Browsing by Author "Swarup, Samarth"

Now showing 1 - 16 of 16
  • Thumbnail Image
    Adverse Health Outcomes Following Hurricane Harvey: A Comparison of Remotely-Sensed and Self-Reported Flood Exposure Estimates
    Ramesh, Balaji; Callender, Rashida; Zaitchik, Benjamin F.; Jagger, Meredith; Swarup, Samarth; Gohlke, Julia M. (American Geophysical Union, 2023-04)
    Remotely sensed inundation may help to rapidly identify areas in need of aid during and following floods. Here we evaluate the utility of daily remotely sensed flood inundation measures and estimate their congruence with self-reported home flooding and health outcomes collected via the Texas Flood Registry (TFR) following Hurricane Harvey. Daily flood inundation for 14 days following the landfall of Hurricane Harvey was acquired from FloodScan. Flood exposure, including number of days flooded and flood depth was assigned to geocoded home addresses of TFR respondents (N = 18,920 from 47 counties). Discordance between remotely-sensed flooding and self-reported home flooding was measured. Modified Poisson regression models were implemented to estimate risk ratios (RRs) for adverse health outcomes following flood exposure, controlling for potential individual level confounders. Respondents whose home was in a flooded area based on remotely-sensed data were more likely to report injury (RR = 1.5, 95% CI: 1.27-1.77), concentration problems (1.36, 95% CI: 1.25-1.49), skin rash (1.31, 95% CI: 1.15-1.48), illness (1.29, 95% CI: 1.17-1.43), headaches (1.09, 95% CI: 1.03-1.16), and runny nose (1.07, 95% CI: 1.03-1.11) compared to respondents whose home was not flooded. Effect sizes were larger when exposure was estimated using respondent-reported home flooding. Near-real time remote sensing-based flood products may help to prioritize areas in need of assistance when on the ground measures are not accessible.
  • Thumbnail Image
    Behavior Modeling and Analytics for Urban Computing: A Synthetic Information-based Approach
    Parikh, Nidhi Kiranbhai (Virginia Tech, 2017-03-15)
    The rapid increase in urbanization poses challenges in diverse areas such as energy, transportation, pandemic planning, and disaster response. Planning for urbanization is a big challenge because cities are complex systems consisting of human populations, infrastructures, and interactions and interdependence among them. This dissertation focuses on a synthetic information-based approach for modeling human activities and behaviors for two urban science applications, epidemiology and disaster planning, and with associated analytics. Synthetic information is a data-driven approach to create a detailed, high fidelity representation of human populations, infrastructural systems and their behavioral and interaction aspects. It is used in developing large-scale simulations to model what-if scenarios and for policy making. Big cities have a large number of visitors visiting them every day. They often visit crowded areas in the city and come into contact with each other and the area residents. However, most epidemiological studies have ignored their role in spreading epidemics. We extend the synthetic population model of the Washington DC metro area to include transient populations, consisting of tourists and business travelers, along with their demographics and activities, by combining data from multiple sources. We evaluate the effect of including this population in epidemic forecasts, and the potential benefits of multiple interventions that target transients. In the next study, we model human behavior in the aftermath of the detonation of an improvised nuclear device in Washington DC. Previous studies of this scenario have mostly focused on modeling physical impact and simple behaviors like sheltering and evacuation. However, these models have focused on optimal behavior, not naturalistic behavior. In other words, prior work is focused on whether it is better to shelter-in-place or evacuate, but has not been informed by the literature on what people actually do in the aftermath of disasters. Natural human behaviors in disasters, such as looking for family members or seeking healthcare, are supported by infrastructures such as cell-phone communication and transportation systems. We model a range of behaviors such as looking for family members, evacuation, sheltering, healthcare-seeking, worry, and search and rescue and their interactions with infrastructural systems. Large-scale and complex agent-based simulations generate a large amount of data in each run of the simulation, making it hard to make sense of results. This leads us to formulate two new problems in simulation analytics. First, we develop algorithms to summarize simulation results by extracting causally-relevant state sequences - state sequences that have a measurable effect on the outcome of interest. Second, in order to develop effective interventions, it is important to understand which behaviors lead to positive and negative outcomes. It may happen that the same behavior may lead to different outcomes, depending upon the context. Hence, we develop an algorithm for contextual behavior ranking. In addition to the context mentioned in the query, our algorithm also identifies any additional context that may affect the behavioral ranking.
