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64 results for “Urban Scaling”
Data from: Fine-scale flight strategies of gulls in urban airflows indicate risk and reward in city living
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Urbanization reduces genetic connectivity in bobcats (Lynx rufus) at both intra- and inter-population spatial scales
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Data from: Nest suitability, fine-scale population structure and male-mediated dispersal of a solitary ground nesting bee in an urban landscape
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Data from: Separating the effects of water quality and urbanization on temperate insectivorous bats at the landscape scale
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Data from: Functional diversity of phyllostomid bats in an urban-rural landscape: a scale-dependent analysis
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Data from: The traits that predict the magnitude and spatial scale of forest bird responses to urbanization intensity
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Fine-scale variation within urban landscapes affects marking patterns and gastrointestinal parasite diversity in red foxes
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At what spatial scale(s) do mammals respond to urbanization?
<p>Spatial scale is fundamental in understanding species-landscape relationships because species' responses to landscape characteristics typically vary across scales. Nonetheless, such scales are often unidentified or unreliably predicted by theory. Many landscapes worldwide are urbanizing, yet the spatial scaling of species' responses to urbanization is poorly understood. We investigated the spatial scaling of urbanization effects on a community of 15 mammal species using ~ 60,000 wildlife detections collected from a constellation of 207 camera traps across an extensive urban park system. We embedded a bivariate Gaussian kernel in hierarchical multi-species models to determine two scales of effect (a scale of maximal effect and a broader scale of cumulative landscape effect) for two biological responses (occupancy and site visit frequency) across two seasons (winter and summer) for each species. We then assessed whether scales of effect varied according to theoretical predictions associated with biological responses and species traits (body size and mobility). Scales of effect ranged from < 50 m to > 9,000 m and varied among species, but not as predicted by theory. Species' occupancy generally showed a weak response to urbanization and the scale of this effect was both highly uncertain and consistent across species. We did not detect any relationship between scales of effect and species' body size or mobility, nor was there any evident pattern of scaling across biological response or seasons. These results imply that 1) urbanization effects on mammals manifest across a very broad spectrum of spatial scales, and 2) current theories that a priori predict the scale at which urbanization affects mammals may be of limited use within a given system. Overall, this study suggests that developing general theory regarding the scaling of species-landscape relationships requires additional empirical work conducted across multiple species, systems, and timescales.</p>
Data from: A model of urban scaling laws based on distance dependent interactions
Socio-economic related properties of a city grow faster than a linear relationship with the population, in a log–log plot, the so-called superlinear scaling. Conversely, the larger a city, the more efficient it is in the use of its infrastructure, leading to a sublinear scaling on these variables. In this work, we addressed a simple explanation for those scaling laws in cities based on the interaction range between the citizens and on the fractal properties of the cities. To this purpose, we introduced a measure of social potential which captured the influence of social interaction on the economic performance and the benefits of amenities in the case of infrastructure offered by the city. We assumed that the population density depends on the fractal dimension and on the distance-dependent interactions between individuals. The model suggests that when the city interacts as a whole, and not just as a set of isolated parts, there is improvement of the socio-economic indicators. Moreover, the bigger the interaction range between citizens and amenities, the bigger the improvement of the socio-economic indicators and the lower the infrastructure costs of the city. We addressed how public policies could take advantage of these properties to improve cities development, minimizing negative effects. Furthermore, the model predicts that the sum of the scaling exponents of social-economic and infrastructure variables are 2, as observed in the literature. Simulations with an agent-based model are confronted with the theoretical approach and they are compatible with the empirical evidences.
Data from: The Urban Heat Island and its spatial scale dependent impact on survival and development in butterflies of different thermal sensitivity
Climate alteration is one of the most cited ecological consequences of urbanization. However, the magnitude of this impact is likely to vary with spatial scale. We investigated how this alteration affects the biological fitness of insects, which are especially sensitive to ambient conditions and well-suited organisms to study urbanization-related changes in phenotypic traits. We monitored temperature and relative air humidity in wooded sites characterized by different levels of urbanization in the surroundings. Using a split-brood design experiment, we investigated the effect of urbanization at the local (i.e., 200 × 200 m) and landscape (i.e., 3 × 3 km) scale on two key traits of biological fitness in two closely related butterfly species that differ in thermal sensitivity. In line with the Urban Heat Island concept, urbanization led to a 1°C increase in daytime temperature and an 8% decrease in daytime relative humidity at the local scale. The thermophilous species Lasiommata megera responded at the local scale: larval survival increased twofold in urban compared to rural sites. Urbanized sites tended to produce bigger adults, although this was the case for males only. In the woodland species Pararge aegeria, which has recently expanded its ecological niche, we did not observe such a response, neither at the local, nor at the landscape scale. These results demonstrate interspecific differences in urbanization-related phenotypic plasticity and larval survival. We discuss larval pre-adaptations in species of different ecological profiles to urban conditions. Our results also highlight the significance of considering fine-grained spatial scales in urban ecology.
