concluded was very important. If you are not near a road
network, how can you get around ? How would you get supplies ?. . . Help ? All of these question
(and more) are why we used road data as a variable in this model. Another important variable we
used was population size. This was tabulated by zip code. With the assumption that highly dense
areas would not have remote places, we gave a higher “remoteness” to those areas with lower
populations.
In conclusion we found a large proportion of our remote areas in the Pinelands area of NJ,
more specifically Burlington County. This is the area I believe many people thought would show up
as very remote. There is also a large number of remote areas highlighted on the coast of Salem and
Cumberland counties. Upon further review, the areas along the Atlantic Coast were mostly
wetlands (Even those areas highlighted in-between Ocean and Atlantic county). In a future model
these areas may want to be removed. Along the edge of Passaic and Sussex counties there was also
a few spots highlighted. All data can be seen on the next page.
Summary
Variable
How Data was
Prepared
Weight
in MCE
Why?
Distance
from Large
Roads
Anything at least 9 miles
away from a large road
was considered ideal
44.9%
Being away from a large road is ideal, this means no
one can get to or out of that place in a hurry. We
associate large roads with a high possibility of
manufactures/buildings close by as well. This why it
was weighted so high in MCE (Multi-criteria
evaluation).
Population
Density
Zip codes with a
population under 100 was
considered ideal
24.5%
We correlated large populations with high amounts of
residential/urban area, so smaller populations were
the best for our model.
Distance
From Small
Roads
Anything at least 1 miles
away from a small road
was considered ideal
15.6%
Once again, being away from roads we thought was
an important aspect making distance from small roads
be third most important in our MCE.
Distance
from
Emergency
Services
Anything at least 10 miles
away from a large road was
considered ideal
9.2%
Not being able to get help when needed is important,
but for our evaluation we decided it wasn’t as
important. In order to get to these place you need to
use road networks which were given higher weights in
the MCE.
Night Lights
Data
Pixels with a value under
.35 was considered ideal
5.8%
This data ended up being very hard to interpolate so
we decided to weigh this less in our MCE.
*All data prepared in Idrisi
** This project was proposed to find the most remote place in NJ. Using
the program Idisi with a compilation of ‘fuzzy’ factors. I found what I
believe to be the most remote place given the five criteria listed below.
The next page shows a map and final map of these criteria.

7. Distance From
Large Roads
Population
Density
Distance From
Small Roads
Distance From
Emergency Services
Night Lights Data
More
Remote
Less
Remote
The Most Remote Places in New Jersey
¯

8. ** I designed this last project myself after coming home from doing volunteer work in Tanzania. The volunteer coordinator
for the organization had been collecting data from elephant raids (when an elephant strays from the National Park and eats
the crops of local villagers) in the area surrounding Ruaha National Park from 2009-present. The data consisted of GPS
coordinates and other relevant information regarding the raid. I hypothesized that a loss in vegetation has led to more
elephant raids in each subsequent year. The following three pages two pages highlight some of the work done on this
project.
This figure shows all of data for
the elephant raids I had entered.
There are some 1000 points that
I added represented by magenta
circle (AKA Final_Point).
Map of Africa highlighting target
country Tanzania.
Map of Tanzania
highlighting target area
Ruaha National Park.

9. July 14th, 2000 July 10th, 2013July 5th, 2011July 1st, 2004 July 7th, 2006
July 14th, 2000 July 10th, 2013July 5th, 2011July 1st, 2004 July 7th, 2006
Legend for all the five maps above
NDVI Value (interpretation)
< 0 [water]
0 – 0.5 [less vegetated]
0.5 – 1.0 [more vegetated]
** The first five images are the true color images from LandSat satalite imagery of my target area. The five images below
are of the NDVI (normalized difference vegetation index) of these area’s ‘reclassed’ into three values.

10. Gains 23.9 %
Losses 16.9 %
Gain/Loss Swap
16.2 %
Losses
Gains
Swaps
Persistence
Elephant Raids
** To the left, three images of “Gains”, “Losses”, and “Swaps” is depicted. From the previous NDVI images, if an area gained
vegetation (higher NDVI value) persistently through the years depicted it would be classified as a “Gain”. The opposite
would be losses in vegetation, a “Lose”, and if the area gained then loosed vegetation (or visa versa) it was classified as a
“Swap”. To the right is a compilation of the gains, losses, and swaps.
Through the analysis
of the landscape I
found that 75.4% of
the elephant raid
locations experience
no change.