  • Thumbnail Image
    Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches
    Brownstein, John S.; Marathe, Achla (JMIR Publications, 2017)
    Background: Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact. Objective: Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions. Methods: We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using Influenzanet and Flu Near You). Results: WISDM-based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40% when compared to the estimates of the model that only incorporates CDC historical information. Conclusions: While the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long-term forecasting of influenza activity in data-poor parts of the world.
  • Thumbnail Image
    Data-Driven Park Planning: Comparative Study of Survey with Social Media Data
    Sim, Jisoo (Virginia Tech, 2020-05-05)
    The purpose of this study was (1) to identify visitors’ behaviors in and perceptions of linear parks, (2) to identify social media users’ behaviors in and perceptions of linear parks, and (3) to compare small data with big data. This chapter discusses the main findings and their implications for practitioners such as landscape architects and urban planners. It has three sections. The first addresses the main findings in the order of the research questions at the center of the study. The second describes implications and recommendations for practitioners. The final section discusses the limitations of the study and suggests directions for future work. This study compares two methods of data collection, focused on activities and benefits. The survey asked respondents to check all the activities they did in the park. Social media users’ activities were detected by term frequency in social media data. Both results ordered the activities similarly. For example social interaction and art viewing were most popular on the High Line, then the 606, then the High Bridge according to both methods. Both methods also reported that High Line visitors engaged in viewing from overlooks the most. As for benefits, according to both methods vistors to the 606 were more satisfied than High Line visitors with the parks’ social and natural benefits. These results suggest social media analytics can replace surveys when the textual information is sufficient for analysis. Social media analytics also differ from surveys in accuracy of results. For example, social media revealed that 606 users were interested in events and worried about housing prices and crimes, but the pre-designed survey could not capture those facts. Social media analytics can also catch hidden and more general information: through cluster analysis, we found possible reasons for the High Line’s success in the arts and in the New York City itself. These results involve general information that would be hard to identify through a survey. On the other hand, surveys provide specific information and can describe visitors’ demographics, motivations, travel information, and specific benefits. For example, 606 users tend to be young, high-income, well educated, white, and female. These data cannot be collected through social media.
  • Thumbnail Image
    Disparities in spread and control of influenza in slums of Delhi: findings from an agent-based modelling study
    Adiga, Abhijin; Chu, Shuyu; Kuhlman, Christopher J.; Lewis, Bryan L.; Marathe, Achla; Nordberg, Eric K.; Swarup, Samarth; Vullikanti, Anil; Wilson, Mandy L. (BMJ Publishing Group, 2017-11-03)
    Objectives: This research studies the role of slums in the spread and control of infectious diseases in the National Capital Territory of India, Delhi, using detailed social contact networks of its residents. Methods: We use an agent-based model to study the spread of influenza in Delhi through person-to-person contact. Two different networks are used: one in which slum and non-slum regions are treated the same, and the other in which 298 slum zones are identified. In the second network, slum-specific demographics and activities are assigned to the individuals whose homes reside inside these zones. The main effects of integrating slums are that the network has more home-related contacts due to larger family sizes and more outside contacts due to more daily activities outside home. Various vaccination and social distancing interventions are applied to control the spread of influenza. Results: Simulation-based results show that when slum attributes are ignored, the effectiveness of vaccination can be overestimated by 30%–55%, in terms of reducing the peak number of infections and the size of the epidemic, and in delaying the time to peak infection. The slum population sustains greater infection rates under all intervention scenarios in the network that treats slums differently. Vaccination strategy performs better than social distancing strategies in slums. Conclusions: Unique characteristics of slums play a significant role in the spread of infectious diseases. Modelling slums and estimating their impact on epidemics will help policy makers and regulators more accurately prioritise allocation of scarce medical resources and implement public health policies.
