Geoawesomeness, a website with a self-proclaimed focus on all things geospatial, celebrated its sixth anniversary last month. From posts on recently declassified CIA maps, to tips on geomarketing, to drones and remote sensors, Editor-in-Chief Aleks Buczowski and Managing Editor Muthukumar Kumar have covered a wide range of topics together.

And yet, despite overseeing a website whose monthly readership exceeds 150,000 visits, Aleks and Muthu have only met in person twice in the last six years. Skype, the two mentioned in our recent interview, has become the third friend in this “internet-age story of friendship.”

To celebrate the hard work of Aleks and Muthu, along with the entire team over at Geoawesomeness, we asked them a few questions related to the website’s history, plans for the future, current trends and changes within the geospatial field, and finally the field’s recent shift toward Location Intelligence.

CARTO: Can you discuss some of the challenges you have faced in expanding Geoawesomeness over the last six years?

Geoawesomeness: Well, it’s hard to believe that it’s been 6 years already! It definitely has been an amazing journey in our lives so far.

Geoawesomeness has always been about having a conversation about the most interesting projects in the geo industry. We want to make a difference in the industry, serve as a platform for connecting people, and highlight interesting work. In that sense, the biggest challenge has always been finding enough time to do so.

CARTO: What are some of your favorite posts from the last six years?

Geoawesomeness: Oh, that’s a tough one. It’s hard to pick favorites, but in terms of the posts that gave us the most satisfaction? It would have to be our GeoTrends series. It was really something to have Jack Dangermond, a legendary figure in the GIS industry blog for Geoawesomeness and share his thoughts for the future of GIS.

If we are being honest, if you had told us six years ago that we would be able to get to talk to companies like CARTO, Mapbox, HERE, and Esri, we would have told you that that sounds like an awesome dream!

CARTO: What else is on the agenda for Geoawesomeness in 2017?

Geoawesomeness: Haha, well, maybe the first thing would be to work on a formal agenda!

Kidding aside, we live in interesting times where three emerging industries—Drones, Earth Observation, and, an offshoot of Smart Cities, Self-Driving Cars—are changing how geospatial tech is being used. Drones, for example, are truly changing how farmers are improving agricultural practices by using data and algorithms that until now had always been used by remote sensing experts.

Drones have the potential to replicate what Google Maps did for (digital) maps!

CARTO: Are there areas in which you’d like to see your website’s focus expand?

Geoawesomeness: One area that we would like to focus attention is in increasing the diversity of opinions on our blog. At one point in time, we had active contributors from 5 different continents. We would love to see Geoawesomeness grow as a platform where people from different backgrounds blog with us so we can all share our passion for the geo industry!

Another area where we would like to expand is in covering startups. Talking to startups has been one of the most interesting aspects of blogging for Geoawesomeness, and we certainly hope to reach out to and blog more about geospatial startups.

CARTO: In Glassdoor’s “50 Best Jobs in America”, data scientist ranked as the number one job for the second year in a row.

How will this increased demand for data scientists as well as data analysts impact the geospatial field?

Geoawesomeness: There is a scene in a popular American TV show Billions where a character explains how she used satellite imagery to decipher that the company in question is actually a dummy corporation that doesn’t really manufacture anything. She certainly wasn’t a GIS analyst, but Location Intelligence made it possible for someone from a completely different background to use that information to make an informed choice. We think this will continue as technology evolves.

Precision agriculture, smart cities, autonomous driving, infrastructure optimization all use geotagged data. Subsequently, we are going to see more and more people working with geospatial data. You still will need someone with geospatial background to come up with more advanced versions of the Normalized Difference Vegetation Index (NDVI), but, generally speaking, geospatial is now truly everywhere and everyone is already using it.

CARTO: Is there a field in which geospatial has taken off that has surprised you? Is there a field that geospatial has not yet taken off in that surprised you?

Geoawesomeness: Indoor Mapping and Location Based Marketing have been fields where we thought things would really take off, and it has been disappointing not to see these industries embrace geospatial technologies.

Of course, things are looking brighter than before, but we were certainly wrong in how quickly we thought things might expand.

Commercial Earth Observation is growing at a very quick pace. It seems like every other month you read about another one of these companies receiving millions of dollars worth of funding. The success of SpaceX has certainly given investors a lot of confidence in the space industry, but this certainly has been one area where we didn’t anticipate to grow in such a rapid fashion.

CARTO: You’ve shared many posts showing the evolution of cartography from static maps to interactive data visualizations.

What, in your opinion, are the qualities of a good data visualization? Any tips or suggestions on what to do (and what not to do) when visualizing a dataset?

Geoawesomeness: Over the years we’ve shared with our readers hundreds of maps on the blog and via social media #GeoawesomeMapOfTheDay. Looking back we believe that there are three elements of a great map:

• Surprising Data
• An Amazing Story about this surprising data
• And, finally, beautiful visualizations conveying this amazing story about surprising data

Many of the best maps we’ve featured, in fact, were created with CARTO! Your team of designers have played a huge role in democratising mapping!

CARTO: Thank you! We have one last question. There’s a growing movement away from a GIS focus on geospatial to what we at CARTO call Location Intelligence.

What are your thoughts on this shift, and have you seen interest in this shift among your readership?

Geoawesomeness: Traditionally, GIS has been used a lot by government agencies and the research community to map, to mention only one application, potential landslides zones. GIS softwares, however, were largely resource intensive, which required workers using this technology to have a high degree of GIS expertise.

This is definitely changing given the recent wave of user-centric GIS and Location Intelligence softwares that have been designed for both experts and people new to the geospatial industry.

We’ve especially noticed this trend when talking with startups, many of whom are no longer bogged down by a lack of “geo” expertise thanks to Location Intelligence.

Cartography is as much art as it is science. A map can use the best data and most sophisticated analyses but is ineffective if the reader can’t understand the story behind it. However, unless you’re Leonardo da Vinci, being both an artist and a scientist doesn’t come naturally, so we’ve found two maps that demonstrate how colors, labels, boundaries, and symbols can help you create a powerful map.

Though this image looks like it could be a picture of a rectangular-shaped petrii dish growing a family of bacteria, it’s actually a map of Washington Park in Denver, Colorado.

Each orange dot represents either a tree or a location of interest, the blue lines depict trails or roads, and the amorphous blobs (technically called polygons) are supposed to be ponds.

However, without proper labels, coherent boundaries, or an intuitive color scheme, it’s difficult to see what’s going on, let alone be able to use the map for a game of Ultimate Frisbee or a Sunday picnic.

Ah, much better. This map looks much more like a park because it follows three key design principles:

1. Colors should align with expectations. Here, the grass is light green, the ponds are light blue, and instead of being an unnerving shade of orange, the trees are now dark green.
2. Labels should be hierarchical. Notice, for example, that the streets adjacent to Washington Park are named. The second map retains those labels but fades them into the background, emphasizing the interior of the park itself.
3. Positioning should match the map’s intent. Because the map is designed for visitors to navigate the park, the walking paths and locations of interest are clarified and labeled. Likewise, instead of cluttering the map with words, the cartographer uses symbols (and a key) to depict the various activities available, which gives the map the same simple and expansive feeling as the park itself.

This map looks like a snapshot from halfway through The Oregon Trail, when a few of the covered wagons have finally made it West but most are still chugging through the East Coast and the Great Plains. In fact, it’s a map of every county in the United States, and it makes a very important point — but what could it be?

