2. Digitizing Data Features
Northshire Properties
Hope, NJ | Aug 2016
GPS Data Collection
Northshire Properties
Hope, NJ | Aug 2016
Raster Data: Slope and Water Flow Direction
Northshire Properties
Mansfield, NJ | Sept 2016
Spatial Analysis Tools
Northshire Properties
Bangor, PA | Sept 2016
Spatial Statistics: Directional Distribution
Rutgers University
Intermediate Geomatics | Fall 2015
Calculating Diversity Index
Rutgers University
Intermediate Geomatics | Fall 2015
Spatial Statistics: Hotspot Analysis
Rutgers University
Intermediate Geomatics | Fall 2015
Raster Calculations: Suitability Analysis
Rutgers University
Advanced Geomatics | Spring 2016
3. 1
Digitizing Data
Features
Northshire Properties
Hope, NJ | Aug 2016
Northshire Properties is a startup
company that hired me to
develop a standardized approach
for creating natural resource
inventories (NRI). The NRIs were
developed as references for
homeowners for property
development and selling
purposes. A land cover analysis is
one map within a series of maps
that highlight natural features
found within a given property.
The target property was a 25
acre residence/farmland that
featured multiple horse
enclosures and stables. I
digitized the map using aerial
images, general remote sensing,
and field observations. The area
of each land cover type was
determined by calculating the
geometry of each polygon, and
the proportions of each section
were included in the legend.
4. GPS Data Collection
Northshire Properties
Hope, NJ | Aug 2016
Another map created for the
target property’s natural
resource inventory was a
hunting-related map. All
properties examined for
Northshire Properties are large-
scale, thus are more likely to
include trails and forested areas.
A hunting map includes the
trails, pathways, and any game-
related features.
I used a Trimble Nomad to
collect data not visible from
aerial imagery. Aerial images,
parcel boundaries, and street
layers were uploaded to the GPS
unit to assist data collection. I
utilized the tracking feature,
collected point data, and
developed a data dictionary to
organize field data. I then
defined the projections for my
point, line, and polygon field
data.
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5. Raster Data:
Slope and Water
Flow Direction
Northshire Properties
Mansfield, NJ | Sept 2016
An important section of the
natural resource inventories is
slope and water flow direction.
Slope categories were defined
according to the SOTER Model
for land development. This map
is meant to act as a reference for
developers to determine ideal
areas for infrastructure.
The slope was created from a
10m Digital Elevation Model
acquired from the New Jersey
Department of Environmental
Protection. The water flow
direction was determined using
spatial analyst tools through
ArcGIS.
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6. Spatial Analysis
Tools
Northshire Properties
Bangor, PA | Sept 2016
Some of the assessed residential
properties included water
features, such as ponds and
streams. While conducting a field
assessment, I determined that
both the pond and stream were
in poor quality. The large
presence of duckweed and lack
of fish were key determinants in
my assessment. Using my
knowledge in ecological
restoration, I determined that
creating a riparian buffer around
both water features will improve
water quality.
The riparian buffer map was
created by digitizing the pond
and stream. After online research
was completed, I determined a
50 foot buffer was ideal for the
location and size of the property.
The NRI included specific details
about riparian buffers and
corridors.
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7. Spatial Statistics:
Directional Distribution
Rutgers University
Intermediate Geomatics | Fall 2015
For a final project, my group
decided to examine crime in the
New Brunswick area. We sought
to identify any distributional
trends in crime in relation to
time of day. Our analysis
showed that the popular
“nightlife” streets, College
Avenue and George Street, were
most prone to crime in both the
day and night times. Results
also showed that time of day
had little influence over the
location of crimes in the New
Brunswick area.
To complete the analysis, I
reorganized all reported crimes
in an attribute table. I then used
spatial statistical tool,
directional distribution, to
examine trends in crime based
on time of day.
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8. Calculating
Diversity Index
Rutgers University
Intermediate Geomatics | Fall 2015
The following map was an
assignment from my course,
which shows the ethnic
diversity within Texas counties.
I acquired race and population
data from the US Census
Bureau. Using state counties as
boundaries, I calculated the
probability that any two people
selected at random will have
different ethnic backgrounds.
Calculations were completed in
Microsoft Excel and the table
was later exported to ArcGIS.
This map required me to
acquire online GIS datasets,
calculate statistical equations,
and work across multiple
software platforms.
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9. Spatial Statistics:
Hotspot Analysis
Rutgers University
Intermediate Geomatics | Fall 2015
The following map was an
assignment from my course,
which examined population
trends in the Bronx. Instead of
identifying areas with a large
population density, I identified
the statistically significant
hotspots of the area. Results
showed that the north Bronx
has intense clusters of low
populations (cold spots),
whereas the western area,
including Manhattan, has
intense clusters of high
populations (hot spots).
This map required me to
acquire online GIS datasets,
determine fixed band distance,
and utilize various spatial
statistic tools in ArcGIS.
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10. Raster Calculations:
Suitability Analysis
Rutgers University
Advanced Geomatics | Spring 2016
For my final project, my class
was told to develop an analysis
to present to the Hunterdon
Land Trust (HLT), an
organization that preserves land
in Hunterdon County, NJ. My
group decided to determine
areas that are most suitable for
the HLT to acquire. I determined
that land cover, population,
slope and soil types contributed
greatly to HLT land acquisition.
The map, in addition to other
analyses, were displayed using
ArcGIS Online Story Maps web
application.
To complete my analysis, I
converted my vector datasets to
raster data. Then, I reclassified
each parameter according to
importance. Using the raster
calculator tool, I created a final
product displaying the
suitability of land for the HLT.ArcGIS Online Story Map Link: https://rutgers.maps.arcgis.com/apps/MapSeries/index.html?appid=6a2b00149f4b4d768c9b8859fac0e67d
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