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the spatial analysis and data science
capabilities of arcgis pro enables
sophisticated problem-solving and
advanced scientific modeling this
morning i will explore some of these
functionalities to model the movement of
plastic debris and its impacts on marine
animals and coastal health plastic
pollution in the rose oceans is a global
problem that is impacting humans and
marine animals alike the ocean currents
are looking at form areas of swirling
movement called gyres where the plastic
debris accumulates and creates plastic
islands for this study I will focus on
the Pacific Ocean where extensive zones
of plastic debris accumulation exist
plastic pollution is hard to detect from
satellite imagery and monitoring the
movement of plastics is very cumbersome
instead we can simulate the way plastics
moving the ocean using an open source
lagrangian ocean modeling library that
i've integrated into a python toolbox
this model uses multiple inputs such as
surface winds and ocean currents and
here you're looking at some of the
predominant currents used in this model
and here here is the simulated plastic
particles now I can explore plastic
pollution temporally note that plastic
debris moves at great speeds and it
starts circling at these gyres I will
use density based clustering a spatial
machine learning algorithm and Pro to
detect statistically significant dance
points that correspond to plastic
islands to evaluate the impacts of these
plastic islands on marine life I will
look for patterns in marine animal
movement with respect to these plastic
islands here I have a subset of the
animal tracking GPS data set from animal
telemetry Network this data set contains
a wide variety
of GPS signals for wide variety of main
animals ranging from sea turtles to
marine birds for every animal I
calculate the distance and the angle
that they keep with respect to these
plastic islands throughout their
movement
I used pros multivariate clustering tool
which uses the k-means algorithm to
define distinct movement behaviors with
respect to plastic islands the resulting
box plot shows that there are four
distinct movement patterns for all
species the red and the green are
loitering around plastic islands and
blue and orange show evading behaviors
for animals that try to avoid these
classic islands I can also investigate
movement patterns of a single species
for this purpose I will choose a
long-distance swimmer the leatherback
sea turtle to study its movement I need
a nice scientific library and R is a
great place for that using the R ArcGIS
bridge I create a simple geoprocessing
tool that reads in animal GPS tracking
data and fits a hidden Markov model so I
identify the movement patterns of this
particular species before I feed the
data into the tool I want to make sure
that I don't bias my analysis that
inconsistencies in my data since time is
key for the hidden Markov model I want
to explore the GPS signals temporally
using the time clock and make selections
on time periods where I have more GPS
signals the result of my analysis shows
me areas where the leatherback sea
turtle demonstrates loitering behavior
around plastic islands these are
characterized with extended times of
small displacement and high angular
changes to their movement so what does
what can this mean this might mean that
these leatherback sea turtles are either
feeding on plastics or they might be
caught from plastics from these plastic
islands just like these two leatherback
sea turtles that were rescued by the
Coast Guard finally I will investigate
if these plastic islands can impact the
health of our coasts for this I will use
coastal ecosystem Canaries clams and
mussels that filter the sea water and
unfortunately ingest materials that they
cannot filter out such as micro plastics
toxins and micro plastics can cause
organ abnormalities and mussels to find
a relationship between environmental
factors and muscle organ abnormalities I
will use our new force base
classification and regression tool I
include rosters as predictors such as
temperature and silicate levels and I
include plastic islands directly as a
distance feature for space
classification and regression makes
incorporating different types of data
very easy the very blob try to predict
types of abnormalities have some rare
cases these vary categories can be a
problem for random forests in this tool
I have the option to compensate for
sparse categories to create a balanced
random forest to build the model
incorporating these rare categories the
resulting summary of variable importance
shows me that the variability in
distance - plastic islands correspond to
variability in different type of Muslim
normalities meaning that there might be
impacting muscle health and thus the
coastal health plastic ArcGIS Pro
enables advanced scientific workflows
that is powerful analysis and
visualization tools and seamless
integration it helps us map the pulse of
our planet so that we can understand
what is at stake and what we can do to
help
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