  • Thumbnail Image
    Ecological and Human Health in Rural Communities
    Gohlke, Julia M.; Kolivras, Korine N.; Krometis, Leigh-Anne H.; Marmagas, Susan West; Marr, Linsey C.; Satterwhite, Emily M.; Angermeier, Paul L.; Clark, Susan F.; Ranganathan, Shyam; Schoenholtz, Stephen H.; Swarup, Samarth; Thompson, Christopher K. (2017-05-15)
    Environmental exposures to chemicals and microbes in the air we breathe, the water we drink, the food we eat, and the objects we touch are now recognized to be responsible for 90% of all human illness. This suggests that well-documented health disparities within and between nations have significant geographic and ecological as well as socioeconomic dimensions that must be addressed in order to secure human well-being at local to global scales. While urbanization is a primary driver of global change, it is widely acknowledged that urbanization is dependent on large-scale resource extraction and agriculture in rural communities. Despite considerable evidence linking human industrial and agricultural activities to ecological health (i.e. health of an ecosystem including the non-human organisms that inhabit it), very little data are available directly linking exposure to environmental pollution and human health in rural areas, which have repeatedly been identified as subject to the most extreme health disparities...
  • Thumbnail Image
    Epidemiological and economic impact of pandemic influenza in Chicago: Priorities for vaccine interventions
    Dorratoltaj, Nargesalsadat; Marathe, Achla; Lewis, Bryan L.; Swarup, Samarth; Eubank, Stephen G.; Abbas, Kaja M. (PLOS, 2017-06-01)
    The study objective is to estimate the epidemiological and economic impact of vaccine interventions during influenza pandemics in Chicago, and assist in vaccine intervention priorities. Scenarios of delay in vaccine introduction with limited vaccine efficacy and limited supplies are not unlikely in future influenza pandemics, as in the 2009 H1N1 influenza pandemic. We simulated influenza pandemics in Chicago using agent-based transmission dynamic modeling. Population was distributed among high-risk and non-high risk among 0±19, 20±64 and 65+ years subpopulations. Different attack rate scenarios for catastrophic (30.15%), strong (21.96%), and moderate (11.73%) influenza pandemics were compared against vaccine intervention scenarios, at 40% coverage, 40% efficacy, and unit cost of $28.62. Sensitivity analysis for vaccine compliance, vaccine efficacy and vaccine start date was also conducted. Vaccine prioritization criteria include risk of death, total deaths, net benefits, and return on investment. The risk of death is the highest among the high-risk 65+ years subpopulation in the catastrophic influenza pandemic, and highest among the high-risk 0±19 years subpopulation in the strong and moderate influenza pandemics. The proportion of total deaths and net benefits are the highest among the high-risk 20±64 years subpopulation in the catastrophic, strong and moderate influenza pandemics. The return on investment is the highest in the high-risk 0±19 years subpopulation in the catastrophic, strong and moderate influenza pandemics. Based on risk of death and return on investment, high-risk groups of the three age group subpopulations can be prioritized for vaccination, and the vaccine interventions are cost saving for all age and risk groups. The attack rates among the children are higher than among the adults and seniors in the catastrophic, strong, and moderate influenza pandemic scenarios, due to their larger social contact network and homophilous interactions in school. Based on return on investment and higher attack rates among children, we recommend prioritizing children (0±19 years) and seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies. Based on risk of death, we recommend prioritizing seniors (65+ years) after high-risk groups for influenza vaccination during times of limited vaccine supplies.
