Finding 'Where to be?' with four Operations Mapping workflows
- Arpit Shah
- Dec 31, 2024
- 12 min read
Updated: Dec 5
Introduction
From seeing potential in linking Mapping with Operations back in 2018 to running Intelloc Mapping Services - the business entity behind Mapmyops.com full-time after the pandemic, I feel genuinely excited whenever I receive responses to my email campaigns. Prospects often reach out wanting to understand what my firm does, how it could benefit their organization, or seeking clarification on a workflow. Many even request demonstrations—drone trials, web map applications, virtual presentations, site visits, and so on.
Since my firm offers a wide variety of solutions, it isn’t feasible to provide fully customised demonstrations at no charge. Instead, I prefer to codify useful workflows into articles + videos on this website and share the ones that closely address a prospect’s query. I feel more inclined to serve someone who, after reviewing my content and understanding how I work, finds the idea of engaging with my firm meaningful and potentially beneficial.
Another advantage of having 50+ workflows published online is the global readership it attracts. Students and young researchers often write to me seeking to understand a methodology or access a dataset used in one of my posts. Supporting them is something I genuinely enjoy, and these interactions help expand my professional network. I hope some of them become collaborators on exciting research opportunities in the future.
In this post, I’m covering four such workflows—hyperlinked to their respective sections:
At first glance, these topics seem completely disparate—they were indeed created for different prospects with unique requirements. Besides the common theme of what my firm does (Mapping Solutions for Operations Improvement), and the headline of this post—Finding where to be (which took me over an hour to arrive at!)—they have very little in common.
I could have written four standalone posts, as each topic is rich in content. But instinctively, I felt compelled to close the loop for all of them within 2024 itself—and start 2025 afresh! 🤩
Let's begin...
Modeling 'Green' in Supply Chain Network Design
In this workflow, I use a fictional example based on the Supply Chain of Kissan—an established Indian food brand—to demonstrate how to apply Linear Programming (via Excel’s Solver tool) to design an optimal Supply Chain that reduces CO₂ emissions. The network comprises three echelons: Agricultural Farms (Suppliers) → Kissan (Manufacturer) → Distributors (Customers).
Reducing greenhouse gas emissions supports UN Sustainable Development Goal #.13 on Climate Change which calls for urgent measures to limit global warming to 1.5°C above pre-industrial levels (we have already crossed 1.1°C).
The demonstration covers:
Optimizing material flows to minimize total supply chain cost
Modelling a 5% reduction in total GHG emissions (inbound + outbound CO₂)
Illustrating how the model adapts to the new constraint and re-optimizes flows
If you’re unfamiliar with Supply Chain Network Design or Linear Programming, I recommend reviewing my comprehensive work on the topic here. That said, this demonstration works fully as a standalone workflow.
Slider 1: Comparing the Optimized and GHG-Optimized Supply Chain Networks of Kissan
While mapping platforms have evolved to support highly dynamic web and mobile applications, one should not underestimate the utility of a well-made, printed 2D map.
A map visualising a supply chain network can serve as a handy infographic for senior executives and as training material for new joiners.
Route Mapping for Ascetics
Ascetics follow strict vows and lead a disciplined, spiritual life. In my culture, which lays strong emphasis on ahimsa (non-violence), the ascetics avoid residing in one place for too long, lest they develop attachment, which they strive to overcome through austerity. As a result, they travel barefoot from one location to another throughout the year (except during the monsoon season).
I had long wished to use my mapping capabilities to preserve aspects of Indian culture, though I didn’t know how that would manifest. One day, during a rare visit to a group of monks, a casual discussion about my work led to an unexpected request:
Since ascetics do not use technology or carry possessions, I pulled out my phone and looked up Google Maps. Three route options emerged for Parasnath → Ayodhya (550–660 km) and one for Ayodhya → Bhopal (~680 km). I estimated that to complete the journey in two months, they would have to cover 20–25 km daily.
"We are familiar with the route to Bhopal from Ayodhya, however, we've never visited Ayodhya from Parasnath before. Please elaborate the three options"
"Route 1 is the shortest and it goes via Azamgarh...
Route 2 is slightly longer and it goes via Varanasi...
Route 3 is the longest and goes via Gorakhpur. You'll encounter several pilgrimage sites on the way..."
"You are into mapping, aren’t you? Can you chart these options on a map? We will present it to the head monk who will decide the route."
"Sure", I said, sensing the glorious opportunity. "Can I come back to you in a couple of days?".
"That is fine. Please do not waste paper." 😊
On my way back home, my mind had already begun thinking through the open-endedness of the task:-
"What should I depict on the map?"
"Should I use Google Maps, Google Earth, or GIS?"
"How does one show a 500+ km walking route clearly on paper?"
"Should the labels be in English?
"How do I design a map that is not only useful for the monks on their journey, but also something they would genuinely cherish?"