Data from: Assessing urban-scale spatiotemporal heterogeneous metro station coverage using multi-source mobility data
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U-Surf: a global 1km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling
<p>High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset to represent the spatial heterogeneity of urban surfaces and their biophysical properties. This deficiency has long obstructed the development of urban-resolving Earth System Models (ESMs) and ultra-high-resolution urban climate modeling, particularly at large scales. Here, we present a first-of-its-kind 1km-resolution present-day (circa-2020) global continuous urban surface parameter dataset – U-Surf. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for developing dataset requirements, U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for UCMs at the facet- and canopy-level. Our high-resolution U-Surf dataset significantly improves the representation of the urban land heterogeneity both within and across cities globally. U-Surf provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs, enables detailed city-to-city comparisons across the globe, and supports the next-generation kilometer-resolution Earth system modeling across scales. U-Surf parameters can be easily converted or adapted to various types of UCMs, such as those embedded in weather and regional climate models, as well as air quality models. The fundamental urban surface constraints provided by U-Surf are also relevant as features for machine learning models and can have other broad-scale applications for socioeconomic, public health, and urban planning contexts. We expect U-Surf to promote the research frontier on urban systems science, climate-sensitive urban design, and coupled human-Earth systems in the future.</p> <p>The complete list of parameters is presented in the table below.</p> <table> <tbody> <tr> <td>Category</td> <td>Parameter</td> <td>Notes</td> </tr> <tr> <td>Radiative</td> <td>Roof | Impervious | Pervious canyon floor | Wall emissivity</td> <td> </td> </tr> <tr> <td> </td> <td>Roof | Impervious | Pervious canyon floor | Wall albedo</td> <td> </td> </tr> <tr> <td>Morphological</td> <td>Roof | Pervious fraction</td> <td>Roof fraction is w.r.t. urban horizontal surface, and pervious fraction is w.r.t. canyon floor (i.e. pervious and impervious canyon floor).</td> </tr> <tr> <td> </td> <td>Building height</td> <td>Unit: m; Height of wind in the canyon is simply set as half of the building height in CLMU.</td> </tr> <tr> <td> </td> <td>Canyon height-to-width ratio</td> <td> </td> </tr> <tr> <td> </td> <td>Urban percentage</td> <td> </td> </tr> <tr> <td>Thermal</td> <td>Roof | Wall thickness</td> <td>Unit: m</td> </tr> <tr> <td> </td> <td>Roof | Impervious canyon floor | Wall thermal conductivity</td> <td>Unit: W/m*K</td> </tr> <tr> <td> </td> <td>Roof | Impervious canyon floor | Wall volumetric heat capacity</td> <td>Unit: J/m^3*K</td> </tr> <tr> <td> </td> <td>Number of impervious canyon floor layer</td> <td> </td> </tr> <tr> <td> </td> <td>Minimum | Maximum interior building temperature</td> <td>Unit: K</td> </tr> <tr> <td> </td> <td>Air conditioning adoption rate</td> <td> </td> </tr> </tbody> </table> <p> </p> <p>Radiative and morphological parameters are presented in the format of both .tif and .nc to accommodate different needs for the urban climate modeling community. Thermal parameters adapted from CLMU are available in a single .nc file. A CESM-compatiable surface dataset and a time-variant urban dataset (including P_AC and T_BUILDING_MAX; Li et al., 2024) at standard resolution (0.9375°x1.25°) are included for direct simulation use. Note that the urban percentage used to create the surface dataset comes from the PCT_URBAN parameter calculated in U-Surf, but users can input their own urban extent data to generate a customized surface dataset. The raw 1-km data can be easily aggregated/regridded to other resolution as needed.</p> <p> </p> <p><strong>Version 1.1 updates:</strong></p> <p>1. Fill part of the data gaps in Asia. </p> <p>2. Change the aggregation method of some parameters to be facet-area weighted in the 1deg surfdata.</p>
Supplementary material 3 from: Wübbelmann T, Bouwer LM, Förster K, Bender S, Burkhard B (2022) Urban ecosystems and heavy rainfall – A Flood Regulating Ecosystem Service modelling approach for extreme events on the local scale. One Ecosystem 7: e87458. https://doi.org/10.3897/oneeco.7.e87458
Maps of the individual potential FRES demand indicators
Supplementary material 2 from: Wübbelmann T, Bouwer LM, Förster K, Bender S, Burkhard B (2022) Urban ecosystems and heavy rainfall – A Flood Regulating Ecosystem Service modelling approach for extreme events on the local scale. One Ecosystem 7: e87458. https://doi.org/10.3897/oneeco.7.e87458
Table of the scaled Flood Regulating Ecosystem Services indicators and categories
Supplementary material 1 from: Wübbelmann T, Bouwer LM, Förster K, Bender S, Burkhard B (2022) Urban ecosystems and heavy rainfall – A Flood Regulating Ecosystem Service modelling approach for extreme events on the local scale. One Ecosystem 7: e87458. https://doi.org/10.3897/oneeco.7.e87458
Ratio of FRES supply and flood hazard
evolution of urban scaling BR
<p>This is the data used for the paper. Evolution if urban scaling: evidences for Brazil</p>
Dataset on 'Green in grey: ecosystem services and disservices perceptions from small-scale green infrastructure along a rural-urban gradient in Bengaluru, India'
<p>This dataset includes the raw data of a survey of 649 residents of 61 villages along a rural-urban gradient in Bengaluru, India. It presents socio-demographic characteristics (village, village rural-urban state, age, gender, level of education, caste, and sources of household income) and Likert-scale answers (ranging from 1, i.e., not important to 5, i.e., very important) to rank the perceived importance of ecosystem services and disservice from five types of small-scale green infrastructure: Domestic trees (DT), Farm trees (FT), Street trees (ST), Platform trees (PT), Temple trees (TT).</p> <p>The complete method is described in Thapa, P., Torralba, M., Bhaskar, D., Nagendra, H., Plieninger, T. (2023): Green in grey: ecosystem services and disservices perceptions from small-scale green infrastructure along a rural-urban gradient in Bengaluru, India. Ecosystems and People, in press.</p>
Data from: The Urban Heat Island and its spatial scale dependent impact on survival and development in butterflies of different thermal sensitivity
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At what spatial scale(s) do mammals respond to urbanization?
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Data from: A model of urban scaling laws based on distance dependent interactions
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.