In recent years, the rapid adoption of electric vehicles (EVs) has underscored the critical need for efficient and accessible EV charging infrastructure. As a researcher in urban planning and sustainable development, I have focused on leveraging Point of Interest (POI) data to analyze the spatial distribution characteristics of EV charging stations. POI data, which includes geographically referenced information on various facilities, provides a rich dataset for understanding patterns in urban infrastructure. This study employs spatial analysis techniques such as the geographic concentration index, kernel density estimation, and nearest neighbor index to explore how EV charging stations are distributed across a study region. By integrating data from popular mapping services like Baidu Maps and Amap, I aim to uncover insights that can inform policy decisions and infrastructure development. The proliferation of EV charging stations is not only a marker of technological advancement but also an indicator of regional economic vitality and environmental commitment. Understanding the spatial dynamics of these stations helps in assessing the readiness of cities to support low-carbon transportation systems. Moreover, the clustering of EV charging stations often correlates with the presence of essential services, such as shopping centers, parks, and hotels, highlighting the interplay between infrastructure and urban livability. Through this research, I seek to contribute to the broader discourse on sustainable urban mobility by providing a data-driven analysis of EV charging station distribution.
The study region is a representative area in southern China, characterized by diverse topography ranging from coastal plains to mountainous terrain. This region has a total area of approximately 240,000 square kilometers, with a population density that varies significantly from urban centers to rural outskirts. The economic landscape includes both developed metropolitan areas and emerging industrial zones, making it an ideal case for examining the distribution of EV charging stations. Data for this analysis were sourced from publicly available APIs of Baidu Maps and Amap, which provide comprehensive POI datasets. These datasets include information on EV charging stations, as well as other relevant points like parks, shopping malls, and star-rated hotels. Additional data on population density and gross domestic product (GDP) were obtained from statistical yearbooks and environmental science databases. To ensure accuracy, the raw POI data underwent preprocessing steps, including removal of duplicates and coordinate transformation to the WGS 1984 system. This allowed for seamless integration into geographic information system (GIS) software for spatial analysis. The use of these diverse datasets enables a holistic view of the factors influencing the placement of EV charging stations, bridging gaps between urban planning and environmental sustainability.
To analyze the spatial distribution of EV charging stations, I employed several quantitative methods. The nearest neighbor index (NNI) was used to determine whether the EV charging stations are clustered, dispersed, or randomly distributed. The NNI is calculated as the ratio of the observed mean distance between each EV charging station and its nearest neighbor to the expected mean distance in a random distribution. The formula is given by:
$$ R = \frac{D_O}{D_E} $$
where \( R \) is the nearest neighbor index, \( D_O \) is the observed mean distance between EV charging stations, and \( D_E \) is the expected mean distance under random distribution. Specifically, \( D_O \) is computed as:
$$ D_O = \frac{\sum_{i=1}^{n} d_i}{n} $$
and \( D_E \) is derived from:
$$ D_E = \frac{0.5}{\sqrt{n/A}} $$
In these equations, \( d_i \) represents the distance between a given EV charging station and its nearest neighbor, \( n \) is the total number of EV charging stations, and \( A \) is the total area of the study region. A value of \( R < 1 \) indicates clustering of EV charging stations, \( R = 1 \) suggests randomness, and \( R > 1 \) implies dispersion. This index provides a foundational understanding of the overall spatial pattern of EV charging infrastructure.
Kernel density estimation (KDE) was another key method used to visualize the concentration of EV charging stations across the study area. KDE calculates the density of EV charging stations in a smooth, continuous surface, highlighting areas of high and low concentration. The kernel density function is expressed as:
$$ f(x) = \frac{1}{nh} \sum_{i=1}^{n} K \left( \frac{x – x_i}{h} \right) $$
where \( K \) is the kernel function, \( h \) is the bandwidth determining the smoothness of the output, \( n \) is the number of EV charging stations, and \( x – x_i \) is the distance between points. By applying KDE, I generated density maps that reveal hotspots of EV charging station activity, facilitating a deeper analysis of regional disparities.
To explore the factors influencing the spatial distribution of EV charging stations, I conducted correlation analysis using Pearson’s correlation coefficient. This statistical measure assesses the linear relationship between the density of EV charging stations and various independent variables, such as the number of parks, shopping malls, star-rated hotels, population density, and GDP. The Pearson correlation coefficient \( r \) is calculated as:
$$ r = \frac{\sum_{i=1}^{n} (x_i – \bar{x})(y_i – \bar{y})}{\sqrt{\sum_{i=1}^{n} (x_i – \bar{x})^2 \sum_{i=1}^{n} (y_i – \bar{y})^2}} $$
where \( x_i \) and \( y_i \) are the values of the two variables for each observation, and \( \bar{x} \) and \( \bar{y} \) are their respective means. A positive \( r \) value indicates a positive correlation, meaning that as one variable increases, the other tends to increase as well. This analysis helps identify which factors are most strongly associated with the presence of EV charging stations, providing insights for targeted infrastructure development.
The spatial distribution analysis of EV charging stations revealed a distinct clustering pattern. The nearest neighbor index yielded a value of \( R = 0.35 \), which is significantly less than 1, indicating that EV charging stations are highly clustered rather than randomly or uniformly distributed. This clustering suggests that EV charging infrastructure is concentrated in specific areas, likely driven by urban demand and economic factors. The associated z-score and p-value confirmed the statistical significance of this pattern, with a confidence level exceeding 99%. Such aggregation of EV charging stations can be attributed to the uneven distribution of population and economic activities, as well as the strategic placement of charging facilities in high-traffic zones. This finding underscores the importance of considering spatial equity in the deployment of EV charging stations to ensure broader accessibility.
