**1. Introduction**

Gasoline has been the primary fuel for transportation in the United States over the past 100 years [1]. In 2021, petroleum products accounted for about 90% of the total energy use in the U.S. transportation sector. Long-term use of fossil fuels deteriorated urban air quality, with road transportation being responsible for 70% of pollutants and 40% of greenhouse gas emissions in urban areas [2]. In American, more than half of Americans (166 million) live in counties with unhealthy air quality conditions [3]. Alternative forms of energy such as solar, wind, hydrogen fuel cells, and electricity have been considered as potential energy sources. Electric vehicles (EVs) are powered by electricity which enables them not to introduce harmful pollutants into our atmosphere like gasoline vehicles. EVs convert approximately 59–62% of the electrical energy at the wheels, whereas conventional vehicles only convert about 17–21% of gasoline energy. EVs emit no tailpipe pollutants [4] and have gained support as a strong alternative candidate for future fuel transportation due to the fact of not introducing harmful pollutants to the atmosphere. The U.S. federal government has started some incentive programs to encourage the purchase and use of EVs [5, 6]. Some relevant policies and several incentive programs have been released to ease dependence on gasoline consumption, including purchasing tax credits and installing EV charging stations. These incentives have been adopted by state and local governments. EV charging stations are typically installed by various entities, including governments, companies, and other organizations, to demonstrate their commitment to promoting cleaner transportation options. This support for EV infrastructure is essential to encourage the widespread adoption of electric vehicles and reduce reliance on gasoline-powered transportation.

Although this promising transportation option is available in some places, most of the programs for EV charging stations lack a comprehensive analysis of the locations and infrastructure is not yet in great supply. Many programs install charging stations in urban areas at popular places such as city centers, shopping areas, train stations, and university campuses. More scientific research is needed to better understand where EV charging stations should be located, and provide sound solutions for the establishment of a robust EV charging station infrastructure. In Greater Chicago Area, an agent-based decision support system for electric vehicle charging infrastructure deployment was investigated for the four surrounding counties [7]. The research identified patterns in residential EV ownership and driving activities to facilitate the strategic deployment of new charging infrastructure. An equilibrium-modeling framework was developed to explore interactions between the availability of public charging opportunities, prices of electricity, and destination and route choices of EVs at regional transportation and power transmission networks [8].

The transportation industry has benefited greatly from the use of GIS to help solve complex transportation-related issues and plan the infrastructure of EVs [9–11]. GIS provides a variety of geospatial analysis tools that allow transportation practitioners to create spatial models that can provide answers to challenging transportation questions. It has been used to identify new transportation corridors, determine the socioeconomic and environmental impacts of future transportation facilities, track the construction progress of transportation projects, and many others. GIS analysis also has been used to identify prime locations for EV charging stations. GIS has been used to analyze grid impact of EVs and origin-destination to model spatial and temporal characteristics of EV charging loads [12]. Grid partition has been used in minimizing the distance to the charging station, zoning the planning area, and selecting the best locations for each partition with the considerations of traffic density and charging station capacity constraints [13]. A GIS multi-criteria analysis method was developed to map optimal locations for EV charging stations in Athens, Greek [14]. The method uses a number of different weighted parameters such as population, points of interest, income, and parking distance to map optimal locations. A GIS site suitability model was also used to locate EV charging stations on public facilities in Los Angeles County, CA [15]. Chen et al. [16] have developed a regional methodology to locate EV charging stations through the use of a regression equation that can predict parking demand in urban areas.

These research methods aim to solve the same complex problem of identifying optimal geographic locations for new EV charging stations. Each uses a defined set of weighted demand factors within a spatial model to determine prime locations and proves to be a success in determining locations for EV charging stations. However, these studies used a limited amount of input demand factors to identify optimal locations. The limited use of input demand factors targets specific facilities to install EV charging station areas. The network provides charging stations to a very selective portion of the driving population. These previous studies have only considered a limited number of demand factors. At the same time, the walking distance between an EV charging station and the desired destination tended to be overlooked. Many studies just used an assumption factor for this variable [16]. Identifying optimal locations for EV charging stations is a complex process that involves various factors. These limited approaches may not accurately capture the full range of variables that influence

the adoption and usage of electric vehicles. Therefore, it is crucial to conduct more comprehensive analyses that consider a broader range of demand factors to identify the most suitable locations for EV charging stations.

The purpose of this research was to conduct a comprehensive analysis of the various factors that influence the installation of EV charging stations. To achieve this, we developed a multi-factor geospatial method that evaluates a range of positive and negative impacts on potential locations. By considering these factors, our method aims to identify the most suitable locations for EV charging stations to provide widespread availability for the driving public.
