There’s no denying the benefits of spending time in the great outdoors. Parks and other recreational spots are often beloved fixtures in every city. Now, imagine if the most beloved parks were data-driven? How would like to impact the systems of park planning and management?
An important aspect of urban life that receives little attention in the discussion over smart cities – is vital to people’s well-being. Urban parks provide essential green spaces for recreation and social interaction while raising the economic worth of a region and promoting the overall ‘brand’ of the city. Given this, it’s only natural to consider how big data may be utilized to leverage these social, economic, and environmental advantages.
Today urban planners are focused more on developing efficient methods towards public park management. Hence, Big Data is rapidly being used by urban planners, policymakers, designers, and engineers to improve the quality of life for city people. Big Data can be used to make informed decisions that enhance the way our cities run, from tracking cars to generate more efficient traffic flow to mapping trees and placing sensors to estimate their influence on air quality.
Using Location Data for More Efficient Park Management
Location data can contribute to a more efficient data collection methodology for park managers. Location data refers to a massive collection of data gathered from various sources, such as mobile GPS, cell phone data, connected vehicles, etc. This massive amount of data is then being cleansed, normalized, preprocessed, extrapolated, and analyzed to identify human movement behavior and interaction patterns, trends, and correlations.
One of the most recent trends in data-driven park planning is utilizing location data from mobile devices. In particular, using such data for park visitor studies can be more cost-effective and reliable than doing in-person counts and/or questionnaires at parks.
Location data from smartphones can reveal how people travel around parks and which areas they utilize. Through location insights, we can understand why people move on specific patterns. For example, people are more likely to visit parks if they are within a short walking distance. The information may be used to concentrate resources on assisting and validating current park and community users.
Understanding Public Spaces Through Location Data
Park and public space planners have traditionally depended on observational methods to learn how people use their facilities, such as counting visits and conducting questionnaires. New technologies and solutions by AirSage, may now give managers data sources that are quicker, cheaper, and more precise.
Across North America, AirSage collects millions of anonymous location signals from mobile phones. Coming in the form of GPS signals from smartphone applications – often from mobility apps, dating apps, news apps, and other sorts of apps that transfer GPS data a location signal - this data can be extrapolated to represent a total population in a particular area.
AirSage supplements GPS data with other data sources like vehicle tracking and Census data, or any other custom data sets like transportation points, grocery stores, vaccination sites, and so on, before analyzing and processing it to estimate population movements. AirSage’s findings provide a better understanding of how individuals move across space and time.
Revitalizing Unused Public Spaces
Underuse of public space can lead to perceived or actual safety concerns, resulting in a cycle of neglect. Management may use data analysis to identify underutilized locations and execute initiatives to revitalize them, ranging from basic improvements like better lighting to more ambitious plans to boost involvement with these areas.
Different forms of lighting are being put in public places surrounding 40 housing projects in New York City as part of a pilot initiative that will allow data to be collected on the influence of different lighting schemes on public safety.
For instance, AirSage insights can even tell where drones should be allowed to fly based on population density patterns. All travel patterns and visitations typically vary from season to season. The information about how it is different can help optimize budgets and focus on what really matters.
For example, if the visitation of park A during the summer is 70% more than during the winter season, it makes sense to reduce expenses on the park services during the winter. Or knowing how many people drive on certain roads helps prioritize the streets to be cleaned from the snow.
Analyzing ROI of City Infrastructure
Location data analysis may identify space’s demands and create development plans from the very beginning of the planning process. Furthermore, it can also help determine the size and form of any public space and how people interact with it or how people use the public space.
AirSage movement insights give an understanding of what is happening in terms of mobility and actual behavior. However, it is not the only knowledge that can be derived from them. It is possible to go deeper down from the data perspective and observe people’s interaction and movement around a particular point of interest (POI).
AirSage allows park management to go back in time and forecast how specific infrastructures will interact in the future. It describes how people move through stadiums, event sites, marketplaces, hospitals, and even streets during “regular” times and major events or holidays. It contributes to the expansion of new assumptions about the use of new infrastructure and proves or refutes the effectiveness of the current infrastructure.
Urban planners may use AirSage to observe what means of transportation they use to get to a certain location and how they move around the city before and after it. Examine the population density in and around certain places. See if people really use the new multilevel parking infrastructure or if they try to avoid it by looking for regular parking places in the area.
AirSage's insights can provide historical context back to 2017 for these activities, allowing us to evaluate these trends and illustrate how newly enacted infrastructure and policy changes impacted current situations.
Case Study: Central Park, NY by ALADIN
Location analysis has never been more accessible than before. ALADIN, AirSage Local Activity Density INterface, is the industry’s first free mobile application for location intelligence. The simple-to-use interface allows industry professionals to run complex location analytics studies on population movement in just a few clicks.
Here is an example of ALADIN’s custom study of Central Park in New York, from 07.12.21 to 07.18.21. We have analyzed the Central Parks peak dates and timings through the heat map illustration. The visualized heatmap demonstrates that peak hours are common in the morning and afternoon for all seven days. The highest activity density during the week happened on Wednesday, 07.16.21, between 18:00 and 24:00.
Furthermore, we can determine specific locations of the highly concentered area during peak hours. This case study illustrates that the Metropolitan Museum Art has the highest people density, following with smaller areas in The Sheep Meadow and Central Park Zoo.
AirSage ALADIN data primarily contribute to various industries since activity density presents direct insight into the living population density heat map, which would otherwise be difficult to capture. You can see the full video at this link.
Location insights can allow park and public area managers to analyze their development efforts and provide the knowledge they need to better understand and optimize the utilization of public spaces. Data-mining techniques are used in so-called “sentiment analysis” to determine how individuals feel when they are in a certain area.
AirSage has made consumer data privacy a major concern for more than two decades. This aim is accomplished by implementing a three-stage plan for securing and safeguarding the data we use. Our source data partners are held to the highest of standards. AirSage uses data only from highly trusted partners that provide data conforming with the most stringent privacy rules in the United States and worldwide.
Many of our major cities are now congested and inefficient in terms of transportation, public services, and other factors. They were not designed for the magnitude and size that we have now. Thus, public authorities, urban and transportation planners, and engineers should work with intelligent data rather than just assumptions to enhance American cities' infrastructure, plan projects and budgets.
Understanding how people really move, what they do, and when they do it allows for more accurate and better decisions to be made on the road to enhanced municipal viability.