By Weplan Analytics

Continued investment in network infrastructure is crucial to bridging the Digital Divide and ensuring seamless connectivity for all users regardless of their geographic location. Leveraging data from crowdsourcing in five key parameters, Weplan Analytics delved into the Digital Divide between urban and rural environments by examining the correlation between population density and connectivity.

Weplan Analytics, a revolutionary force in the industry, gathers data on network performance from billions of users worldwide through its pioneering crowdsourcing methodology. This approach involves collecting vast amounts of information on network performance directly from users’ devices.

Originated in Spain, Weplan Analytics has rapidly expanded its global reach, including a growing client base in the United States comprising top-tier players in the industry. Weplan Analytics recently become a member of WIA to reach American mobile network operators (MNOs) and TowerCos. Leveraging a massive consumer smartphone sample, Weplan Analytics collects over 200 million measurements daily within the United States alone.

Weplan’s proprietary Dashboard, tailored to the specific needs of TowerCos and MNOs, provides invaluable insights for network optimization. Several leading MNOs worldwide rely on Weplan Analytics’ tool for their network optimization needs, differentiating the company from other industry providers. Both the measurement quality and quantity matter. The extensive number of measurements offered by Weplan Analytics ensures high accuracy, while the Dashboard enables effective visualization and informed decision-making.

Collaboration is a cornerstone to our success. Weplan Analytics prides itself on fostering long-term partnerships with its clients. Furthermore, we engage in significant research and development (R&D) projects in collaboration with telecom universities, supported by various public institutions in Europe. Weplan’s innovative commitment ensures that clients have access to the most advanced and cutting-edge technologies and methodologies available in the industry.

In this blog post, Weplan Analytics delves into the digital divide in the U.S. by examining the correlation between population density and connectivity. The study focuses on population distribution within distinct geographical areas, using a classification system based on pixels of 9 square miles to distinguish between rural and urban categorizations. We are focusing on data from September 2023 to March 2024.

A visual representation of this segmentation, with pixels designated as “Urban” highlighted in red, while those categorized as “Rural” occupy yellow spaces. Those areas without classification, represented by blank spaces on the map, signify pixels where population data is absent, which means no habitation in those areas (Source: GHS population grid (R2023)).

To define network quality, we will examine the KPIs that most significantly impact the consumer’s experience. This will provide a global perspective of connectivity across different regions of the country.

For in-depth growth opportunities for TowerCos analysis in the US click here.

Parameter 1: 5G time

This KPI, 5G time, considers mobile connections via both 5G SA (Standalone) and 5G NSA (Non Standalone) networks, where devices are connected either directly to a Standalone 5G network or to a secondary 5G cell used for data transmission.

The results reveal that the total percentage of time spent in a 5G network, segmented by environment type, is 11.4% in rural areas and 29.4% in urban zones. Therefore, it can be deduced that urban areas have almost three times more 5G usage than rural areas, where only 11% of the population has access to a 5G connection.

In an ideal scenario, without user or device restrictions, the 5G network would be the preferred choice due to its superior speed and capacity compared to previous technologies like 4G. However, in cases of insufficient or nonexistent coverage, devices would automatically switch to 4G or another available network. Therefore, a higher percentage indicates broader coverage of the 5G network.

Parameter 2: Signal Power (RSRP)

Reference Signal Received Power (RSRP), refers to the linear power in Watts emitted by the reference signal, measured in dBm. Values typically range between -130 and -45 dBm, where higher values indicate better signal strength. For this analysis, both 4G and 5G NSA networks are considered.

The average RSRP recorded for rural areas is -102.7 dBm, while for urban areas, it stands at -96.4 dBm. However, the median RSRP values for rural and urban environments are -104 dBm and -97 dBm, respectively, therefore it is concluded that urban signal strength (RSRP) is acceptable while rural areas are close to poor.

A higher value in dBm indicates a stronger signal, whereas a lower value suggests a weaker signal. Both on average and in terms of median values, the received signal strength in urban environments surpasses that in rural ones. This difference is attributed to a higher density of cellular towers in urban areas

Paramater 3: Signal Quality (RSRQ)

The RSRQ (Reference Signal Received Quality) KPI refers to the ratio between the received reference signal power and the amount of interference in the radio spectrum, measured in dB.

