Spatial analysis is how we apply The Science of Where to ask questions, solve
problems, and make smart decisions. This morning, I want to share with you what's
possible when we integrate science into planning and design to tackle the
practical problems we face, taking advantage of the hundreds of spatial
analysis tools at our fingertips. The city of Boston, for example, has a
bustling bike share system that they plan to expand by over 50% in just two
years. This means two things: extending the existing bike lane network
and putting new stations throughout the city. I'll use spatial analysis to tackle
both. Now, to figure out where to put new bike lanes, the first thing that we have
to do is understand how people are using the system. We can see the highest usage
in downtown Boston, and near both Harvard and MIT in Cambridge. But this
is really just part of the story. Where do people go after they pick up a bike?
How are these stations connected? By looking at where each of the trips
starts and ends, we can start to get a sense of which stations have the
strongest connections, and we can combine our understanding of how people are
using the system with powerful network analysis to identify highly used, highly
connected stations that lack adequate bike lane coverage. This route between
Porter Square and One Kendall Square is a great example of an indirect route
between two busy stations that could be improved with a new bike lane. So what's
the best path for a new bike lane between these two stations? It may seem
like a simple routing problem, but a good bike lane is about a lot more than just
the shortest path. A good bike lane isn't too steep, so we'll include slope. It's
well-lit, so we'll include street lights. It's green, so we'll include trees. And it
avoids major roads if possible, so we'll include traffic volume. And we can use
spatial analysis to combine our sophisticated weighted overlay with our
network analysis to choose the best path. Now, at this point we've worked on where
to put new bike lanes. Next, we want to figure out where to put new stations
throughout the city. One of Boston's priorities for locating new stations is
providing equitable access, ensuring that everyone is within a 10-minute walk of
a bike share. Looking at these 10 minute walk times, we
can see that there are some gaps in coverage, which is where we'll put our
candidate stations. Now, of course, we also have to consider where people live
and we can combine our network analysis with our population data to calculate
which of the potential stations has the ability to improve access the most. And,
we can see how the model has allocated the underlying population to each of
those stations. Now we've chosen these station locations based on providing
access to as many people as possible, but how many people are actually going to
use these stations? How many bikes should each of them have? This geostatistical
surface models what we know about existing stations to predict usage at
each of our new stations. These predictions are important information
for planning a build-out. Ultimately, it's about getting creative and integrating
and applying The Science of Where, including powerful network in raster and
geostatistical methods to unlock the full potential of our data. It's how you
GIS professionals build the maps that run the world and shape the future.