This map clearly has a point to make, and it does so by employing three more key visual design principles:

1. Add additional variables to tell a story. By showing both the median cost of rent (height) and the percent of income spent on rent (color), the map, called a bivariate map, implicitly tells a complex story about how affordability differs by region.
2. Incorporate shapes to de-clutter the map. Though the first map has just as many points as the second, the former feels cluttered because each county is represented in an identical way. By expanding the range of visual options, the cartographer actually makes the map more legible.
3. Use features that emphasize unexpected data. It’s well understood that major cities are expensive to live in, but what’s surprising about this dataset is the outliers. For example, a county in northern Salt Lake City has a high median rent, but it’s relatively cheap for people in that area. Conversely, residents in many counties around Milwaukee give a lot of their paycheck to their landlords, even though the property isn’t that costly in absolute terms.

This post was based off the work and research of Mamata Akella, the Senior Cartographer at CARTO. You can watch her full talk, All about the carto in CARTO at the Location Intelligence Summit to learn even more principles to apply to your map making process.

The opportunity for New York City’s bus system has never been greater. As its sibling system, the subway, enters a declared state of emergency, the NYC bus system could help solve the city’s transit woes.

Last month, the Comptroller surveyed 1,220 subway riders and found that nearly 75% would give the system no better than a “C” grade.

Unfortunately for these commuters, repairs to the underground are expensive, laborious, and time-consuming. By comparison, improving the bus system could be relatively painless — especially if the Metro Transit Authority heeds the advice of a consortium of organizations called Bus Turnaround.

To formulate their recommendations, the organizations, including Transit Center and Riders Alliance, analyzed bus arrival times using the MTA’s location app, incorporated real-time data from the General Transit Feed Specification, reviewed ridership data, and mapped (and optimized) bus routes.

Based on this location intelligence, Bus Turnaround offered three major solutions that could be practically implemented:

Create Three Tiers of Buses — Each With a Distinct Purpose

Currently, New York’s bus routes attempt to satisfy every kind of commuter, but the Bus Turnaround Coalition has a different approach. After analyzing how people actually flow throughout the city — and what kinds of transportation they’d need to efficiently achieve their goals — they proposed restructuring the bus system into three separate lines, each with its own distinct branding:

• One would take passengers on the outskirts of the city directly to subway lines, facilitating a quick commute.
• Another would offer short, quick routes within a neighborhood, circulating people who want to run a quick errand or visit a nearby friend.
• The third would transport riders across borough lines, making up for gaps in subway coverage.

Expedite the Boarding Process and Prioritize Bus Traffic

Using location intelligence, the team also identified the most problematic intersections on each route.

For example, the BX19 line, which serves the Bronx and parts of Harlem, has an average speed of 4.9 MPH in the morning but 7.8 MPH at night.

While rush hour traffic certainly contributes to the problem, the Coalition identified two intersections that cause the greatest delays and proposed three street-level solutions:

• creating special areas along the road, called bus loading islands, where passengers could board more efficiently
• optimizing traffic signals so that buses hit the fewest red lights and could maintain a constant speed
• introducing “queue jump lanes,” lanes exclusively for buses, which then enjoy a three-second green light “head start” ahead of the other traffic.

Allow Dispatchers to Take Advantage of Existing Technology

Besides analyzing space, the Coalition also looked at time by targeting “bunching,” when multiple buses arrive at the same stop within minutes of each other.

This phenomenon reveals not just issues of unreliability, but also problems with bus dispatching and top-level control.

Because service tends to deteriorate more once buses start to cluster, the Coalition recommends giving dispatchers the power to hold buses or to instruct them to skip a stop. buses are already equipped with GPS navigation, so implementing such measures is a relatively straightforward, though somewhat bureaucratic, solution.

What’s Next?

By relying on location intelligence to identify the most troublesome areas, the Bus Turnaround Coalition has formulated practical solutions that would improve the flow of traffic of the 2.5 million New Yorkers taking the bus every weekday.

To learn more about the Coalition’s plans for the city, check out their proposal here — or, even better, join us for Buses, Beers, and Bytes: A Happy Hour for Tech and Public Transit in New York on July 25th, 2017. Hear from leading voices in the effort to upgrade New York City’s bus network and get a behind-the-scenes look at some of the solutions that can help make buses faster and more reliable.

As the importance of location data continues to grow so do the ways you can visualize this information. We’ve scoured the web in search of data visualizations showing the value of location data in its many varieties, and have compiled this mega list to bring you the very best examples. The 80 entries below surprised us, taught us, inspired us, and drastically changed the way we understand location data.

We grouped these 80 data visualizations into thematic categories, and then listed each entry (click on the name of the visualization to open it). The six categories include:

• Conflict Zones
• Connectivity
• Environmental
• Sites, Sounds, and Smells of City Living
• Social Media Maps
• Transportation

From data visualizations on global breathing patterns, to fan reactions to the latest episode of Game of Thrones, to international diplomacy and humanitarian crises, these 80 data visualizations are only a small glimpse into the different ways location data is being used around the world.

Enjoy!

Conflict Zones

Reprojected Destruction
Hans Hack’sReprojected Destruction uses satellite imagery showing city-wide damage to buildings and infrastructure in Aleppo, Syria that he then projected onto figure-ground maps of Berlin and London. “The overall aim of the exercise,” as stated on the website, “is to help viewers imagine the extent of the destruction that might have been visited upon the UK and German capitals had these cities stood at the centre of Syria’s current conflict.” In using location data to relocate the destruction wrought by the Syrian civil war, Hack reminds us that data visualizations are not only beautiful, but powerful communication tools.

Conflict Urbanism: Colombia
The causes behind the unsustainable increase in global urban migration are many, but this data visualization shows how armed conflict has caused mass migration in Colombia from 1985 to 2015. Built with a recently released open dataset, Conflict Urbanism uses location data from displaced populations to chart routes from Colombia’s countryside to its urban centers. These routes can be enriched with municipal location data and population demographics provided from NASA satellite imagery and the Colombian National Department of Statistics respectively.

World Migration Map
With open data on worldwide net migration between 2010 and 2015 provided by the United Nations Population Division, Max Galka set out to visualize this large volume of data in one map. The result? An incredible resource charting migration flow patterns from origin-to-destination spanning five years. In visualizing this location data, the “World Migration Map” provides a transparency tool that can help fact check politicians flaming fears with heated rhetoric about walls and what not. You can read Galka’s full analysis here.

Missile Threat: CSIS Missile Defense Project
As relations among NATO member states cool, Russia continues to flex its militaristic might and increased geopolitical presence. In response, the Center for Strategic and International Studies’ (CSIS)Missile Defense Project built MissileThreat, an interactive data visualization providing a broad (but admittedly not exhaustive) overview of the A2AD situation in Europe. Location data was used to map military bases throughout the area, and then an area of influence analysis was applied to approximate the radius of areas at risk from different missile launch capacities.

Spies in the Sky

Peter Aldhous’ “Spies in the Sky” data visualization reveals flight track patterns of the U. S. government’s airborne surveillance using aircraft location data provided by flightradar24. Individual aircraft flights are represented by animated dots while dense circles indicate regularly monitored areas. The data visualization color palette–red, white, and blue–reinforces Aldhous’s point, and perhaps explains why National Geographic ranked “Spies in the Sky” among its best maps of 2016.