  • Thumbnail Image
    EpiViewer: an epidemiological application for exploring time series data
    Thorve, Swapna; Wilson, Mandy L.; Lewis, Bryan L.; Swarup, Samarth; Vullikanti, Anil Kumar S.; Marathe, Madhav V. (2018-11-22)
    Background Visualization plays an important role in epidemic time series analysis and forecasting. Viewing time series data plotted on a graph can help researchers identify anomalies and unexpected trends that could be overlooked if the data were reviewed in tabular form; these details can influence a researcher’s recommended course of action or choice of simulation models. However, there are challenges in reviewing data sets from multiple data sources – data can be aggregated in different ways (e.g., incidence vs. cumulative), measure different criteria (e.g., infection counts, hospitalizations, and deaths), or represent different geographical scales (e.g., nation, HHS Regions, or states), which can make a direct comparison between time series difficult. In the face of an emerging epidemic, the ability to visualize time series from various sources and organizations and to reconcile these datasets based on different criteria could be key in developing accurate forecasts and identifying effective interventions. Many tools have been developed for visualizing temporal data; however, none yet supports all the functionality needed for easy collaborative visualization and analysis of epidemic data. Results In this paper, we present EpiViewer, a time series exploration dashboard where users can upload epidemiological time series data from a variety of sources and compare, organize, and track how data evolves as an epidemic progresses. EpiViewer provides an easy-to-use web interface for visualizing temporal datasets either as line charts or bar charts. The application provides enhanced features for visual analysis, such as hierarchical categorization, zooming, and filtering, to enable detailed inspection and comparison of multiple time series on a single canvas. Finally, EpiViewer provides several built-in statistical Epi-features to help users interpret the epidemiological curves. Conclusions EpiViewer is a single page web application that provides a framework for exploring, comparing, and organizing temporal datasets. It offers a variety of features for convenient filtering and analysis of epicurves based on meta-attribute tagging. EpiViewer also provides a platform for sharing data between groups for better comparison and analysis. Our user study demonstrated that EpiViewer is easy to use and fills a particular niche in the toolspace for visualization and exploration of epidemiological data.
  • Thumbnail Image
    Finding Succinct Representations For Clusters
    Gupta, Aparna (Virginia Tech, 2019-07-09)
    Improving the explainability of results from machine learning methods has become an important research goal. In this thesis, we have studied the problem of making clusters more interpretable using a recent approach by Davidson et al., and Sambaturu et al., based on succinct representations of clusters. Given a set of objects S, a partition of S (into clusters), and a universe T of descriptors such that each element in S is associated with a subset of descriptors, the goal is to find a representative set of descriptors for each cluster such that those sets are pairwise-disjoint and the total size of all the representatives is at most a given budget. Since this problem is NP-hard in general, Sambaturu et al. have developed a suite of approximation algorithms for the problem. We also show applications to explain clusters of genomic sequences that represent different threat levels
  • Thumbnail Image
    A Framework for Data Quality for Synthetic Information
    Gupta, Ragini (Virginia Tech, 2014-07-24)
    Data quality has been an area of increasing interest for researchers in recent years due to the rapid emergence of 'big data' processes and applications. In this work, the data quality problem is viewed from the standpoint of synthetic information. Based on the structure and complexity of synthetic data, a need to have a data quality framework specific to it was realized. This thesis presents this framework along with implementation details and results of a large synthetic dataset to which the developed testing framework is applied. A formal conceptual framework was designed for assessing data quality of synthetic information. This framework involves developing analytical methods and software for assessing data quality for synthetic information. It includes dimensions of data quality that check the inherent properties of the data as well as evaluate it in the context of its use. The framework developed here is a software framework which is designed considering software design techniques like scalability, generality, integrability and modularity. A data abstraction layer has been introduced between the synthetic data and the tests. This abstraction layer has multiple benefits over direct access of the data by the tests. It decouples the tests from the data so that the details of storage and implementation are kept hidden from the user. We have implemented data quality measures for several quality dimensions: accuracy and precision, reliability, completeness, consistency, and validity. The particular tests and quality measures implemented span a range from low-level syntactic checks to high-level semantic quality measures. In each case, in addition to the results of the quality measure itself, we also present results on the computational performance (scalability) of the measure.
