Two massive shifts in infrastructure are changing the way we live today: the electrification of transportation and the development of ultra-fast networks using 5G technology. Both processes require accurate, data-led location planning, not only to know where people live, but also to understand where they move, work, and stay.
At this point, the importance of so-called POI data (Points of Interest data) comes into play. POI data is not only numerical coordinates plotted on a map, but POI datasets represent rich, contextual insight about any location, from what kind of businesses occupy it, to what volume of customers visit it, when they visit, and how long they stay.
In the battle to install EV charging stations or 5G small cells, POI data acts as the strategic driver supporting planners to:
• Identify areas of high customer demand
• Optimise site selection
• Minimise roll-out costs
• Minimise regulatory timeframes
• Enhance customer experience
This article will show how POI data and location intelligence allow for the more innovative and more rapid roll-out of both EV and 5G networks, and how these are driving the fundamental building blocks for the smart cities of the future.
What is POI Data and Why is it Important?
A Point of Interest (POI) is a location that is of interest for some reason, such as a restaurant, parking lot, office, or public park. Modern-day POI datasets are characterized by:
● Geospatial characteristics — latitude/longitude, borders, building footprints
● Categories — type: retail, lodging, office, industrial, etc.
● Attributes — capacity, parking availability, hours, accessibility
● Ownership and brand data — name of operator, chain, or franchise information
● Behavioral metrics — visit frequency, time spent, time of day of activity
● Data freshness — update date information, quality metrics
For the infrastructure planners, this means an understanding not only of where people go but why and an accompanying adjustment of infrastructure deployment.
POI Data in EV Charging Network Planning
- Targeting the Right Location
Successful EV charging requires placing chargers where drivers regularly stop and stay, not just in densely populated areas.
Places of Interest (POI) data would inform locations like:
● Shopping centers (which can have 30 – 90 minute dwell durations)
● Places of business and offices (which have long-stay parking during the working week)
● Hotels and Leisure locations (for overnight charging needs)
● Grocery Stores and coffee places (quick turn, fast charge needs)
By determining visitation patterns and POI category type, EV operators can match the type of charger to the way the user behaves, ensuring a higher return on utilization and investment.
- Filling Coverage Gaps
Through POI mapping, locations of high traffic and commercial or community activity, but missing chargers, can readily be identified.
Example use cases:
● Suburban retail strips that enjoy steady weekend traffic (not just supply),
● Industrial parks are enjoying EV fleets, but without depot charging,
● University districts or hospitals, which have extended requests for parking.
By taking both existing charger data and POI density maps, the result shows where demand is growing in anticipation of supply, enabling operators to zone in on high ROI locations.
- Streamlining Site Acquisition and Permitting
POI datasets have ownership and business metadata to ensure site planners can more quickly locate viable partners and navigate the regulatory framework.
● Identify some potential private partners (retail chains, hotels)
● Minimize restricted areas (heritage sites, utility easements)
● Evaluate electric proximity and grid interconnection alternatives
It minimizes the time to approval of new charging sites, an essential competitive advantage in the expansion of the electric vehicle network.
- Enhancing User Experiences with POI Context
Location context means confidence for EV drivers. POI data opens up opportunities for charging maps and apps to deliver rich experience content, such as:
● Notable things to find on-site (restrooms, coffee, WiFi, etc.)
● Access and parking information
● Business hours and security aspects
Contexts make simple charging maps charge-experience platforms designed to encourage adoption and enhance customer satisfaction.
- Aiding Predictive Energy Management
Electric utilities use POI-enhanced behavior models to predict where and when to expect spikes in charging load. By relating the activity at points of interest (for example, foot traffic in malls or business hours) with charging usage, planners can anticipate peak loads and establish intelligent demand-response methods.
POI Data in 5G Rollouts
- Mapping True Demand Hotspots
Unlike 4G, the small cell of 5G has a short range and therefore needs a dense and local deployment.
POI data enables the telecom planners to pinpoint micro demand clusters, such as:
● Stadium and concert visits
● Business and co-work
● Shopping center and transport nodes
These hot spots have extreme peak demands for data at certain times, and POI data will ensure that the necessary 5G capacity is built where it is mainly needed.
- Finding Sites for Backhaul, Power, and Access
POI data is often complemented by structural/business data, which indicates power access, rooftop access, and where there are existing fiber lines.
Through the combination of POI data with utility and zoning layers, telecom operators can:
● Eliminate unfeasible sites at an early juncture
● Negate high costs associated with field surveys
● Speed up licensing by finding compliant assets such as lamp posts, rooftops, and utility corridors.
- Competitive Market Insight
POI records are not strictly technical but commercial. Telecoms use POI datasets to get better ideas on enterprise customer footprints and the presence of competitors:
● Identify unserved corporate customers within evolving coverage gaps
● Optimize network quality in high-value, strategically important areas.