The average RSRQ values recorded for rural and urban areas are -11.1 dB and –11.7 dB, respectively, with median values of -11 dB for rural environments and -12 dB for urban environments. Hence, signal quality (RSRQ) is almost the same in both environments.

A higher RSRQ value indicates better signal quality. In this case, we observe that the urban environment has a slightly lower RSRQ compared to the rural settings. This discrepancy may stem from various factors, including network congestion and signal interference.

Parameter 4: Latency

Latency refers to the delay in network communication, measured in milliseconds. Only mobile connections are taken into account, including the following network types: 2G, 3G, 4G, 5G NSA and 5G SA.

The average latency recorded for rural areas is 71.1 milliseconds, while for urban areas, it is 58.8 ms. The median latency values for rural and urban environments are 62 milliseconds and 51.2 ms, respectively. This means that urban areas exhibit lower delayed response time values (with a latency average of 58.7 ms) compared to rural areas, which exceed times by 20%.

A lower latency indicates a faster connection. Both in terms of average and median values, connections in urban environments experience less delay compared to connections in rural areas.

Parameter 5: Packet Loss

Packet loss percentage reflects the rate of lost data packets within the network.

Categorized by environment type, the values indicate a packet loss rate of 1.2% in rural areas and 0.6% in urban areas. The packet loss rates reveal significantly poorer outcomes in rural environments, reaching half the packet loss values observed in urban areas.

A lower packet loss percentage signifies a more reliable and stable connection, whereas a higher packet loss rate may suggest potential congestion, interference issues or a less capable network. In this case, it appears that urban environments exhibit lower packet loss compared to rural areas, indicating more robust network infrastructure. This discrepancy could be attributed to the prevalent technology in each environment, as higher-tech coverage areas tend to show reduced packet loss. This phenomenon is linked to the greater capacity and efficiency of advanced networks, resulting in more reliable data transmission.

Conclusions

This analysis unveils a significant disparity in 5G access between rural and urban areas, with access being severely limited in rural regions compared to urban ones (almost three times higher). Furthermore, network reliability, as indicated by Packet Loss, is notably superior in urban areas compared to rural ones (by half), while network communication delay (referred to as Latency) is lower in urban areas than in rural ones (close to 20% better than rural).

Regarding signal quality and strength (RSRQ and RSRP), similar values are found in both environments, despite slight differences favoring rural areas. These variations may be caused by the low population density in rural areas, which results in less congestion and therefore better signal quality. However, the signal is worse due to lower density of tower deployments compared to urban areas.

The following table summarizes all averages extracted for each parameter according to categorization by population density. Besides, the difference between each environment is approximated in terms of the values extracted from the analysis.

Parameter	Rural zones average	Urban zones average	Urban/rural differential value
5G Time (%)	11.46	29.41	Urban is almost three times higher than rural.
Packet loss (%)	1.24	0.65	Urban is half of rural.
RSRQ (dB)	-11.09 (Median: -11)	-11.7 (Median: -12)	RSRQ is almost the same.
RSRP (dBm)	-102.69 (Median: -104)	-96.4 (Median: -97)	Urban signal strength is acceptable. Rural areas are close to poor.
Latency (ms)	71.08 (Median: -61.97)	58.77 (Median: -51.26)	Urban latency is close to 20% better than rural.

In addition, 5G percentage time visualization has been extracted through Weplan Analytics Dashboard.

A correlation analysis was conducted between population density and the percentage of 5G technology usage time at the pixel level. The results revealed a strong correlation, evidenced by a correlation coefficient of 0.8.

In conclusion, our analysis of various KPIs provides valuable insights into mobile network performance across all US environments. Urban areas generally show superior network speed, reliability and stability compared to rural ones, thanks to more consolidated infrastructure and advanced technologies.

These findings underline the importance of continued investment in network infrastructure to bridge the digital divide and ensure seamless connectivity for all users, regardless of their geographical location.

If you liked this article, you can access our website here to see how we contribute to the telecom sector thanks to crowdsourcing.

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