The “Problem”: A testimony to why we do what we do at Locale!
Our secondary research with its moments of inspiration.
Here’s a question for you to think about today:
When the going gets tough, what pushes you to show up everyday?
Early-stage startups are hard! At Locale, we ask ourselves this pretty often: What keeps us going forward when times are bleak? One word answer: the problem.
The problem is real, my friend. It’s something we have experienced first hand in our previous avatars. However, while working with companies, talking to industry experts and researching what the most successful companies have done, we have learned that the problem is much deeper.
Previously, we have talked about the problem enough — what we have learned while working with companies and having conversations with domain experts. You can find some articles down below:
In this piece, we want to talk about our findings from secondary research — what has the world been talking, writing, and experiencing about the problem we are dabbling with at Locale.
- Section 1: Learning from the Best Companies
- Section 2: The Disrupting GIS Industry
- Section 3: Location Coupled with Different Industries
- Section 4: Successful Strategies through Analytics
Section 1: Learning from the Best Companies
1. Uber: On the Tedious, Inefficient Process
“Maps are based on our physical world. At Uber, we leverage data visualization to better understand how our cities move. Our solutions enable us to embed maps with rich location data, render millions of GPS points in the blink of an eye, and, most importantly, derive insights from them.
No matter the frameworks or tools used, creating interactive visualizations follows a similar process: data collection, data processing, visualization exploration via web-based tools such as QGIS, Carto, and Mapbox Studio, and then porting the visualizations into Javascript to build prototypes.”
Not only is the process tedious, but it may or may not reap useful visualizations. In many cases, there are more visualization ideas than there are time and people to make them.
2. Uber: On Scalable Maps Infra
“At Uber, we use maps for everything — visualizing millions of geodata points, monitoring road conditions, and advocating for policy change in cities around the world. Many teams across the organization producing map visualizations for a wide range of needs.
The way we’ve made maps in the past worked when the company was smaller. Each map was created for specific purpose. However, as the company grew it was no longer enough to have individually great maps. We needed a framework that made sense for a global scale.
Individually, each map was successful, but as a family, they lacked consistency. That was a core issue we wanted to address.
We thought it would be useful to have a starting point for teams across the org to scale up and make better maps, faster.”
3. Airbnb: On the Importance of Location
“At Airbnb, the question we want to answer is how do you understand a place without ever having been there? How do you know where in the world will make your perfect trip?
Location is a crucial building block for a number of our engineering efforts. Our team needs to help the Airbnb traveler mitigate their number one concern when finding a place to stay: location.”
Building the future of travel requires a number of special spatial tools working together in the background.
Section 2: The Disrupting GIS Industry
4. Carto: On Geospatial vs GIS
“Maps are no longer static assets on the web. Location data isn’t limited to simple pins on Google Maps. Now, we see compelling maps that mix advanced visualization and powerful web cartography in our day to day lives.
GIS is exploding, and our industry has never been bigger than it is now — with a growing number of players not only providing cross-industry platforms, but also niche industry geospatial specialists. This means that GIS is happening where we’re not used to it happening — outside of traditional GIS tools.
Geospatial is no longer a special niche, it is blending in with other technology. This means that GIS is happening where we’re not used to it happening — outside of traditional GIS tools.”
5. Forbes: On the Need for a Horizontal Product
It is, however, safe to say that the activity of leveraging data that holds a geographic reference has always been challenging to compartmentalize. The reason for this is that geography is quite finicky and that the details matter.
Geospatial could be a pervasive layer supporting virtually every business vertical. But we continually think of ourselves as a vertical. We think of the GIS industry. This thinking silos and separates us.
But GIS has typically done a poor job of building robust, highly repeatable geospatial workflows. We need to start building tools for the robust distribution of geographic workflows to a cloud-based environment.”
There is a dire need for a horizontal cloud application that can handle spatial analytics.
7. Geospatial 2.0: On Different Kinds of Players, User Personas & their Needs
Innovators in this Geospatial 2.0 environment have focused on delivering a ‘one-size-fits-all’ platform-as-a-service (PaaS) for analytics.
Much of the value-add of geospatial analytics is in the simplification of previously complex and/or time-consuming processes for geo-aware uses and going from raw sensor data to refined signals serving business insights.
The opportunity here is significant. A survey of market forecasts indicates the current size of the geospatial analytics market is somewhere between $35 billion and $40 billion, with forward looking 5-year CAGR of 14–17% — and a market projected to hit $86 billion by 2023.
While there has been an initial focus on ‘low-hanging fruit’ applications, such as precision agriculture, finance and defence, there are huge markets where uptake of geospatial products will drive billions of dollars in value, ranging from insurance, climate change, supply chain management and intelligent city management.