  • Thumbnail Image
    Human Behavior Modeling and Calibration in Epidemic Simulations
    Singh, Meghendra (Virginia Tech, 2019-01-25)
    Human behavior plays an important role in infectious disease epidemics. The choice of preventive actions taken by individuals can completely change the epidemic outcome. Computational epidemiologists usually employ large-scale agent-based simulations of human populations to study disease outbreaks and assess intervention strategies. Such simulations rarely take into account the decision-making process of human beings when it comes to preventive behaviors. Absence of realistic agent behavior can undermine the reliability of insights generated by such simulations and might make them ill-suited for informing public health policies. In this thesis, we address this problem by developing a methodology to create and calibrate an agent decision-making model for a large multi-agent simulation, in a data driven way. Our method optimizes a cost vector associated with the various behaviors to match the behavior distributions observed in a detailed survey of human behaviors during influenza outbreaks. Our approach is a data-driven way of incorporating decision making for agents in large-scale epidemic simulations.
  • Thumbnail Image
    Impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States
    Singh, Meghendra; Sarkhel, Prasenjit; Kang, Gloria J.; Marathe, Achla; Boyle, Kevin J.; Murray-Tuite, Pamela; Abbas, Kaja M.; Swarup, Samarth (2019-03-04)
    Background Self-protective behaviors of social distancing and vaccination uptake vary by demographics and affect the transmission dynamics of influenza in the United States. By incorporating the socio-behavioral differences in social distancing and vaccination uptake into mathematical models of influenza transmission dynamics, we can improve our estimates of epidemic outcomes. In this study we analyze the impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States. Methods We conducted a survey of a nationally representative sample of US adults to collect data on their self-protective behaviors, including social distancing and vaccination to protect themselves from influenza infection. We incorporated this data in an agent-based model to simulate the transmission dynamics of influenza in the urban region of Miami Dade county in Florida and the rural region of Montgomery county in Virginia. Results We compare epidemic scenarios wherein the social distancing and vaccination behaviors are uniform versus non-uniform across different demographic subpopulations. We infer that a uniform compliance of social distancing and vaccination uptake among different demographic subpopulations underestimates the severity of the epidemic in comparison to differentiated compliance among different demographic subpopulations. This result holds for both urban and rural regions. Conclusions By taking into account the behavioral differences in social distancing and vaccination uptake among different demographic subpopulations in analysis of influenza epidemics, we provide improved estimates of epidemic outcomes that can assist in improved public health interventions for prevention and control of influenza.
  • Thumbnail Image
    Modeling and Twitter-based Surveillance of Smoking Contagion
    Tuli, Gaurav (Virginia Tech, 2016-01-08)
    Nicotine, in the form of cigarette smoking, chewing tobacco, and most recently as vapor smoking, is one of the most heavily used addictive drugs in the world. Since smoking imposes a significant health-care and economic burden on the population, there have been sustained and significant efforts for the past several decades to control it. However, smoking epidemic is a complex and "policy-resistant" problem that has proven difficult to control. Despite the known importance of social networks in the smoking epidemic, there has been no network-centric intervention available for controlling the smoking epidemic yet. The long-term goal of this work is the development and implementation of an environment needed for developing network-centric interventions for controlling the smoking contagion. In order to develop such an environment we essentially need: an operationalized model of smoking that can be simulated, to determine the role of online social networks on smoking behavior, and actual methods to perform network-centric interventions. The objective of this thesis is to take first steps in all these categories. We perform Twitter-based surveillance of smoking-related tweets, and use mathematical modeling and simulation techniques to achieve our objective. Specifically, we use Twitter data to infer sentiments on smoking and electronic cigarettes, to estimate the proportion of user population that gets exposed to smoking-related messaging that is underage, and to identify statistically anomalous clusters of counties where people discuss about electronic cigarette a lot more than expected. In other work, we employ mathematical modeling and simulation approach to study how different factors such as addictiveness and peer-influence together contribute to smoking behavior diffusion, and also develop two methods to stymie social contagion. This lead to a total of four smoking contagion-related studies. These studies are just a first step towards the development of a network-centric intervention environment for controlling smoking contagion, and also to show that such an environment is realizable.