Syria After Four Years of Mayhem
Before and after pictures can seem gimmicky, but Sergio Pecanha, Jeremy White, and K. K. Rebecca Lai reminded us of this genre’s effectiveness in “Syria After Four Years of Mayhem” (2015). Leveraging satellite imagery, location data from IHS Energy Data Information Navigator, and data from several humanitarian relief agencies, the authors show the devastation of the Syrian civil war by visualizing how in two years “the country is 83 percent darker at night than before the war.”

The Executive Abroad
Did you know that until Theodore Roosevelt, no sitting United States President traveled outside the country? This is just one of the many historical insights made accessible thanks to the University of Richmond’s Digital Scholarship Lab, and specifically its data visualization of every executive trip from Roosevelt to Obama. We especially love the customized basemap’s interactive compass that makes use of location data’s temporal and spatial dimensions.

The Refugee Project
Hyperakt’sThe Refugee Project reminds us that art is a medium for political protest. This data visualization is both a resource that uses United Nations’ refugee data to enable comparative studies on refugee migration and a work of art that New York’s Museum of Modern Art selected for its “Design and Violence” exhibit.

The Shape of Slavery
Michelle Alexander’s The New Jim Crow, a major source for Ava DuVernay’s 13TH, identified Jim Crow legislation as the origins of the “school to prison pipeline.” In The Shape of Slavery, Bill Rankin and Matt Daniels distill the location component of historical data related to slavery and incarceration rates to provide visual proof that America, far from being a “post-racial” society, “is only recently a post-slavery one.”

United States Sanctions Tracker
Enigma’sSanctions Tracker, which monitors U. S. sanctions from 1994 to the present and updates each day, is one of the first resources to map this data despite the inherent spatial aspect of sanctions. The tracker uses time-series analysis to show the rise in sanctions across four different presidencies, and its interactive design allows viewers to click on animated dots to learn more about the specific type of offense being sanctioned.

White Collar Crime Risk Zones
The New Inquiry is bucking a data visualization trend that maps open data from police reports that tend to focus on “street crime” prevention. Instead, this data visualization uses machine learning to locate white collar crime risk zones (and provide some uncanny facial profiles!). Brian Clifton, Sam Lavigne, and Francis Tseng explain their methodology here, and we’re excited to enter this brave new world of data visualizatons!

Connectivity

A Day in the Life of an American
Nathan Yau over at Flowing Data takes a creative spin on location data in his simulation of movement patterns among Americans. With data from 1,000 people’s daily activities as reported in the United States Department of Labor’s American Time Use Survey, Yau’s simulation represents each person as an animated dot, whose color changes en route from one activity to the next over the course of twenty-four hours, to locate behavioral patterns. It’s meta. It’s beautiful. It’s a must see.

Connectivity Atlas
John Donne told us that “no man is an island entire of itself,” but what, exactly, connects everyone to “a piece of the continent”? Luckily, Connectivity Atlas has an answer with its data visualizations that shows how “[i]nfrastructure connects and defines us.” Built entirely with open data, this data visualization maps all the connective threads powering your day to day activities including telecommunication, transportation, and energy.

Global Diplomacy Index
The Lowy Institute for International Policy, a nonpartisan Australian think tank, created the Global Diplomacy Index to visualize diplomatic networks. At the same time, this map exposes gaps in network coverage as well as high concentrations of diplomatic resources around the world. The map’s interactive design allows viewers to see the global reach of diplomacy at both the city and country level as serene blue lines are drawn across a basemap reminding us all to keep a cool head.

Live Cyber Attacks
If 2016 taught us anything, it was the threat posed by cyber attacks. Norse provides threat attack intelligence, and its mesmerizing Cyber Attacks data visualization uses location data to show, in real-time, the origin and destination of security breaches.

Chicago’s Million Dollar Blocks
Millions of dollars are being invested in low-income neighborhoods across Chicago, but not to (re)invest in these neighborhoods. Instead, as Chicago’s Million Dollar Blocks project reveals, a war on neighborhoods is being waged as more money is spent policing low-income neighborhoods across the city. This data visualization is more than an expose of wasteful spending. In fact, Chicago is reimagined from the perspective neighborhoods whose low-income status is perpetuated by large infusions of tax dollars that fund disproportionate policing, which leads to higher incarceration rates despite declining crime levels.

National Broadband Map
What does your broadband connection say about you? Well, as the National Broadband Map demonstrates, a lot! It may not have as many interactive features as the Connectivity Atlas or Global Diplomacy Index, but it does use location data to identify gaps in broadband coverage across the country. In light of recent FCC rulings, this data visualization is an important reminder that the digital divide persists. Check it out today!

Every Active Satellite Orbiting Earth
David Yanofsky and Tim Fernholz provided some much needed “edutainment” in Every Active Satellite Orbiting Earth. This data visualization gives viewers a sense of the location and orbit perimeter of the 1,300 active satellites. Satellites, represented as individual bubbles whose color indicates its use, are compressed into a column indicating altitude position above the earth. Make sure to turn on the orbit feature to get a sense of each satellite’s orbit!

Twenty Years of India at Night
A picture may be worth a thousand words, but how many data points can a picture provide? Twenty Years of India at Night may have the answer! Using pictures from the Defense Meteorological Satellite Program that were taken each night between 1993 and 2013, researchers extracted location data on light output from 600,000 villages and then mapped these points on the India Lights map. The time-series analysis feature shows both the volume of data collected and reveals the large rural areas still lacking access to electricity across India.

What Powers the World?
GoCompare’sWhat Powers the World? is an interactive visualization built with location data provided by the International Energy Agency displaying how reliant each nation is upon fossil fuel, nuclear, and renewable energy. What we love about this data visualization is its use of a dark matter basemap, a subtle use of color theory illuminating what really does keep the lights on around the world.

World’s Biggest Data Breaches
David McCandless, founder of information is beautiful, and Tom Evans created World’s Biggest Data Breaches, an interactive timeline of data leaks from 2005 to the present including interactive bubbles for each entry. Oh, by the way, each bubble represents breaches of at least 30,000 records and provides detailed information on the leak type, the industry in which the leak occurred, and links to detailed reports covering the breach. Locating data on data leaks has never been easier (…or scarier!).

Environmental

Breathing Earth
John Nelson’sBreathing Earth used satellite images from NASA’s Visible Earth catalog to create an animated data visualization showing the earth’s pulse through a year’s seasonal transformation. The map was a huge hit, and has spawned many noteworthy follow-ups including Nadieh Bremer’sA Breathing Earth (2016) and an entry included a little further down on our list!

Cloudy Earth
We’ve looked at clouds from both sides, but NASA Earth Observatory has us beat with its visualization of cloud data between July 2002 and August 2015. Cloudy Earth attempts to visualize data on clouds, one of the least understood components of our climate, in order to study its role in global climate change. NASA’s Aqua satellite, and its MODIS sensor, provided imagery and location data for this visualization whose cool-blue color palette and time-lapse animation enables viewers to easily identify patches of high cloud density around the world.

Ecoregions 2017
RESOLVE’sEcoregions 2017 data visualization displays the earth’s 846 ecoregions in a stunning example of biogeography. The map contains a host of interactive features that not only use location data to identify areas of biological diversity, but also to track global progress on Nature Needs Half’s commitment “to protect half of all the land on Earth as a living terrestrial biosphere.”