Kernel density analysis further elaborated on the spatial characteristics of EV charging stations. The resulting density map showed pronounced peaks in urban centers and coastal areas, with a gradual decrease in density toward peripheral and mountainous regions. For instance, the highest densities of EV charging stations were observed in central metropolitan areas and along the coastline, where economic development and tourism are prominent. In contrast, remote and topographically challenging areas exhibited sparse distributions of EV charging stations. This pattern highlights regional imbalances in EV charging infrastructure, which could hinder the adoption of electric vehicles in underserved regions. The density gradients often formed concentric rings or linear belts, reflecting the influence of transportation networks and urban sprawl on the placement of EV charging stations. By visualizing these patterns, the kernel density analysis provides a clear picture of where EV charging stations are most needed and where investment gaps exist.
To quantify the relationships between EV charging station distribution and various influencing factors, I performed a correlation analysis. The results are summarized in the table below, which displays the Pearson correlation coefficients between the density of EV charging stations and key variables. This analysis included data from multiple POI categories, as well as demographic and economic indicators.
| Variable | Correlation Coefficient with EV Charging Stations |
|---|---|
| Number of Parks | 0.62 |
| Number of Shopping Malls | 0.64 |
| Number of Star-Rated Hotels | 0.61 |
| Population Density | 0.68 |
| GDP per Capita | 0.65 |
As shown in the table, all variables exhibit positive correlations with the density of EV charging stations, with coefficients ranging from 0.61 to 0.68. This indicates that areas with more parks, shopping malls, hotels, higher population density, and greater economic output tend to have a higher concentration of EV charging stations. For example, the correlation coefficient of 0.64 for shopping malls suggests that commercial hubs are key drivers for the deployment of EV charging infrastructure, likely due to the high foot traffic and longer dwell times. Similarly, the strong correlation with population density (0.68) underscores the role of urban density in supporting EV adoption. These findings align with the kernel density results, reinforcing the idea that EV charging stations are strategically located in areas with robust service facilities and economic activity.
Topography also played a significant role in the distribution of EV charging stations. The study region’s terrain includes flat plains, rolling hills, and steep mountains, with elevation data derived from digital elevation models (DEM). I observed that EV charging stations were predominantly located in low-lying areas, such as coastal plains and river valleys, where infrastructure development is more feasible. In contrast, mountainous regions with higher elevations had fewer EV charging stations, indicating a negative correlation between elevation and station density. This pattern can be explained by the higher costs and logistical challenges associated with installing and maintaining EV charging stations in rugged terrain. Moreover, lower elevation areas often coincide with urban centers and transportation corridors, further encouraging the clustering of EV charging stations. This topographic influence highlights the need for adaptive strategies in hilly or remote regions to ensure equitable access to EV charging infrastructure.
Economic factors, particularly GDP, were closely linked to the spatial distribution of EV charging stations. Regions with higher GDP per capita exhibited greater densities of EV charging stations, as evidenced by the correlation coefficient of 0.65. This relationship suggests that economic prosperity enables higher investment in EV charging infrastructure, both from public and private sectors. For instance, affluent urban areas with strong industrial bases and tourism revenues tend to prioritize the development of EV charging stations as part of broader sustainability initiatives. Additionally, the presence of star-rated hotels and shopping malls—often indicators of economic vitality—further amplifies this effect. The interplay between economic performance and EV charging station distribution underscores the importance of integrating energy infrastructure planning with regional economic development policies. By fostering economic growth, communities can accelerate the deployment of EV charging stations and support the transition to electric mobility.
Population density emerged as one of the strongest predictors of EV charging station distribution, with a correlation coefficient of 0.68. Densely populated areas, such as city centers and suburban hubs, require a higher number of EV charging stations to meet the demand from residents and commuters. The concentration of EV charging stations in these areas facilitates convenient access for daily users, reducing range anxiety and promoting EV adoption. Furthermore, high population density often correlates with improved public services and infrastructure, creating a virtuous cycle that supports the expansion of EV charging networks. However, this also raises concerns about spatial equity, as rural and sparsely populated regions may be left behind. To address this, policymakers could consider incentives for deploying EV charging stations in underserved areas, ensuring that the benefits of electric mobility are widely shared.
The integration of POI data with spatial analysis techniques has proven highly effective in elucidating the distribution patterns of EV charging stations. By combining data from multiple sources, I was able to capture the multifaceted nature of urban infrastructure and its impact on EV charging station placement. For example, the proximity of EV charging stations to parks and recreational areas suggests that users often charge their vehicles while engaging in leisure activities, highlighting the potential for synergies between green spaces and sustainable transportation. Similarly, the association with shopping malls and hotels indicates that EV charging stations are increasingly being integrated into commercial and hospitality sectors, enhancing customer convenience and driving economic activity. These insights can guide urban planners and stakeholders in optimizing the location of new EV charging stations, maximizing their utility and accessibility.
In summary, this study demonstrates that EV charging stations in the study region are characterized by a clustered spatial distribution, with significant regional imbalances influenced by topography, service facilities, population density, and economic conditions. The nearest neighbor index and kernel density analysis provided robust evidence of aggregation in urban and coastal areas, while correlation analysis revealed positive associations with key socio-economic factors. These findings emphasize the need for a balanced approach to EV charging infrastructure development, one that addresses both efficiency and equity. Future research could build on this work by incorporating additional variables, such as income levels, traffic patterns, and policy incentives, to develop more comprehensive models. Moreover, advanced spatial econometric techniques could be employed to account for spatial dependencies and interactions. As the adoption of electric vehicles continues to grow, understanding the spatial dynamics of EV charging stations will be crucial for building resilient and sustainable urban environments. By leveraging POI data and spatial analysis, we can pave the way for a future where EV charging stations are accessible to all, supporting the global shift toward low-carbon transportation.