  • Thumbnail Image
    Modeling the effect of transient populations on epidemics in Washington DC
    Parikh, Nidhi; Youssef, Mina; Swarup, Samarth; Eubank, Stephen (Nature Publishing Group, 2013-11)
    Large numbers of transients visit big cities, where they come into contact with many people at crowded areas. However, epidemiological studies have not paid much attention to the role of this subpopulation in disease spread. We evaluate the effect of transients on epidemics by extending a synthetic population model for the Washington DC metro area to include leisure and business travelers. A synthetic population is obtained by combining multiple data sources to build a detailed minute-by-minute simulation of population interaction resulting in a contact network. We simulate an influenza-like illness over the contact network to evaluate the effects of transients on the number of infected residents. We find that there are significantly more infections when transients are considered. Since much population mixing happens at major tourism locations, we evaluate two targeted interventions: closing museums and promoting healthy behavior (such as the use of hand sanitizers, covering coughs, etc.) at museums. Surprisingly, closing museums has no beneficial effect. However, promoting healthy behavior at the museums can both reduce and delay the epidemic peak. We analytically derive the reproductive number and perform stability analysis using an ODE-based model.
  • Thumbnail Image
    On the Land, Territory, and Crisis Triad: Enclosure and Capitalist Appropriation of the Russian Land Commune
    Smirnova, Vera (Virginia Tech, 2018-11-13)
    My research provides a historical, geographical reading of land enclosure in the context of economic and agrarian crises in late imperial Russia. Using original records of Russian land deals that I obtained in the federal and provincial archives, I explore how the coalitions of landed nobility, land surveyors, landless serfs, and peasant proprietors used land enclosure as a conduit for coercive governance, accumulation of landed capital, or, in contrary, as a means of resistance. Through critical discourse analysis, I illustrate how the Russian imperial state and territories in the periphery were dialectically co-produced not only through institutional manipulations, resettlement plans, and husbandry manuals, but also through political and public discourses. I argue that land enclosure exploited practices of autonomous land management in the commune and furthered growing agrarian and economic crises in the countryside. The urban periphery became a strategic territory used for the accumulation of new wealth and displacement of two million peasant households, which accommodated capitalist development under the Russian Tsarist and, later, Soviet political regimes. Through this example, my research reexamines predominant assumptions about the land, territory, and crisis triad in Russia by positioning the rural politics of the late imperial period within the global context of land enclosure. At the same time, by focusing on the historical reading of territory from the Russian perspective, this study introduces a more nuanced alternative to the traditional colonial territory discourse often found in Western interpretations.
  • Thumbnail Image
    Tweeting the High Line Life: A Social Media Lens on Urban Green Spaces
    Sim, Jisoo; Miller, Patrick; Swarup, Samarth (MDPI, 2020-10-27)
    The objective of this study is to investigate elevated parks as urban green spaces using social media data analytics. Two popular elevated parks, the High Line Park in New York and the 606 in Chicago, were selected as the study sites. Tweets mentioning the two parks were collected from 2015 to 2019. By using text mining, social media users’ sentiments and conveyed perceptions about the elevated parks were studied. In addition, users’ activities and their satisfaction were analyzed. For the 606, users mainly enjoyed the free events at the park and worried about possible increases in housing prices and taxes because of the 606. They tended to participate in physical activities such as biking and walking. Although the 606 provides scenic observation points, users did not seem to enjoy these. Regarding the High Line, users frequently mentioned New York City, which is an important aspect of the identity of the park. The High Line users also frequently mentioned arts and relaxation. Overall, this study supports the idea that social media analytics can be used to gain an understanding of the public’s use of urban green spaces and their attitudes and concerns.