Eruptions, Earthquakes, and Emissions (E3)
The Smithsonian’s E3 data visualization is a time-lapse animation of volcanic eruptions, earthquakes, and carbon emissions around the world since 1960. Using data from its Global Volcanism Program, earthquake data from the United States Geological Survey, and data from the Deep Carbon Observatory, this map uses location data to better understand our environment.

Global Historical Emissions Map
Similar to the Smithsonian’s E3 data visualization, Aurélien Saussay’sGlobal Historical Emissions Map surveys environmental changes over time. However, this data visualization displays location data on fossil-fuel burning and gas flaring as well as cement production between 1750 and 2010. You can read more about Saussay’s methodological approach to mapping the industrial revolution’s historical impact as well as his decision to use a gridded dataset here.

GlobalView: Climate Change in Perspective
Data visualizations are a great way to tell a story, and that’s exactly what the editors at Bloomberg View do in GlobalView: Climate Change in Perspective. This story map works with location data related to climate change to present a clear, concise message about the urgency of this global crisis. Following the recent announcement about the Trump administration’s decision to leave the Paris Agreement, we need more of these types of data-driven stories.

The Lead Map
We’ve mentioned the pressing issue of water insecurity, which is why we’re thrilled to include SimpleWater’s latest data visualization. The Lead Map uses location data related to the ages of homes in a given county as well as the average corrosiveness of that state’s groundwater to predict the level of lead exposure in a neighborhood’s water. We love this data visualization not only for drawing attention to the pressing issue of access to clean drinking water, but also for its innovative use of different types of location data used for risk assessment. Check out your neighborhood’s lead exposure today!

London Atmospheric Emissions Inventory
Similar to the previous two entries, Parallel’s data visualization uses location data to map emissions across London, England. What’s different, however, are the 3D interactive features showing the levels of concentrated atmospheric emissions across the city.

Migrations in Motion
We’ve mentioned that climate change is contributing to increased urban migration, but how are animals reacting to these changes? This is the question that Dan Majka, member of The Nature Conservancy’s North America Region science team, set out to answer with Migrations in Motion, a data visualization charting the average migration routes for mammals, birds, and amphibians. Inspired by our next entry, this map distills location data on the migratory movement of nearly 3,000 different animal species into a macro-level view. Check it out today!

The Earth Wind Map
In 2013, Cameron Beccario created The Earth Wind Map, a data visualization showing global weather conditions as forecasted by supercomputers with updates every three hours. The project was originally inspired by Hint.fm’sWind Map, a data visualization of wind patterns that automatically updates based upon available weather data. The Earth Wind Map’s use of location data is nothing short of revolutionary, which you’ll discover by interacting with the data visualization. See what the same location data looks like using a stereographic projection! In the words of Florence and the Machine: “So big, so blue, so beautiful!”

Five Years of Drought
The widely-celebrated Five Years of Drought, John Nelson’s second appearance on our list, visualizes 285 weeks of drought data as reported by the United States Drought Monitor in a single view. Despite its static design, the results, as Nelson writes, was “a map that accidentally characterizes the movingness of droughts over five years by using opacity to represent motion.” A great example of the role perceptual color theory plays in spatial analysis and data visualizations, both static and interactive!

The True Size Of…
James Talmage and Damon Maneice created this app to dispel geographical misconceptions resulting from map projections, like Mercator, that distort the size and shape of land masses, and most notably the size of the African continent. Similar to Reprojected Destruction, we love this data visualization for its re-visualizations! Check it out!

What Is Missing?
Maya Lin’s What Is Missing? is a wake-up call for a world on the brink of the sixth mass extinction. Unlike other entries on this list whose primary aims also can show gaps in network coverage, this entry is entirely premised on using location data to visualize degradation and absences.

Treepedia
MIT’s Senseable City Lab’sTreepedia maps location data related to tree canopies for cities around the world including Paris, Frankfurt, and Cape Town. Instead of mapping each individual tree in each city, these data visualizations are built with an analysis method that uses location data to show the “amount of green perceived while walking down the street.”

Meteor Showers
How can data visualizations represent abstract concepts without distorting or reducing the data’s spatial design? That was the problem facing Ian Webster while working with meteor shower data collected by astronomer Peter Jenniskens in Meteor Showers. The solution? Create a 3D visualization providing viewers a 360 degree view of meteor showers moving through the solar system.

World Population Density
The accelerated rate of global urban migration is cause for alarm for elected officials tasked with providing city-wide services. But where, exactly, are these population increases happening? To answer this question, Duncan Smith over at CityGeographics built this World Population Density map with location data from the European Commission JRC and the Center for International Earth Science Information Network at Columbia University. What’s stunning about this data visualization is its ability to dispel a reductive urban-rural understanding of geography as dense pockets of human settlements are found beyond traditionally recognized urban centers around the world.

Sites, Sounds, and Smells of City Living

3D Model of New York City
CESIUM’s data visualization brings together features discussed in other entries–like mapping city-wide shadows and historical development–in one 3D model of New York City. This data visualization uses 3D Tiles to represent location data in a responsive manner leading the way to fulfilling the Digital Earth vision.

50 Years of Concerts of The Rolling Stones
This data visualization commemorating the 50th anniversary of The Rolling Stones maps moonlight miles traveled while touring for half a century. The location data for this data visualization was extracted from Wikipedia, which we know can sometimes be like playing with fire, but luckily fans know their facts!

A New View of the Housing Boom and Bust
The Urban Institute’s Bing Bai and Taz George originally published A New View of the Housing Boom and Bust in September 2013, but this interactive data visualization continues to be updated each year with open data made possible by the Home Mortgage Disclosure Act. What we love about this data visualization is the annual time-lapse animation set against a static line graph enabling viewers both a macro and micro glimpse of the housing market’s travails.

Block by Black, Brooklyn’s Past and Present
Thomas Rhiel built this data visualization in 2013 with historical location data provided by New York City’s Department of City Planning to chart the uneven evolution of Brooklyn’s look and feel. More specifically, Rhiel plotted and shaded over 320,000 Brooklyn buildings according to construction year to see why certain areas of the city are more developed whereas some neighborhoods seem not to have been modified at all.

Breathing City: Manhattan’s at Work and Home Population by Hour
Inspired by John Nelson’s Breathing Earth, discussed above, Joey Cherdarchuk’s Breathing City visualizes Manhattan’s respiratory motion over a single day. This dot density map charts Manhattan’s population both at work (red dots) and at home (blue dots), which, as Cherdarchuk explains, was harder than expected as obtaining the appropriate location data was difficult.

Count Love
Sometimes, less is more. We love Nathan Perkins and Tommy Leung’s understated visualization of location data related to resistance protests, titled Count Love. When asked about their inspiration, Tommy and Nathan said that they “created Count Love in the hopes of historically documenting protests related to civil rights, immigration, racial injustice, and other important societal issues across the United States.” In addition to Count Love’s interactive data map, check out the use of proportional bubbles scaled to the size of each demonstration on the statistics page!

Every Shot Kobe Bryant Ever Took
To commemorate former Los Angeles Laker Kobe Bryant’s final game, the Los Angeles Times created a data visualization featuring a custom basemap displaying a basketball court on top of which are 30,699 dots representing the location from which Bryant took every shot of his career. An innovative approach to indoor mapping to say the least!

