A Demographic Analysis of Urban Nature Utilization

Ferrara, Enrico; Liotta, Antonio; Ndubuaku, Maryleen U.; Erhan, Laura; Giusto, Daniele D.; Richardson, Miles; Sheffield, David; McEwan, Kirsten

doi:10.1109/ceec.2018.8674206

Cited by 5 publications

(4 citation statements)

References 16 publications

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“…Neozephyrus quercus and Thecla betulae are two examples of species which can be found in urban settings but may be poorly sampled by transect counts, due to their elusive behaviour and the propensity for colonies to be supported by single mature trees of Quercus spp. (Ferrara et al, 2018;Niell et al, 2007) and Fraxinus excelsior respectively. For these canopy-top specialists, transect surveys of adults and models based upon them may always be sub-optimal, and any interpretation of monitoring data must be caveated as such.…”

Section: Discussionmentioning

confidence: 99%

Using butterfly survey data to model habitat associations in urban developments

Cooper,

Plummer,

Middlebrook

et al. 2024

Journal of Applied Ecology

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One approach for measuring the potential biodiversity in new urban construction projects is through ecological models that predict how wildlife will respond. For the United Kingdom, such models have only been developed for birds, but to maximise the extent to which models represent overall biodiversity, species from different indicator groups must be considered. Here, we assess this possibility for butterflies by combining citizen science survey data with high‐resolution digital maps. We derive detailed characteristics of urban landscapes around survey sites using previously established methods and quantify their relationship to counts of adults of 18 butterfly species in urban and peri‐urban settings. Higher butterfly counts were found when traversing urban sites with larger areas of semi‐natural grassland, other managed greenspaces and adjacent arable land. Most of the butterfly community were found to have negative relationships with highly built‐up or fragmented landscapes. We found high species‐specificity for different details of urban form, particularly in habitat elements such as gardens, vegetation around railways and grass verges. Policy implications. Improving biodiversity is now part of legislation governing new construction projects from England and Wales. However, predicting quantitative changes from hypothetical land‐use modifications remains challenging. Our models provide the foundation through which butterfly abundance could be integrated into an urban biodiversity assessment tool, providing species‐ and community‐level statistics to non‐specialists from the urban planning and design sector. This would allow them to hone configurations for built surfaces, private gardens, greenspaces and wildlife areas and assess their capacity to provide residents with the intended access to nature.

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Section: Discussionmentioning

confidence: 99%

Using butterfly survey data to model habitat associations in urban developments

Cooper,

Plummer,

Middlebrook

et al. 2024

Journal of Applied Ecology

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“…People mobility analysis is usually related to urban planning programs, such as the development of green areas to improve wellbeing (Ferrara et al 2018) and studies of travel demand to provide a better transportation system (Demissie et al 2019). Human mobility also contributes to disease transmission, as mentioned by Findlater & Bogoch (2018) that greater human mobility will lead to the increase in the frequency of infection as well as the range of spread.…”

Section: Literature Reviewmentioning

confidence: 99%

Mobility Pattern Changes in Indonesia in Response to COVID-19

Pramana

Yuniarti

Paramartha

et al. 2021

ECON FINANC INDONES

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All countries affected by the COVID-19 pandemic have established several policies to control the spread of the disease. The government of Indonesia has enforced a work-from-home policy and large-scale social restrictions in most regions that result in the changes in community mobility in various categories of places. This study aims to (1) investigate the impact of large-scale restrictions on provincial-level mobility in Indonesia, (2) categorize provinces based on mobility patterns, and (3) investigate regional socio-economic characteristics that may lead to different mobility patterns. This study utilized Provincial-level Google Mobility Index, Flight data scraped from daily web, and regional characteristics (e.g., poverty rate, percentages of informal workers). A Dynamic Time Warping method was employed to investigate the clusters of mobility. The study shows an intense trade-off of mobility pattern between residential areas and public areas. In general, during the first 2.5 months of the pandemic, people had reduced their activities in public areas and preferred to stay at home. Meanwhile, provinces have different mobility patterns from each other during the period of the large-scale restrictions. The differences in mobility are mainly led by the percentage of formal workers in each region.

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“…We could thus determine how much time people spent in green areas, the type of activity and find out the top interaction areas. However, the initial studies based on this dataset have been based on a post-collection (offline) analysis of a wealth of objective and subjective data [9], [11], which proved difficult in terms of achieving statistical significance over broad geographical areas. Recently, we have reported on the benefit of urban analytics, for the purpose of understanding the interaction between citizen and city [10], which has motivated the new approached proposed herein (based on real-time, intelligent data collection/analysis) to pursue more effective urban analysis studies.…”

Section: B the Iwun Datasetmentioning

confidence: 99%

“…The outcome is an interactive data analysis framework for urban environments that we put to test in the context of a citizens' well-being project, which we have previously investigated through static data collection methods and bulk data analysis [9]- [11]. In comparison with our earlier works, herein the whole subjective data extraction process is dynamically adapting to context (e.g.…”

Section: Introductionmentioning

confidence: 99%

An AI Approach to Collecting and Analyzing Human Interactions With Urban Environments

et al. 2019

Self Cite

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Thanks to advances in Internet of Things and crowd-sensing, it is possible to collect vast amounts of urban data, to better understand how citizens interact with cities and, in turn, improve human well-being in urban environments. This is a scientifically challenging proposition, as it requires new methods to fuse objective (heterogeneous) data (e.g. people location trails and sensors data) with subjective (perceptual) data (e.g. the citizens' quality of experience collected through feedback forms). When it comes to vast urban areas, collecting statistically significant data is a daunting task; thus new data-collection methods are required too. In this work, we turn to artificial intelligence (AI) to address these challenges, introducing a method whereby the objective, sensor data is analyzed in real-time to scope down the test matrix of the subjective questionnaires. In turn, subjective responses are parsed through AI models to extract further objective information. The outcome is an interactive data analysis framework for urban environments, which we put to test in the context of a citizens' well-being project. In our pilot study, each new entry (objective or subjective) is parsed through the AI engine to determine which action maximizes the information gain. This translates into a particular question being fired at a specific moment and place, to a specific person. With our AI data collection method, we can reach statistical significance much faster, achieving (in our city-wide pilot study) a 41% acceleration factor and a 75% reduction in intrusiveness. Our study opens new avenues in urban science, with potential applications in urban planning, citizen's well-being projects, and sociology, to mention but a few cases.

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A Demographic Analysis of Urban Nature Utilization

Cited by 5 publications

References 16 publications

Using butterfly survey data to model habitat associations in urban developments

Using butterfly survey data to model habitat associations in urban developments

Mobility Pattern Changes in Indonesia in Response to COVID-19

An AI Approach to Collecting and Analyzing Human Interactions With Urban Environments

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