7. DaniArribas-Bel (University of Liverpool): On Geography and Data Science
A lot of modern Data Science, to put it bluntly, doesn’t really care where things happen, all, vacuum-style. We think there’s a lot of value in making space a first-class citizen in ML/AI/DS because it very explicitly brings together the two communities we’d like to engage with each other: Geography(/ic Information Science) and Data Science.
For this, we could avoid reinventing a few wheels by looking at what the spatial analysis, spatial econometrics, and Geocomputation literature have cooked over the last decades. But only that is not enough as they weren’t designed with new forms of data in mind.
The choice is whether the combination of Geography and DS should exist. This is already happening whether you like it or not, and we don’t see stopping any time soon!
Section 3: Location coupled with Different Industries
8. Hypertrack: On the On-demand Economy & Location
Every industry that involves people or things on the move — transport logistics sales teams service fleets social networks etc. — wants to use dynamic location. And the “location stack” is a primary building block in these larger macro trends.
Every industry that involves people or things on the move — transport logistics sales teams service fleets social networks etc. — wants to use dynamic location. As people consume products and services in an increasingly mobile world understanding the context within which these should be served and delivered becomes important.
Location is not about a point. It’s about a line. There is no movement without dynamic location.
9. Pando: On Logistics & Location
Since the way shippers currently operated was not data-based, giving them analysed historic data, on-demand would be useful.
“For example, a (large, public-listed) paper manufacturer in Tamil Nadu faces an interesting problem — their distributors in North India collude with the drivers delivering their shipments from TN, to sell the rolls of paper en route, making 2x the profits, and disrupting the local markets in those routes.
Loading and unloading happen at specific times at different locations — and delays mean demurrage borne by either party. Proactive ETA allows all stakeholders to plan their activities, and reduce costs.”
Our approach to location-tracking
How we tailor location-tracking features for our clients.
medium.com
10. Colorado: Transportation and Location
Location intelligence is integral to a holistic understanding of the transportation network of the past, current, and our connected network of the future. Safety, congestion, mobility, sustainability, infrastructure — location data helps inform us of our surroundings with unprecedented accuracy.
Location-based apps provide one way for CDOT to turn raw data into actionable knowledge. Once data is exposed, it is no longer locked in silos. Data is seen in a whole new way, enhancing accuracy and generating insights.
Rapid population growth in Colorado drives more focus on traffic, congestion, and safety. In response, CDOT staff are increasing real-time data streams to improve operations and maintenance activities. By collecting historic data on incidents, as well as connected vehicle data such as hard braking and lane departure, the CDOT team can help identify which locations to address proactively.
“Linking location with the power of visualization provides invaluable input for the discovery of trends, patterns, and insights,” Cohn said. “Visualizations can help distill increasing amounts of data, rendering complex data into images that maximize understanding.”
11. Zoba: On Microbility and Location
The world is experiencing a Cambrian explosion of smart mobility and logistics services, all requiring geo-based forecasting and optimization. A great analogy for this is the transformation we saw from Blockbuster to Netflix in terms of a mobility scenario over the next 10–20 years.
The mismatch between demand and vehicle allocation is not usually stark; mobility operators have some intuition about where users would like to start rides and can position their vehicles accordingly.
But, it’s possible to do better than intuition. If we can estimate the demand for a mobility service, with data-driven methods and mathematical optimization we can discover vehicle allocations which optimally serve user’ desires — whether “optimal” means maximizing rides, maximizing revenue, maximizing accessibility, or any combination thereof.
Section 4: Successful Strategies through Analytics
12. Lyft: Philosophy on Analytics
City managers tend to have time-sensitive questions specific to their markets that should be self-serve for all their needs.
For example, if I’m the General Manager of Los Angeles, I need to know whether the airport promotions we run at LAX are delivering value to Lyft. In order to find that out, I might have to answer a question like, ‘How many rides happened at the airport last week?’
When I joined, we were sending Excel spreadsheets around over email. Thankfully, we’ve moved beyond that, but it took a tremendous amount of work. We had to build out a significant data engineering team to make it possible.
Building an analytics team without supportive functions in place is like driving a car on an empty tank — a recipe for disaster. Without scalable infrastructure, queries grind to a halt.
Without reliable data logging, analyses turn out inaccurate. And without the proper data visualization software, people waste time running the same analyses over and over again.
13. Manik Gupta (Uber CPO): On Localization of Strategies
Knowing that Uber is spread across so many culturally distinct places, Gupta wants his teams to build what’s right for the world around them rather than trying to make Uber the same everywhere.
One of the things I learned back at Google is that you really have to empower teams that are locally situated.
Almost 25 years into digital mapping, we’re at an interesting crossroads. Computers have also gotten really good at these tasks: GPS pinpoints our current location, geocoders can look up the street address, and navigation apps plan cross-country trips in a split second. Together, they assure us that we’ll never really be completely lost, and every destination is somehow reachable — Mapbox
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Originally posted here.