● Target B2B capabilities towards areas of dense business activity.
It is this geographical market intelligence that leads directly to revenue and customer retention benefits.
- Modeling Real-World Network Behavior
Combining POI data and mobility data allows planners to model actual human movement and data consumption behavior.
Example:
A stadium POI with historical attendance and mobility data can help operators predict congestion spikes during events, allowing for dynamic capacity allocation or temporary small-cell deployment.
The POI-Driven Deployment Workflow
Enterprises typically execute the deployment process in structured rollout phases with a POI scheme:
● Define Goals — utilization objectives, coverage targets, or cost outlays to be saved.
● Assemble POI Data — from verifiable suppliers, where the taxonomy is standardized.
● Enhance Context — with mobility, traffic pattern, and utility data overlays.
● Filter & Rank — by a multi-criteria scoring system (power access, pedestrian traffic, ease of permitting).
● Field Verification — survey the best candidates to see if they are feasible.
● Monitor & Learn — putting real-world usage data back into the models for improvements in the next iteration.
This closed-loop planning process leads to quality deployments, less waste, and better reinvestment decisions.
Real-World Examples & Vendor Capabilities
Mapbox and CARTO: In the case of location platforms, tools enriched with POIs are embedded for EV trip planning and telecom spatial analysis. The platforms provide APIs for simplified charger discovery, routing, and the spatial analytics needed by telecom teams for small cell placements.
Coverage intelligence providers: Companies that deal with crowdsourced metrics of networks, together with POI layers, now provide the operators with indications of where experience gaps match venues in the world, so that you can assign priority for fixing them.
Research projects: In some recent academic work, it shows that EV charging demand models that figure in POI features (retail density, workplace clusters) are considerably superior to those based on pure population or vehicle counts. Such models allow better grid-integration planning since they forecast temporal charging load patterns with acceptable spatial resolution.
What Are The Challenges and Pitfalls of POI Data?
Data Freshness: Using stale POIs can result in misinformed choices for action. → Use providers that provide validated update cycles.
Inconsistent taxonomy can hinder analytics: A normalizing of classifications should occur beforehand.
Not recognizing physical resource constraints: A “perfect” site may have insufficient power or right of way. Cross-matching customarily happens with utility and cadaster data.
Privacy concerns in compliance: Ensure compliance with GDPR/CCPA provisions when layering location-based POI data with mobility data.
Overreliance on models: Always ground truth analytical insights based on data. By creating hygiene in such data measures and compliance, an organization can fully enjoy the opportunities afforded to POI-augmented infrastructure planning.
Evaluating Impact
EV Charging Networks
● Utilization Increase: 2-3× increase in usage where POI-based site selection is implemented
● Site Selection Speed: Plausibly a Reduction in Site Selection Timeframes of 30-50%
● Customer Experience: The amenity context and access is enhanced
5G Rollouts
● Optimized Capital Utilization: 20% number of sites required to provide same area of coverage
● Improvement in quality of service Metrics: Latency, throughput in priority zones
● Faster Permitting Cycle: Improved access to viable rooftops and street accessories
City Planners and Regulators
● Equal Access: Data-driven placement of infrastructure
● Sustainability: Decreased redundancy, improved integration of grid
● Smart City Alignment: Unified planning of infrastructure via common POI layers.
Future Trends: Evolution of POI Data
The future of POI data is dynamic, intelligent, and predictive. Expect to find:
● Real-time POI data with live volume, event and parking data
● Semantic (POI) with infrastructure-specific attributes (number of EV stalls, rooftop access, power capacity) development forecast models for future growth in demand (i.e. growth forecasts based on development of POIs relative to human behaviour)
● Digital twinning that ties the POI layers to networks associated with traffic (transport), energy and telecommunication systems via unified dashboards in the innovative city environments.
The fusion of POI, AI and IoT sensors in the context of EV charging and 5G networks will be maximally adaptive, sustainable and human-centric in nature.
Conclusion: Data-driven Infrastructure Enabling Connected Futures
The opportunity for success in respect of EV charging networks and 5G rollouts is predicated upon a universally acknowledged truth — infrastructure works best when it is sited to coincide with the location of real people. POI data translates that truth into actionable measures for strategy. For EV operators, this means: installing chargers that replicate travel or dwelling patterns that exist in the real world. For telecom operatives, this means locating the small cells and base stations where there is a maximization of the demand for data traffic. The power of POI data and its employment in conjunction with location intelligence to ensure that deployment is optimised as to speed, cost, and user experience is not to be underestimated. The above-mentioned aspirations of Cities to achieve data-smart and sustainable connected infrastructures are greatly aided to fruition by POI data. The future of mobility and connectivity is location-driven, and POI data is the map that takes us there.