The visual detail and navigation instructions on Google Maps—available even in Hindi and Gujarati—for travelling to Ayodhya from Parasnath were extremely intricate. The written directions alone spanned more than half a dozen pages, making me wonder:
"Is there a way to simplify this information without losing its usefulness? A smarter way to depict it on a map?"
To present only the relevant information and frame it aesthetically on a single sheet of paper appeared to be the biggest challenge. Using Google Maps to do so, despite it being an exceptional navigation tool, was not going to work out.
As a result, I turned to ArcGIS Pro, a full-fledged GIS (map-creation) platform, knowing well that I would have to build my own location dataset from scratch before reverse-geocoding it onto the map.
Given that the monks would need to travel ~20 kilometers daily, and have a resting place at evening, I deemed it necessary to depict a suitable stopover location around this distance interval on the Map.
Therefore, I began scouring Google Maps for urban, semi-urban, and large rural settlements along/near the routes that would be compatible with the distance criteria.
Fortunately, given that all the three Parasnath-Ayodhya routes traversed through densely-populated plains in the states of Jharkhand, Bihar and Uttar Pradesh respectively, I had little difficulty in finding them (Google Maps also labels settlements in ways that reflect their relative importance or size within the search extent—for example, notice how the village Aoura appears in capital letters at the 0:12 mark in Video 3. This feature made it easier for me to spot and select the largest settlement among the options).
Next, I proceeded to add coordinates and other relevant attributes to this dataset of shortlisted settlements, anticipating that this would make labelling and symbology easier later on-
Subsequently, using Google Translate, I created a Gujarati (native language for the ascetics) version of the Location Dataset-
I had anticipated that a map-based visual output alone would not be ideal—depicting all the information directly on the map would be unnecessary and would only create clutter. Besides, ArcGIS Pro does not support regional Indian languages. Instead, the table could be printed on the back of the map, in a language the monks readily understand, allowing it to serve as a handy reckoner.
Slowly but surely, the skeleton of my creation began to take shape, and I felt a growing sense of optimism...
While I had already identified the settlements that met the daily distance criteria, I wondered if I could take it a step further and locate an actual resting spot within each of them. I knew that during the monsoon season, when the ascetics remain stationary, they usually stay at a resting place attached to—or situated near—their place of worship. Their needs are minimal: a simple covered shelter. No beds (they sleep on the floor), no kitchen (they seek alms daily, typically from households of the same faith), no electricity or telephones (they avoid material and technological comforts), and no security guards (they own nothing that requires protection).
I figured that since temple-affiliated rest houses would not be available in every settlement, the monks usually relied on government schools, bus stations, charitable homes and similar places for shelter, obtaining information about these spots from locals during their journey.
If only I could find one temple per settlement, I wondered, the monks could visit it straightaway and obtain directions to a suitable resting spot, saving them time and effort while also allowing them to rest in a private enclosure.
My search was largely futile, though. Along two of the three routes to Ayodhya, I could hardly find any temples at all.
Having no option but to work with what I had, I moved on to visualizing the assembled Location Dataset on a map—the outer skin of my creation. This step, however, took far more time than I expected. These were the kinds of dilemmas I found myself grappling with:
Selecting an appropriate basemap was confusing at first. Should I show terrain, topography, aerial imagery, or keep the background plain?
Plotting roughly sixty settlements—all strung along a continuous path—posed a challenge. The data points clustered tightly on the map instead of spreading evenly across the extent, which made achieving a clean visual layout difficult.
Choosing the right symbology was equally tricky: What should the text size be? Which colours would best capture the hierarchy between villages, towns, and cities?
Basemaps often include reference layers (cities, rivers, roads), but deciding whether to retain all, some, or none of them required careful thought.
Navigation and directionality also needed handling. Displaying the exact walking path was not only technically arduous, but also it wouldn’t have added meaningful value—after all, hundreds of local roads and lanes criss-cross the journey. Mapping them would only make the output cluttered, unreadable, or partially invisible depending on the labelling logic. Even the reliability of open-source roads data (such as from OpenStreetMap) was uncertain. I realised I would have to compromise on visual aesthetics to ensure that only the most relevant information made it onto the map, especially given the limited space.
There were seemingly minor decisions too—such as whether to display water bodies. Several lay alongside or intersected the route. Would the monks prefer a path with fewer of them, given the flooding risks? I wasn’t sure.
Eventually, I was able to settle on this map design—
To summarize my decisions, I chose-
to keep label sizes relative to the type of settlement.
to use a single text colour for consistency.
to vary the colour of settlement markers based on whether it was a city, town, or village.
to include total distance elapsed on each label so one could easily estimate the distance to the next stop (with detailed values captured separately in the table).
to use a clean terrain basemap without reference layers or hillshade so the map remained visually light and free of unnecessary elements.
to depict only the main highways, and without labels—just enough to give the monks a sense of where the route aligns with a major road and where it diverges into the interiors. The data table carried the names of the connecting primary roads anyway.
to omit navigation instructions entirely due to lack of space. A general sense of direction—north, northwest, and so on—was still easy to infer from the map.
to mark temple locations with icons, labelled only with serial numbers that could be cross-referenced with the table printed on the back (which included the temple’s name, address, nearby landmark, and phone number, if available).
