Hello dear reader! Before we begin with Locale nostalgia, we would love to wish you a fantastic new year.

Well, 2019 has been an amazing year at Locale. It seems as though we have been on a tremendous warp drive. From product conception to building, to setting up a team and raising a pre-seed round, we have accrued quite a lot of experience. We are committed to making the geospatial industry more robust and mainstream.

In the spirit of growing the community, we started writing and spreading awareness about geospatial data and we have received immense love from readers all around the world.

So, in this blog post, we surmise the best articles we have produced in 2019 to satiate your delectation. Enjoy the read.

1. [Data Science] Spatial Modelling Tidbits: Honeycomb or Fishnets?

At the very top, many companies ignore putting lat/long data to perform city level analysis.

Even if they do, city level mapping is very broad. Our cities and wards or boroughs within them demand arbitrary boundaries. As a result, city level mapping gives you aggregate insights, if you need to know what’s happening on…say…Street 5 at 4 PM, you need to go many levels deeper. In fact to make sense of spatial data and extract business insights you need to go granular.

Spatial data analysis tools use shapes such as squares, equilateral triangles or hexagons to draw grids on maps. The idea is to cover the entire map with shapes aligned next to each other without overlapping. Each shape will be assigned a unique identification value. And then you use those unique IDs to analyze what’s happening in that particular grid. However, squares, triangles and hexagons have different advantages and limitations. In this blog post, Aditi analyzes each shape and makes the case for the ideal choice for street level, granular analysis.

Spatial Modelling Tidbits: Hexbins vs Geohashes?
Why we at Locale.ai are fond of hexagonal grids?

2. [Engineering] Architecting Vuex store for large scale Vue.js applications

At the heart of all large-scale Vue.js application lies the store which holds all its data. The Vuex store in a Vue.js application acts as a single source of truth which provides great performance and reactivity out of the box. As your application grows in complexity and code, Vuex stores get easily cluttered and become hard to manage. Architecting the state management of your application considering best practices can solve most of the problems that grow with complexity.

In this piece, we discussed some of the best practices and tips to architect state management on a large scale Vue.js application. We have covered concepts to help architect your store better including - structuring the store, modularizing the store, auto importing modules, resetting module state and global module state reset.

Architecting Vuex store for large scale Vue.js applications
Best practices and tips to architect state management on a large scale Vue.js application

3. [Product] How we created a location intelligence analytics tool using first principles

Let us begin with a business case you might be familiar with. Let’s say 85% of your users drop off at a certain price point. Allowing you to make a clear case to revisit your pricing or promotional strategies. Or say, half of your delivery partners are idle for more than 9 minutes between 3 PM and 5 PM in area x. This analysis enables you to make the right set of business decisions and quickly optimize resources and boost sales.

You see, the need for analysis, let us put it this way, the need for granular analysis has the power to change the way you run your business by-the-hour. Right now, your data science team might be tinkering with open source tools to whip up real-time, spatial insights which makes sense to your business case.

However, does your data team have access to the right kind of data, are they visualizing data in the best way to drive insights which will help you make business decisions in real-time? In this blog post, Aditi puts emphasis on creating a geospatial analytics tool using first principles. A tool built from the ground up based on accessibility and visualization of data and generating intelligent, actionable insights on-the-go.

How we’re building our geospatial analytics product using first principles
Our philosophy on analytics at Locale!

4. [Business Use Case] How Food Delivery Companies Leverage Geospatial Data!

Swiggy, Zomato, Postmates, Deliveroo — the list of apps which deliver food and/or groceries under an hour is growing by the year. As customers, we expect our food to be delivered even sooner and at a cheaper cost. As a result, hyper-local delivery businesses have their task cut out. They have to keep up their delivery standards, yet carve out more revenue each year. How do they do this?

As food delivery companies set about servicing hyper-local deliveries in dense cities, they live and breathe by geospatial data to optimize their operations. Anubhav writes a terrific account of how food delivery businesses put location data to use and solve problems in real-time.

How Food Delivery Companies Leverage Geospatial Data
Interesting use cases of geospatial data in last mile delivery

5. [Inside Locale] Why I chose Locale.ai for my internship (and why you should too!)

