Urban growth machine

Urban growth machine is an influential thesis of urban politics that suggests the objective of growth unites otherwise pluralistic interests in relation to a city. The thesis is situated within a broader theory about the commodification of place, where place is understood to be socially and economically valued land. Its key premise is that coalitions of actors and organizations (i.e. growth machines), all sharing an interest in local growth and its effects on land values, compete with growth machines elsewhere for scarce mobile capital investment, while simultaneously attempting to gain the tacit support of local publics for such urban growth. Following an introductory overview, this entry discusses the urban growth machine in two main parts. The first part sets out the key concepts underlying the growth machine thesis: use value, exchange value and place; place entrepreneurs; growth machines and their allies; competing for mobile capital; and promoting growth as a public good. The second part identifies core issues and debates in relation to the thesis (particularly those made by human geographers), including critiques of: the property focus; the human agency focus; difficulties with international comparison; the conceptualization of local dependency and scale; and the relationship of political projects with local feeling

Spatial snow water equivalent estimation for mountainous areas using wireless-sensor networks and remote-sensing products

We developed an approach to estimate snow water equivalent (SWE) through interpolation of spatially representative point measurements using a k-nearest neighbors (k-NN) algorithm and historical spatial SWE data. It accurately reproduced measured SWE, using different data sources for training and evaluation. In the central-Sierra American River basin, we used a k-NN algorithm to interpolate data from continuous snow-depth measurements in 10 sensor clusters by fusing them with 14 years of daily 500-m resolution SWE-reconstruction maps. Accurate SWE estimation over the melt season shows the potential for providing daily, near real-time distributed snowmelt estimates. Further south, in the Merced-Tuolumne basins, we evaluated the potential of k-NN approach to improve real-time SWE estimates. Lacking dense ground-measurement networks, we simulated k-NN interpolation of sensor data using selected pixels of a bi-weekly Lidar-derived snow water equivalent product. k-NN extrapolations underestimate the Lidar-derived SWE, with a maximum bias of −10 cm at elevations below 3000 m and +15 cm above 3000 m. This bias was reduced by using a Gaussian-process regression model to spatially distribute residuals. Using as few as 10 scenes of Lidar-derived SWE from 2014 as training data in the k-NN to estimate the 2016 spatial SWE, both RMSEs and MAEs were reduced from around 20–25 cm to 10–15 cm comparing to using SWE reconstructions as training data. We found that the spatial accuracy of the historical data is more important for learning the spatial distribution of SWE than the number of historical scenes available. Blending continuous spatially representative ground-based sensors with a historical library of SWE reconstructions over the same basin can provide real-time spatial SWE maps that accurately represents Lidar-measured snow depth; and the estimates can be improved by using historical Lidar scans instead of SWE reconstructions

Noticias accidentales: el gran vertido de crudo como suceso local y acontecimiento nacional.

Sin resume

Las noticias como conducta intencionada: sobre el uso estratégico de los acontecimientos rutinarios, los accidentes y los escándalos.

Sin resume

Spectral Profiler Probe for In Situ Snow Grain Size and Composition Stratigraphy

An ultimate goal of the climate change, snow science, and hydrology communities is to measure snow water equivalent (SWE) from satellite measurements. Seasonal SWE is highly sensitive to climate change and provides fresh water for much of the world population. Snowmelt from mountainous regions represents the dominant water source for 60 million people in the United States and over one billion people globally. Determination of snow grain sizes comprising mountain snowpack is critical for predicting snow meltwater runoff, understanding physical properties and radiation balance, and providing necessary input for interpreting satellite measurements. Both microwave emission and radar backscatter from the snow are dominated by the snow grain size stratigraphy. As a result, retrieval algorithms for measuring snow water equivalents from orbiting satellites is largely hindered by inadequate knowledge of grain size

Las noticias como conducta intencionada: sobre el uso estratégico de los acontecimientos rutinarios, los accidentes y los escándalos.

Sin resume

Weekly high-resolution multi-spectral and thermal uncrewed-aerial-system mapping of an alpine catchment during summer snowmelt, Niwot Ridge, Colorado

Alpine ecosystems are experiencing rapid change as a result of warming temperatures and changes in the quantity, timing and phase of precipitation. This in turn impacts patterns and processes of ecohydrologic connectivity, vegetation productivity and water provision to downstream regions. The fine-scale heterogeneous nature of these environments makes them challenging areas to measure with traditional instrumentation and spatiotemporally coarse satellite imagery. This paper describes the data collection, processing, accuracy assessment and availability of a series of approximately weekly-interval uncrewed-aerial-system (UAS) surveys flown over the Niwot Ridge Long Term Ecological Research site during the 2017 summer-snowmelt season. Visible, near-infrared and thermal-infrared imagery was collected. This unique series of 5–25 cm resolution multi-spectral and thermal orthomosaics provides a unique snapshot of seasonal transitions in a high alpine catchment. Weekly radiometrically calibrated normalised difference vegetation index maps can be used to track vegetation health at the pixel scale through time. Thermal imagery can be used to map the movement of snowmelt across and within the near sub-surface as well as identify locations where groundwater is discharging to the surface. A 10 cm resolution digital surface model and dense point cloud (146 points m−2) are also provided for topographic analysis of the snow-free surface. These datasets augment ongoing data collection within this heavily studied and important alpine site; they are made publicly available to facilitate wider use by the research community. Datasets and related metadata can be accessed through the Environmental Data Initiative Data Portal, https://doi.org/10.6073/pasta/dadd5c2e4a65c781c2371643f7ff9dc4 (Wigmore, 2022a), https://doi.org/10.6073/pasta/073a5a67ddba08ba3a24fe85c5154da7 (Wigmore, 2022c), https://doi.org/10.6073/pasta/a4f57c82ad274aa2640e0a79649290ca (Wigmore and Niwot Ridge LTER, 2021a), https://doi.org/10.6073/pasta/444a7923deebc4b660436e76ffa3130c (Wigmore and Niwot Ridge LTER, 2021b), https://doi.org/10.6073/pasta/1289b3b41a46284d2a1c42f1b08b3807 (Wigmore and Niwot Ridge LTER, 2022a), https://doi.org/10.6073/pasta/70518d55a8d6ec95f04f2d8a0920b7b8 (Wigmore and Niwot Ridge LTER, 2022b). A summary of the available datasets can be found in the data availability section below.</p