Fans on the Move
Are you willing to travel internationally to attend your favorite band’s concert? Your favorite sports team’s big game? Ticketbis, an international subsidiary of StubHub, examined 36 months of location data on attendees purchasing international tickets through its service, and the results are interesting. Spoiler: the Superbowl and 2012 Summer Olympics rank pretty high, but check out which countries of origin are home to some of the world’s most diehard groupies!

How Music Taste Evolved: The Billboard Top 100 from 1958-2016
Matt Daniels over at The Pudding, visualized data for 22,000 songs ranked among Billboard’s Top 100 over nearly six decades. This time-lapse animation shows the top five songs each week while the audio plays clips from each number one song. Yes, this visualization uses data to locate cultural trends at a certain moment of time, but what really caught our attention was the audio component that scaled the length of time a number one hit played to the length of its time in the top spot!

Mapping the Shadows of New York City: Every Building, Every Block
Manhattanhenge is great, but for the rest of the year New Yorkers take access to sunlight very, very seriously. In “The Struggle for Light and Air in America’s Largest City” (2016), Quoctrung Bui and Jeremy White built a data visualization of New York City that maps building shadows. Using location data on Manhattan buildings, Bui and White used ray tracing to simulate the effect of sunlight on each building and its surrounding area. The results are stunning as “dark” neighborhoods in the shadows of nearby skyscrapers are easily spotted. Location data can cast a long shadow it turns out!

Musical Map of the World
Eliot Van Buskirk, Data Storyteller at Spotify, built this data visualization using location data extracted from customers’ streaming preferences. As such, Musical Map of the World curates “distinctive playlists” each week for cities around the world featuring that city’s top 100 streamed songs. Map viewers become map listeners with this data visualization as each dot can stream that city’s playlist. Check it out!

Netherlands Building
Inspired by Thomas Rhiel’s data visualization mentioned above, Bert Spaan and the Waag Society created this data visualization representing all 9,866,539 buildings in the Netherlands. The qualitative color scheme shades each buildings by construction year, and the use of a dark matter basemap adds a contrast that catches the eye. The Waag Society, in fact, has been selling reproductions of this beautiful visualization of location data!

Population.io
The World Data Lab’sPopulation.io may be both the most comprehensive and informative visualization of location data on our list. One of our favorite interactive features is the visualization of demographic data based upon a map viewer’s date of birth, a neat way to show how in your own life span the world’s population has increased. Another interesting feature is the interactive map that estimates the remainder of your life expectancy based upon current location that can be compared to other countries around the world.

Smellmap: Amsterdam
Entries on this list so far have used location data to map a given city’s visual sites and audible sounds, but our next entry takes data visualizations to a whole new level: visualizing a city’s smell. Kate McLean, artist and designer working on urban smellscapes, created Smellmap: Amsterdam, a sensory map whose animated dots indicate over 50 smell types whose wafting radius is represented by concentric circles.

Spain in Figures
We’ve mentioned in the past how open data enhances transparency around a local government’s smart city projects, and Spain in Figures is a great example of what that means. This visualization of location data across Spain provides proof of changes across the country over the last four years. As an open source tool, moreover, this data visualization encourages local residents to contribute data on their municipality, a great method to hold elected officials accountable.

The Geographic Divide of Oscar Films
Inspired by Josh Katz’s cultural divide maps, Matt Daniels, Ilia Blinderman, and Russell Goldenberg over at The Pudding decided to see if cultural and geographical divides corresponded in relation to 2017 Oscar-nominated films. The maps are gorgeous, the methodology rigorous, and the widespread popularity of Arrival undeniable!

Underworlds
How can location data from your city’s wastewater system help public health officials better understand urban epidemiology in near real-time? This is the question behind Underworlds, which is the second entry on our list from MIT’s Senseable Lab. We love this data visualization for providing a twenty-first century take on John Snow’s cholera map, and look forward to seeing whether this project can improve a city’s health one neighborhood at a time.

Ungentry
Ungentry, a Code for America Brigade project, wanted to know if Beantown would follow a similar pattern of gentrification as that of San Francisco, California and New York, New York. This data visualization uses a choropleth map to highlight changes in data for each Boston neighborhood between 1990 and 2010 to determine a baseline for gentrification, which will enable further analysis helping to identify factors contributing to this change in city demographics.

Why Measles May Just Be Getting Started
Keith Collins, Adam Pearce, and Drew Armstrong’s Why Measles May Just Be Getting Started is a great example of data journalism, and one of our favorite visualizations of location data without a geographical basemap! Instead of plotting the geographic coordinates of an outbreak of measles, this entry visualizes each state as a post-it note whose size proportionally corresponds to the number of reported outbreaks.

Social Media

Gay Happiness Index
PlanetRomeo, Europe’s leading gay social network, created the Gay Happiness Index using location data retrieved from their online dating app. Based on data from over 115,000 users, this data visualization provides a happiness score for each country that is then ranked to determine the best place for gay men to date. Find out how your country scored below, and scroll down to learn some interesting facts too!

How Every #GameOfThrones Episode Has Been Discussed on Twitter
For a twist on location data, check out Krist Wongsuphasawat’s interactive How Every #GameOfThrones Episode Has Been Discussed on Twitter data visualization. Using social media data from Twitter, this data visualization forgoes geographical location and instead locates thematic interest in each new episode of HBO’s Game of Thrones. More specifically, this data visualization depicts statistics on fan reactions shared on Twitter in the twenty-four hour period following each episode’s premiere. Find out when, exactly, winter arrives using this website! #Longmayshereign

Inequaligram: Measuring Social Media Inequality
You’ve probably seen a lot of public Instagram images of midtown Manhattan shared by both tourists and residents, but what about Fort Tryon Park? The answer is likely “No,” and the reason for this disparity, the team behind Inequaligram: Measuring Social Media Inequality finds, relates to economic inequality across New York City. These dot density data visualizations were built with location data extracted from 7,442,454 public Instagram images shared by visitors to, and residents of, Manhattan, and you’ll note that the volume of images for both visitors and locals drastically trails off after 110th street.

Sunrise around the World
Are you a morning person? Well, as this visualization of location data extracted from global tweets demonstrates, you’re not alone! This time-lapsed dot density map shows geotagged tweets containing “sunrise” in different languages from around the world on April 6, 2014. Make sure to zoom in to see just how many Twitter users tweeted about the rising sun!

The Food Capitals of Instagram
We know foodies love posting images of their meals on Instagram, but what location data can the images themselves provide? The Food Capitals of Instagram adds a twist to social media data visualizations in mapping not restaurant locations but rather the geograpical orgins from which the food served orginates. The visual’s location data was extracted from more than 100,000 photos posted on Instagram between March 10 and March 15, 2015, and bubbles are sized in proportion to the volume of photos.

The Louvre on Instagram
This entry is “meta” to say the least. Tin Fisher’s created a data visualization featuring a basemap representing the floorplan of the Louvre, one of the world’s premiere art museums located in Paris, France, and mapped Instagram images of the images on display in the Louvre. Fisher downloaded geotagged photographs of Louvre images from 2014 using the Instagram API, and mapped each image as a data point according to its actual positioning within the Louvre. It wasn’t too surprising to see the high volume of foot traffic around the Mona Lisa. It was surprising, however, to see how many people looked at art pieces through a screen at the Louvre!

