Abstract

The site selection of billboard locations has become crucial in promoting corporate image and expanding brand influence. Therefore, when choosing billboard placement locations, factors such as target audience and surrounding traffic conditions must be considered to fully leverage the value of billboard advertising. In this article, various analytical methods, and tools such as kernel density analysis, standard ellipse analysis, and geodetector are used to analyze the relationship between factors such as building density, road density, population density, and the number of shared bicycle rentals and returns per kilometer grid with the spatial distribution of advertising billboards in Wuhan City. Experimental results indicate that advertising billboards in Wuhan City exhibit spatial clustering, and the density of bus routes and stops, as well as the number of shared bicycle rentals and returns per kilometer grid, have a significant impact on their distribution.

Background

In today's market environment, the promotion of corporate image and the expansion of brand influence is an important task that every enterprise must face. To stand out in the competitive market, in addition to continuous innovation and optimization in advertising content, the choice of advertising location also becomes crucial. Therefore, reasonable billboard location selection has become an indispensable part of corporate image promotion strategy. When choosing the location of billboards, it is necessary to fully consider the brand image, audience groups, the surrounding advertising environment, as well as traffic conditions visibility, and other factors.

The optimization of billboard placement is greatly facilitated by an understanding of the current spatial distribution of existing billboards. This understanding can provide valuable insights into the factors that should be considered in the site selection process. Traditional methods relying on subjective memory and experience are prone to distortion over time, making them less reliable. However, obtaining specific and comprehensive analytical results can be time-consuming and resource-intensive. The development of spatial analysis methods has effectively addressed this issue. In this study, we employ spatial analysis methods such as standard deviation ellipses and geographic detectors to investigate the spatial distribution of billboards in Wuhan, as well as the factors influencing their distribution. This research aims to provide relevant insights for optimizing billboard placement.

Results

(1) Spatial Analysis

It is evident that billboard locations are notably concentrated within the central urban regions of Wuhan City. This concentration includes districts such as Jianghan District, Hanyang District, and Wuchang District. This observation aligns seamlessly with the primary areas of urban development within Wuhan City, specifically the convergence point of the Hanjiang and Yangtze Rivers. These regions are known for their robust economic development and consequently exhibit a higher demand for billboards.

Figure 1: Spatial Distribution of Billboards in Wuhan City

(2) Standard Deviational Ellipse

After conversion, it is determined that an error ellipse, centered at approximately 30°34'40.74"N, 114°18'21.81"E, can effectively encompass around 98% of the billboard locations within Wuhan City. Moreover, it becomes apparent that the clustered regions of billboards predominantly follow a northwest to southeast distribution pattern, covering a substantial portion of the central urban development zone in Wuhan City.

Figure 2: Directional Distribution Analysis (Standard Error Ellipses)

Table 1. Error Ellipse Parameters

(3) Average Nearest Neighbor

The spatial pattern among the points is determined by comparing the average distance of the nearest point pairs. In the analysis report, showing a p-value less than 0.05. This finding suggests that billboard locations are not randomly distributed, as also highlighted in the report. Notably, the Z-score in the report is a very small negative value, reinforcing the conclusion that the distribution of billboards in Wuhan City exhibits significant clustering and is not a result of random data generation.

Figure 3: The report on the average nearest neighbor results

(4) Hot Spot Analysis and Kernel Density Analysis

The hot spot analysis results show that the distribution of billboards in Wuhan presents an obvious hot spot aggregation pattern. In contrast, the cold spots are distributed in a ring pattern, mainly in the farther outer regions. The results of the kernel density analysis show a unique multi-modal pattern, indicating polarization.

Figure 4: Hotspot analysis

Figure 5: Hotspot analysis

(5) Geodetector

Bus Route Density has the highest explanatory power, indicating that the spatial distribution of billboards in Wuhan City is most strongly influenced by the distribution of bus route density, meaning that there is a strong consistency between the bus route density and the spatial distribution of billboards in Wuhan City. Secondary factors are the Number of Shared Bicycle Returns and the Number of Shared Bicycle Rentals per Kilometer Grid, indicating that shared bicycles are also important factors affecting the spatial distribution of billboards in Wuhan City. The results of the Interaction Detector are shown in the figure, indicating that the spatial differentiation pattern of billboards in Wuhan City is not controlled by a single factor but is the result of the combined action of multiple factors. Among these interactions, the interactions between Bus Route Density and Charging Station Density, are relatively large. The interactions between Road Density and Population Density and are relatively small.

Table 2. Factor Detector Results

Figure 6: Interaction Detector Results

Conclusion

1.By utilizing various spatial analysis methods, we explore the influence of different factors on the spatial distribution of billboards.

2.From the aforementioned analysis, it is evident that the optimal placement of billboards should be closely associated with public transportation infrastructure and other relevant structures.

3.The methods and tools employed in this paper can serve as a reference for spatial distribution analysis in other cities.

4.This study has certain limitations, such as the failure to consider the impact of competition between different types of billboards on their placement. In the future, we will continue to improve and expand spatial analysis methods, focusing on delving deeper into the spatial distribution patterns of billboards in Wuhan.

5.This will provide more valuable insights for optimizing the placement of billboards.