This is an honest account of Anubhav Pattnaik, our Data Science enthusiast and one of the content contributors at Locale. Anubhav has always been a keen data science guy. This post chronicles his attempt to work as an intern at Locale and primarily what drove him to apply. Anubhav is a great asset for Locale and in this post, you can read more about how he went from application, rejection and to becoming one of the cogs in the Locale machine today.

Why I chose Locale.ai for my internship (and why you should too!)
Why being a part of the Locale journey (despite getting rejected) was so rewarding!

6. [Business Use Case] Site Planning using Location Data

Starbucks is no run-of-the-mill coffee brand. We may like their coffee or we may not. But we cannot argue about the brand’s success. Starbucks is literally everywhere. Hang on a minute. Saying that Starbucks is everywhere is a bit lackadaisical, as the company doesn’t randomly open its outlets across cities and neighborhoods willy-nilly. There’s science behind their expansion. And at the center of this science, is spatial data intelligence.

In this post, Dhrumil Patel elegantly puts forward a case of how Starbucks uses internal and external data to build a spatial intelligence model. A model which has been accurately predicting locations for new stores and reallocating existing ones. This is a fabulous read.

Site Planning by Starbucks using Location Intelligence
How Starbucks uses Location Intelligence to plan their next store location. Let’s see how they do it and what are some additional parameters that they consider.

7. [Data Science] Getting Started in the Geospatial World

If you are a data scientist, data engineer or a data enthusiast working hard to build a career in data science, you would want to learn how geospatial data upends business operations at modern day businesses. We understand that there’s only so much one can learn from theoretical or anecdotal content.

Long before we built Locale, we were data boffins like you. We scoured the internet to find resources which could help us understand and learn gold standards of location data intelligence. We learned first hand that geospatial data tech moves at light speed and at a hundred different directions.

With our hands-on experience and the context of business cases, we started creating content to help fellow data science enthusiasts to learn anything under the data-science-sun. If you are one among them, then you should bookmark our blog. To begin with, here’s a thoroughly detailed article about the foundations of GIS, it’s types and use cases — written by Dhrumil.

A Guide to Kickstart into the Geospatial World.
A collection of the best data sources, open source tools and packages to get started.

8. [Design] From Thoughts to Visuals — How did we design our geospatial product at Locale.ai!

At the core of deriving actionable insights from location data is — visualizing data. Think about it, you work so hard to collect and sanitize data, define the business problems you want to solve, and analyze data to uncover hidden patterns. You also hire a data science team to work with the data. The crucial part of every data team’s function is how they visualize findings in a way that business leaders look at the data and make real-time decisions.

Our in-house UX engineer Mushtaq is at the crux of designing a geospatial analytics tool — which will be used by data scientists and business leaders alike. Here’s his account of how he works behind-the-scenes at Locale to transform thoughts into visuals on our analytics tool.

From Thoughts to Visuals —How we designed our geospatial analytics product
How do we apply “design thinking” to bring our ideas to life at locale.ai?

9. [Product] A Product for Operational Analytics using Geospatial Data!

There’s a saying in the GIS community — “everything happens somewhere.” From Starbucks to large scale hospitals — businesses are infusing product and service delivery at the customer’s doorstep like never before. Hyper-local and mobility businesses too have played their part. Collectively, they have given birth to the gig-economy, where partners such as drivers and delivery folk have become part of the wider economy today.

More partners, products, and locations bring increased complexity to delivering products and services. Since each action from placing the order, servicing it, to the route taken to deliver it — geospatial data is intertwined into every business action. The trouble is that businesses find it hard to analyze geospatial data, visualize it and derive actionable insights on BI tools available in the market today. They are all too…one-size-fits-all.

Current day BI tools are extremely limited in plotting real-time location data across a LIVE map. This very problem is the genesis of Locale. Aditi makes a case for building a bespoke product to analyze, visualize and help businesses solve problems in real-time using geospatial data. This is an excellent read.

A Product for Operational Analytics using Geospatial Data!
What led to the birth of Locale.ai?

Now that you have come this far, we would love to tell you that at Locale, we are committed to share industry knowledge and best practices regularly. That’s why we will continue to post content to bring a lot of value to you. So, bookmark our blog without fail. :)

If you want to delve further, check our website out or get in touch with me on LinkedIn or Twitter.