Twitter Tongues
The wealth of location data provided from social media platforms is staggering, and in this data visualization James Cheshire mapped 3.3 million geo-located tweets collected by Ed Manley featuring different languages spoken across the city of London during summer 2012. That London hosted the 2012 Summer Olympics accounts for the high density of dots of different languages found in and around Olympic Park. Check it out (and make sure to see the difference the basemap slider makes)!

Locals & Tourists
We were impressed with the previous entry’s visualization of 3.3 million data points, but a year later Eric Fischer mapped 3 billion tweets in Locals & Tourists. This data visualization breaks down the tourist-local divide by mapping social data provided by GNIP. Learn more about what went into processing this high volume of geospatial data here.

Wikipedia Recent Changes Map
Stephen LaPorte and Mahmoud Hashemi’s data visualization tracks global updates to Wikipedia made by unregistered users. Although this population only amounts for approximately 15 percent of total Wikipedia updates, it is pretty cool to see how LaPorte and Hashemi used IP addresses to extract the geograhical location of unregistered users.

Wikiverse
Have you ever gone down a Wikipedia rabbit hole? Well, imagine if that were a black hole and you’d begin approximating Wikiverse, a self-proclaimed “galactic reimagining of the Wikipedia universe.” What we love about this data visualization is its treatment of location data wherein spatial “proximity” is rendered as semantic “similarity,” a move reminiscent of Tobler’s first law of geography. Check it out today!

Visualizing Global Blog Activity
If you work in marketing, then you’ve probably heard the phrase “content is king.” And you’ve probably been warned about the volume of quality content being produced around the internet too. Well, Twingly’s data visualization offers quantitative proof of at least the amount of blogging around the world in near real-time. Make sure to turn on the sidebar showing each post’s language to get a better sense of what all those expanding lines really mean!

Transportation

A Tale of Twenty-Two Million CitiBikes
In his almost Dickensian break down of location data extracted by CitiBike riders, Todd Schneider details the hidden story behind 22.2 million Citi Bike rides across New York City. We love the time-lapse animation tracking the route of each and every bike in use over the course of a day. Make sure to check out what rush hour looks like for bikers starting around 5:30pm for a whole new take on “it was the best of times, it was the worst of times.”

Average Commute Times
The grass may seem greener on the other side, but is the commute time shorter too? Thanks to this data visualization you can now dispel the belief that other commuters have it easier using AutoAccessoriesGarage’s interactive data map. Built with open data on commuting from the United States Census Bureau, this data visualization allows viewers to check their average daily commute against zip code and state average in a beautiful choropleth map. Check your commute score today!

Every A380 Route
The Airbus A380, the world’s largest airplane carrier, has fallen out of favor as airlines attempt to cut costs. Today, as David Yanofsky reports, only 13 out of 57 airlines even fly the A380! Read more of Yanofsky’s report here, and check out the rotating globe visualizing A380 flight routes using location data from PlaneStats and OpenFlights.

Glasgow in Motion
Glasgow is Scotland’s largest city, but with Glasgow in Motion you can glimpse the pulse of this thriving metropolis in real time. The Urban Big Data Centre has created more than a transportation app with real-time alerts as this location app even visualizes the city’s air quality using data from World Weather Online. Check it out today!

The Ship Map
KilnsThe Ship Map, winner of a 2016 Information is Beautiful Award, displays the 2012 movement of the global merchant fleet using location data provided by Julia Schaumneier and Tristan Smith of the UCL Energy Institute. By cross referencing location data on each ship’s geographical coordinates and speed with other databases, this data visualization determines characteristics of each ship in order to calculate the hourly rate of carbon emissions. This is a great example of using accessible location data to estimate missing variables!

Hubway
The second entry on our list from Nathan Perkins and Tommy Leung’s of Count Love. Hubway charts over five million hubway trips taken in Boston to measure station traffic in order to identify outliers that are being underutilized. We loved this cyclist take on route optimization!

In Flight
Kiln created this data visualization for The Guardian using data provided by FlightStats. This data visualization tracks individual flights in near real-time as well as flight routes identifying areas with high traffic volume each and every day. Watch the video before exploring the map for yourself!

New Europe
Benedikt Groß, Philipp Schmitt, and Raphael Reimann over at Moovel Lab set out to determine whether all roads do, in fact, lead to Rome. Similar to other entries on our list, New Europe undertook a route optimizaton, but on a whole new level as they determined which of the nearly 500,000 roads leading to Rome was the best option. These data visualizations definitely weren’t built in a day, but we’re grateful for all the time and energy that went into mapping the routes from each of the 486,713 starting points! Learn more about the project here.

Night Lights Map
We’ve been told the night is dark and full of terror, but luckily there’s a Night Lights Map for that. NASA’s Earth Observatory created this dazzling data visualization, which is “the clearest yet composite view of the patterns of human settlements across our planet,” using location data extracted from satellite imagery. In addition to beautiful images, this data visualization will help researchers investigate how cities around the world expand in the coming years in response to global urban migration.

Sensing Vehicle: The Car As An Ambient Sensing Platform
There’s been a constant buzz around driverless vehicles for some time, but did you know that our current fleet of cars contain upward of 4,000 sensors already? Sensing Vehicle, the third and final entry on our list from MIT’s Senseable City Lab’s, provides one of the first interactive car model maps that locate the sensors making our cars smarter and smarter each day.

The Megaregions of the U.S.
Based upon their research related to economic geography across the United States, Garrett Dash Nelson and Alasdair Rae visualized this work in The Megaregions of the U.S. This data visualization participates in growing efforts to map beyond static boundaries and instead see what the U.S. “might look like if we based our regions on the pattern which commuters weave every day between cities, suburbs, and rural areas.” With more than four million lines, this data visualization weaves together beautiful digital cartography and innovative spatial analysis. A must see!

Travel Time to the Closest Primary Airport in the U.S.
City-Data built Travel Time to the Closest Primary Airport in the U.S. This data visualization calculates commute times with the help of machine learning, specifically using Open Source Routing Machine (OSRM), that is applied both to public and private data to calculate spatial distance and your optimized route to and from the airport. Check it out!

Visualizing 24 Hours of Subway Activity in New York City
Will Geary takes a different approach to visualizing a day in the life of New York City’s subway system by forgoing a static basemap altogether. Instead, Visualizing 24 Hours of Subway Activity in New York City displays a day’s worth of location data related to train travel beyond the customary NYC Transit Map. Geary’s data visualization provides a brand new way of seeing how the subway runs in the city that never sleeps.

Communities are incredibly difficult to map and most research packs them into isolated groups.

But we know that communities are almost never distinct, spatially isolated groups, especially when it comes to urban areas.

The same space or area may serve many different groups of people, who access different aspects of that space, and certain communities can span beyond hard borders like zip codes and census-defined city borders.

With the growth in urban mobility and location data, strategies around spatial planning are increasingly addressing the notion that space and land use can be dynamic and flexible, changing shape and purpose at different times of the day.

We wanted to explore how we can use data to better understand and define communities of people, going beyond spatial borders like zip code and neighborhood boundaries.

We do this through the lens of one neighborhood: Williamsburg, New York.