I printed one map each for the three route options to Ayodhya and another for the onward journey to Bhopal—four sheets in total, all on A3 paper. I preferred A3 over A4 because it preserved the visual clarity of both the map and the data table. “It should be comfortable to read and refer to,” I felt…
…and the monk agreed! He appreciated the effort and took me along to discuss the options with the head monk. Route 2, via Varanasi, was chosen as the most suitable—offering more and better-known settlements, stronger highway connectivity, and, importantly, a pilgrimage site the monks wished to visit. The shortest route (Route 1) was only 16 km shorter, so the trade-off was negligible.
P.S. The head monk even requested that I print just the data table for Route 2 on an A4 sheet so he could keep it handy during the journey!
Overall, I felt good about my output and the outcome in general. I hope the monks refer to the map during their journey and benefit from it. The design of a map is, in many ways, subjective—as long as the essential objectives are met. I remain grateful for the map-making principles I picked up through Esri's Learn ArcGIS tutorials.
If you have suggestions on how I could have made the map even better, I would be glad to hear it.
Terrain Change Detection for Physical Security Planning
This video workflow on Terrain Change Detection was part of a larger demonstration to develop a Geographic Early Warning System for the head of Physical Security at a major Indian conglomerate (other workflows such as Climate EWS and Line of Sight-based-Security Planning were part of it as well).
The business group in question has 100+ manufacturing units and mining locations across India—many of them very large, and some situated in remote areas. To prevent incidents of theft, and to ensure the safety of both personnel and assets, physical security teams are deployed in multiple shifts along the site perimeters, operating 24×7×365. In a discussion with another Security Head I had worked with earlier, I realized that the terrain surrounding a site significantly influences how security should be deployed. For instance, marshy stretches are naturally less prone to incursions, whereas areas with dense foliage provide cover that miscreants could readily exploit. Planning and allocating security personnel wisely—given that they are a limited resource—is therefore essential.
With this in mind, I prepared a demonstration showing how Mapping technology can be used to detect terrain changes around a site and generate valuable insights for security planners:
While I used a Vegetation Index (NDVI) on multispectral satellite imagery to classify the terrain and track areas with dense vegetation—and how they changed over time—I could just as well use a Water Index to monitor marshy zones, or derive an Urban Footprint utilizing Radar Remote Sensing to identify built-up areas around the site perimeter.
Since satellites typically prioritize temporal resolution (wide swath, short revisit interval) over spatial resolution (pixel size), a drone equipped with a multispectral payload is often a better choice for perimeter-level mapping. It offers far greater clarity and the flexibility to capture imagery as frequently as needed, enabling much higher-quality change detection.
Expanding Market Coverage for Consumer Goods Distribution
An organization’s growth potential is usually shaped by two forces: vertical growth (expanding its offerings) and horizontal growth (expanding its markets). Every month, I come across twenty-plus news articles centred on the latter.
I had developed the video demonstration below for a former employer who wanted to understand how mapping technology could identify locations with high distribution potential that were currently untapped by the field sales team.
The Location Analytics methodology used in the demonstration is straightforward and can benefit a wide range of organizations, especially those involved in consumer goods distribution.
Using Esri ArcGIS Pro, a powerful geospatial platform, I:
loaded the PIN-code layer of India and clipped it to the city of Mumbai,
enriched the PIN codes with population data,
loaded an open-source road network layer and clipped it to Mumbai,
filtered the network to keep only primary, secondary, tertiary and residential roads,
reverse-geocoded existing store coverage using a hypothetical list scraped from Google Maps,
compared store coverage against the population of each PIN code,
generated a 10-minute walking-time coverage for each store to approximate its customer reach, and
filtered out road segments lying outside these coverage areas.
Thus, where a PIN code had high population density, any uncovered road segments indicated areas with strong potential for network expansion and business growth.
Hope you enjoyed exploring these Operations Mapping workflows. Wishing you a happy 2025!
ABOUT US - OPERATIONS MAPPING SOLUTIONS FOR ORGANIZATIONS
Intelloc Mapping Services, Kolkata | Mapmyops.com offers a suite of Mapping and Analytics solutions that seamlessly integrate with Operations Planning, Design, and Audit workflows. Our capabilities include — but are not limited to — Drone Services, Location Analytics & GIS Applications, Satellite Imagery Analytics, Supply Chain Network Design, Subsurface Mapping and Wastewater Treatment. Projects are executed pan-India, delivering actionable insights and operational efficiency across sectors.
My firm's services can be split into two categories - Geographic Mapping and Operations Mapping. Our range of offerings are listed in the infographic below-
A majority of our Mapping for Operations-themed workflows (50+) can be accessed from this website's landing page. We respond well to documented queries/requirements. Demonstrations/PoC can be facilitated, on a paid-basis. Looking forward to being of service.
Regards,
