How to Choose the Optimal Location for Electric Vehicle Charging Stations Using GIS and Spatial Analysis
Finding the right location for electric vehicle charging stations is one of the most important stages in planning an effective EV charging network. To deploy charging infrastructure efficiently and profitably, companies need more than intuition: they need GIS, spatial analysis, open geographic data and a structured location-scoring process.
A data-driven location analysis helps identify areas with strong demand, relatively low competition and high business potential. This type of analysis is relevant for charging-point operators, automotive companies, leasing companies, real estate developers, parking operators, municipalities, planners and architects.
Planning a charging-station rollout in a city, parking facility or real estate project? An early spatial assessment can help reveal which areas show strong potential and which datasets are available for deeper analysis.
As the number of electric vehicles grows, the demand for public and private charging stations increases as well. Charging providers face a strategic challenge: how to select sites that will generate usage, reduce risk and support long-term profitability.
Instead of relying on assumptions, generic maps or manual field checks, a spatial decision-support process combines local competition, demand indicators, accessibility and business constraints into a single decision framework.
GIS makes it possible to combine geographic layers such as existing charging stations, roads, parking facilities, shopping centers, train stations, population density, income levels, land prices and environmental constraints. The result is not just a map, but a repeatable location analysis that supports investment and planning decisions.
This approach is aligned with common GIS-based multi-criteria decision-making methods used in EV charging infrastructure planning, where different spatial and business factors are weighted and combined to identify suitable candidate locations
Main stages in EV charging location analysis:
1. Select the key criteria that influence site suitability, such as land cost, rent, accessibility, proximity to transportation hubs, EV-driving patterns and population density.
2. Map existing charging stations to identify geographic gaps, underserved areas and saturated competitive zones.
3. Assign weights to each criterion so that business priorities can be reflected in the final score.
4. Combine the data layers into a dynamic map and spreadsheet that rank potential locations by suitability.
Key benefits of GIS-based EV charging site selection:
• Market-gap detection: identify areas with high potential and limited charging coverage.
• Competitive analysis: understand where competitors are already active and where unmet demand may exist.
• Time and cost savings: reduce manual scouting and focus only on the most promising areas.
• Transparent decision-making: use a consistent scoring model instead of one-off assumptions.
• Flexible planning: adjust weights and scenarios according to city, project type or business strategy
Practical example: identifying EV charging locations in Kfar Saba city
To demonstrate how EV charging location analysis works in practice, we can look at Kfar Saba as a case study based on open data, GIS layers and municipal information.
Kfar Saba city in Israel is a relevant example because it combines a relatively large population, above-average socioeconomic indicators, significant private-car ownership, commuting patterns outside the city and access to transportation, retail and employment centers. Nearby communities and cities such as Ra’anana and Hod Hasharon may also increase the potential user base.
In the first stage, national and municipal spatial layers are collected, including existing charging stations, train stations, parking facilities, shopping centers, gas stations, main roads, population density, socioeconomic data, land prices and green areas.
Data layers and criteria used in the Kfar Saba city analysis
Existing charging stations: location, type, number of stations and local competition.
Public transportation hubs: train stations, central bus stations and planned high-capacity transit.
Large parking facilities: especially near retail, employment and public destinations.
Shopping centers and leisure destinations: places where drivers are likely to stay long enough to charge.
Gas stations: relevant mainly for fast-charging use cases and passing traffic.
Population density and socioeconomic indicators: demand proxies for EV adoption and charging needs.
Land prices: lower cost areas may improve project feasibility.
Main roads and interchanges: access by car and traffic exposure.
Walking distance: proximity to train stations, shopping centers, parking facilities or employment hubs.
Nature reserves and green areas: areas that may be less suitable or restricted for infrastructure development.
Network analysis examines real access through roads and pedestrian paths rather than straight-line distance alone. This makes it possible to evaluate actual driving or walking times to a potential charging station.
For walking-distance analysis, it is useful to calculate access along sidewalks and paths, for example whether a charging location is within a realistic 5–10 minute walk from a train station, mall, parking facility or employment center.
From raw data to a spatial suitability score
The next step is to connect the different data sources into a unified spatial index. Each layer receives a score and a weight, and the city is divided into consistent analysis cells. This makes it possible to compare neighborhoods, statistical areas and H3 cells and identify places where demand, accessibility, lower relative cost and limited competition overlap.
H3 is a hexagonal geospatial grid that divides space into consistent cells. It allows different areas to be compared using the same spatial unit and supports scoring each cell according to population, accessibility, competition and land-cost layers.
The output is a spatial suitability model. Proximity to a train station, parking facility or shopping center may increase a location’s score, while heavy competition, high land costs or poor access may reduce it.
This is a form of Suitability Analysis: each data layer receives a score and a weight, and all candidate areas are ranked according to their suitability for EV charging deployment.
The result is a map and spreadsheet in which each area receives a suitability score, for example on a scale of 1–8. High-scoring areas become strong candidates for further business and planning evaluation, while lower-scoring areas may be deprioritized or tested only under specific scenarios.
What can be learned from the Kfar Saba analysis?
Even without exposing specific candidate sites, the analysis makes it possible to distinguish between high-potential areas and areas that should be examined more carefully. Strong locations are usually those that combine good accessibility, proximity to activity or transportation hubs, relevant demand indicators and relatively limited competition.
Areas with potential demand but many existing charging stations, high land costs or poor accessibility may receive a lower score. This helps companies focus field checks, negotiations and planning resources on the most promising locations.
What the client receives at the end of the process
At the end of the analysis, the client receives practical decision-support outputs: a dynamic map, ranked areas by business potential, a spreadsheet with scores and weights, relevant GIS layers and recommendations for further evaluation of parcels, addresses or specific sites.
The model can also be adjusted by changing the sensitivity or weighting of each criterion. For example, increasing the weight of land cost or parking proximity can produce a different ranking of candidate sites, either in the same city, another city or another country.
GIS and spatial analysis are not just a way to create attractive maps. They provide a practical decision-making framework for identifying where charging infrastructure is most likely to succeed.
If this topic is relevant to a project you are evaluating, a first step can be a preliminary review of a city, area or site to understand which datasets are available before making planning or business decisions.