A brief history of Williamsburg, NY

Williamsburg has a history of being home to a diverse range of immigrant ethnic communities, including Italians and eastern Europeans in the early 20th century, refugee or migrant Jewish people during World War II, and Hispanics and Puerto Ricans in the 1960s in search of factory jobs. Since the 1970s, it has also been a hub for the cultural community, as the decline of heavy industry in the area eventually brought an artist and musicians in the area in search of cheap rent and spacious accommodations. And artists, it is generally conceived, are often the harbingers of gentrification for previously low-rent inner-city neighborhoods.

The density and diversity of Williamsburg has often led to the spatial and cultural territory conflicts, ranging from tensions between the Hasidic Jewish community and Hispanic and black minorities in the neighborhood, to those between hipsters and poser hipsters.

Add to this an increasing number of tourists and inter-borough tourists to Williamsburg, and we can see that the borough is indeed diverse in the types of people that live, work, visit, and play here (despite no longer being the predominantly working-class neighborhood it once was).

Analyzing the Location Data

We wanted to better understand the communities within Williamsburg using location data, so we decided to revist the New York City Taxi and Limousine Commission’s open data and the clustering algorithm called DBSCAN, which looks for clusters that are at least a minimum number of points and a minimum “distance” away from each other. This diagram below illustrates how this type of clustering works.

Instead of thinking about distance as a purely spatial concept, we wanted to look at the ‘closeness’ of a bundle of characteristics, some of which are non-spatial, such as the time of the taxi drop-off, to find groupings of taxi rides that are similar to each other. The characteristics we clustered are: pick-up and drop-off locations the day of the week the time of the day the trip distance

Identifying the Communities

Using this advanced spatial analysis combined with open location data, we’re better able to understand the Williamsburg neighborhood and the communities that exist within it. With this cluster analysis, we identified 75 communities, five of which we have highlighted here as, “partiers”, “intra-borough residents”, “working class” residents, “visitors with expensive taste”, and “Orthodox Jewish” residents.

On our map, if you toggle for certain groups and times of the day, you can see the emergent behavior of these groups: For instance, the “partiers” take taxis from Lower Manhattan and Brooklyn to Williamsburg, generally pretty late at night, the Orthodox Jews do not travel very far and mostly congregate in South Williamsburg.

There are many different ways to label and interpret the data used to create the map, but our goal was to highlight an interesting method to investigate communities that occupy similar space. We hope that this map also does justice in representing the beauty of all the many diverse groups of people that visit and live in Williamsburg.

A central component of territory management is sales territory design. Gartner has found a 73 percent increase in business implementation of territory management softwares since 2012, a figure likely to increase as more and more companies adopt data-driven strategies.

Many companies still design sales territories based on predefined geographic boundaries, like state borders, zip codes, and city borders.

But defining sales territories this way can lead to uneven distribution of deals, poor utilization of sales executives’ time, and a lack of the granularity required in large urban areas.

The most forward-thinking B2B sales and operations directors have started incorporating location intelligence into their sales territory management, capturing the value of their “data exhaust” to set themselves apart from their competition.

Instead of basing sales territories on geographic boundaries, they are using diverse datasets related to customer value and size, customer behavior, and other demographic data to identify areas with below average sales performance.

A recent study found 72 percent of companies admitted to disproportionally allocating more resources to high profile clients.

Below, let’s take a look at 3 steps sales and operations directors should take when redesigning sales territories:

Establish Clear Goals and Objectives

The first step to take when working with a location intelligence platform for sales territory design is to have specific goals and objects established. Sales territories are often defined in relation to an individual company’s go-to-market strategies. To put these strategies into practice, a company may be using a definition of “territory” with a:

• Geographic-based focus
• Service-industry focus
• Account-based focus
• Product-based focus

Understanding your company’s go-to-market strategies as well as the working definition of “territory” will help in selecting the design’s base unit of measurement. Typically, sales territories are divided up with traditional geographic boundary units like 3-digit zip code, 5-digit zip code, census tract, city, county, state, or even by region.

But predefined boundaries can lead to territory designs out of sync with your company’s specific strategies, which can result in missed opportunities or misallocation of resources.

Instead, location intelligence visualization tools can provide an alternative solution.

Sales and operations directors, for example, could use average sales size by location as a base unit measurement to design territories. As such, this design would take into account service industry trends to determine territories with best business growth potential, but whose dimensions may not correspond to predefined geographic boundaries.

Explore territory designs for balance and alignment

The next step in designing territories with a location intelligence platform involves using an interactive data visualization tool with advanced routing capabilities.

Instead of simply plotting points on a map of a customer or prospect’s location, sales and operations managers are exploring and realigning territories with data visualization tools to discover data-driven solutions to the two biggest challenges encountered when designing a sales territory:

1. Territory Balancing: the equal distribution of prospects among sales team
2. Territory Alignment: the process of assigning the right salesperson to the right area to increase sales productivity

Extracting location data that can be used to ensure territory balance and alignment in your design requires a dynamic data visualization tool capable of importing data layers from various internal and external sources related to industry trends, potential customer segmentation, geographic constraints derived from traffic and mobility data, and sales team performance.

Location intelligence platforms equipped with data visualization tools can help sales team managers:

• View territory hierarchies to locate where customer base is located
• Design custom territory according to specific goals and objectives of your company
• Prioritize customers and prospects in an area with a heat map
• Import external data from a curated data library to contextualize surrounding sales location
• Overlay datasets from different sources to determine if territory is over-or-under serviced

These are only a few features that can help optimize a territory management plan especially when design a sales territory. But let’s take a look at one feature specifically in the next section that can really help reduce overhead.

Optimize sales territory routes

A recent business travel report found that the average total cost for a business trip in 2016 amounted to $1,068. At the same time, Salesforce’s 2017 State of Sales Report found that 64 percent of surveryed reps spend most of their time on non-selling tasks. High travel costs plus low sales opportunities equals serious trouble for companies including an increase churn rate as frustrated employees resign.

Spatial optimization techniques can help companies avoid this situation, however, and design territories based on highest revenue potential with lowest amount of travel possible, which, in turn, can decrease operational spending while increasing sales productivity.

Three location data services helping design efficient territories are:

• Routing: an analysis providing shortest route based on mode of transportation
• Geocoding: an analysis providing geometric coordinates for select location
• Isolines: an analysis providing accurate time of travel per route within selected location

Advanced routing capabilities can help operations managers with fleet tracking, sales managers with productivity, and sales reps with meeting quotas all of which depend upon sales territory design.

What’s Next

We’ve covered a lot of ground related to designing a sales territory with a location intelligence platform focused on (1) choosing boundary units that align to your company goals and objectives, (2) building dynamic data visualizations to ensure territory balance and alignment, and (3) optimizing designs using location data services.

Are you looking to create a new sales territory plan? (https://carto.com/request-live-demo/)[Schedule a call] with one of our territory management specialists to get started.

Online retailers have become experts in tailoring consumer experiences, building stronger relationships with customers, and launching targeted ad campaigns by collecting data.

Yet marketers and online retailers still struggle to prove ROI of their advertising campaigns and to know how to leverage the data about their customers in meaningful ways.

We’ll show you one company that’s doing it right.

Recently, an ecommerce site based out of New York City shared with us all of the data from its latest advertising and product campaign.

The company was rolling out a new product line and had launched Facebook ads in a 25-mile radius of what they believed to be the metro areas in the U.S. with the highest concentration of past customers.

While their overall campaign was a success, visualizing and analyzing their sales data allowed them to do three key things:

1. Identify highly concentrated locations of customers.

A city is more than an address.

When predicting customer behavior, one of the most common attributes for marketers to consider is the street on which they live. In reality, though, a company should be looking at the entire metropolitan area.

Cities are designed to accommodate flows of people, whether they’re driving to work, going out to a restaurant, or shopping at a mall.

The ecommerce company asked: What are the largest metropolitan areas where my customers live?

Within CARTO, we plotted yellow dots to represent all of the individual sales for their product campaign, as shown in screenshot above. (We’re including some screenshots and some interactive map embeds to protect individual privacy).

We then intersected those dots with a layer of urban areas (obtained through Census and stored in our Data Observatory). This provided an incredibly accurate view of sales by city (in the interactive map below), as it aggregated suburban areas that might have a different city shipping address listed into the major metro area.

As you can see from the widget next to the map, if the user zooms or adjusts the map, the highest revenue cities automatically update. They could also adjust the sum_total_spent to show only cities with sales over a certain threshold.

2. Measure the ROI of location-based Facebook ad campaigns.

Every marketer needs to show the impact of their ad campaigns, and attribution tracking can be challenging to get 100% accurate.

The ecommerce company asked: How are my ad campaigns influencing total revenue for this product launch?

As a reminder, this ecommerce company’s Facebook ad campaign targeted 25 miles radius’ of over a dozen cities. By mapping those radiuses and intersecting them with sales over the duration of the ad campaign, the marketer in charge of the ad campaign can show all of the sales that their ads influenced. In this part of the United States, 82% of sales (550k out of 670k) came from within ad-targeted regions.

That’s valuable information for a marketer looking to optimize and report on their ad campaigns.

3. Identify potential new markets for ad campaigns and stores.

Birds of a feather shop together.

The ecommerce company asked: Where are there new areas that I should focus my marketing efforts to increase total revenue for the company?

Like every retailer, the ecommerce site is looking to expand its business, and they were shocked to see many customer-rich regions that they had not included in their advertising campaigns. By overlaying the Facebook ad targets with the highest value metropolitan areas, we can see a couple of things:

• Minneapolis is one of the biggest metropolitan areas in terms of total revenue, but it is not part of the Facebook ad campaign.
• Several smaller cities are included in the Facebook ad campaign, though they aren’t in the top 20 most valuable metropolitan areas. That doesn’t necessarily mean they aren’t good targets, but the digital marketer running the campaign may want to shift ad budgets to areas with more spending.

Additionally, by looking at the individual sales data, we can see additional potential ad areas outside of the urban zone. For example, the marketing targeted at San Francisco missed both the bulk of customers living in San Jose and all those living in Sacramento and the targeting in Los Angeles missed a bulk of people in northern counties.

By plotting shipping addresses on the map, the ecommerce site could see the deficiencies and also draw surprising insights.

Location Intelligence for Online Retailers

Online retailers have the data necessary to make insightful, strategic decisions — but only when the analyses are as good as the data. Visualization and location intelligence can reveal clear, actionable insights that help businesses fully engage with every customer.

Are you looking to execute more effective online ad campaigns and grow your retail business? Schedule a call with one of our ecommerce specialists to get started.

Sales and marketing directors all ask the same question: “How do I get my products or services in front of the right people at the right time?”

The key to any go-to-market strategy that answers this question is customer segmentation (also called market segmentation), the process of dividing a client base into groups of potential customers that share similar characteristics and needs.

Traditionally, customer segmentation has faced three main challenges:

• Segments were based entirely on a company’s internal sales data, which was inconsistent, incomplete, or even incorrect.
• Segmentation required a large investment in third-party data to create customer profiles, a process that was prohibitive for both sales and marketing departments.
• The third-party data was often based on small samples created in surveys or panels, which generated insights on what people say they do, not what they actually do.

The foundation of a data-driven, go-to-market strategy is, literally, data. More specifically, sales and marketing directors need data related to (1) potential client base, (2) location of potential client base, and (3) available resources to allocate to the location of potential client base.

These are all challenges Location Intelligence solves. By enriching existing sales territories or more general geographic areas with publicly accessible demographic, geographic, and behavioral data, any industry can effectively identify and optimize their customer segments.

Let me show you a few examples.

Customer Segmentation for Retail

A recent survey found that 60 percent of retailers have difficulty boosting sales and promotional offers by leveraging geographic and socio-economic demographic data. Geographic and demographic data happen to rank among the most popular types for retailers segmenting a potential client base.

In the visualization below, we see the city of Chicago, Illinois with two layers of data (from our Data Observatory):

• first, we pulled in a layer of census tract data to set spatial boundaries;
• second, we enriched those boundaries with multiple demographic measures about Chicago residents such as age, gender, race, and annual income.

This map above is a good starting point. You can adjust the widgets on the right to explore the different measures used to enrich the census tracts and the default view shows an auto-styled histogram of income levels.

However, retailers need a more granular view.

To dive a little deeper, with the click of a button, we applied the Spielman and Singleton algorithm, a popular clustering procedure for generating customer profiles, often paired with open U. S. Census data.

While we could populate up to 55 different clusters, we decided 10 clusters would be sufficient for this market segmentation. We applied a divergent color scheme to easily identify areas by average annual income from highest (red) to lowest (blue).

Retailers serving price-conscious customers might want to consider areas shaded yellow and orange:

• Yellow: White and minority mix, mixed multilingual, mixed education, and mixed Income
• Orange: Renters within cities, mixed income, White and Hispanic Mix, and Unmarried

This level of customer segmentation provide sales, marketing, and operations directors a better understanding of a territory’s context, helping them answer the question: “How do I get my products or services in front of the right people at the right time?”

Customer Segmentation for Transportation

As urban migration continues to strain transit infrastructure and congest traffic highways, urban developers and city planners are turning to location data for help.

Solving these transportation issues requires an understanding of commuting routes and commuter behaviors.

For example, Transport For London developed the Transport Classification of Londoners by visualizing and analyzing location data. By understanding and clustering seven key variables like travel behavior, use of mobile phones, and income, they came up with customer segments with nine different customer segments.

We created a visualization below of San Francisco, California to show similar data. The map is built by importing layers of census tract data and shows behavioral segments using location data on mobility and commuting patterns. We also visualized this data with a sequential color scheme which was chosen to shade volume from light to heavy along a multi-hue palette.

Anyone working in transportation could easily create a similar visualization to better understand and categorize their customer segments.

This type of segmentation is especially useful for fleet routing and tracking, a pain point few go-to-market territory management strategies adequately address. It can help build more accurate predictive models that can assist route optimization.

If an operations director is looking to reduce fuel costs and travel time, for instance, then one potential option would be to consider rerouting deliveries through areas where residents own fewer vehicles as it is likely to have less traffic congestion.

What are your segments?

A lot of ground has been covered in this post, and I added links throughout on additional resources to specific topics covered in more depth.

Clearly, customer segmentation is pivotal in commercial strategy across a wide range of sectors, including many that we didn’t even cover in this post (like healthcare). But sales and marketing leaders will continue to struggle if they don’t include new, location-driven data sources in their segmentation.

If you have any specific questions, please feel free to schedule a call with one of our territory management specialists today!