Terrestrial megafauna (animals >1,000 kg) have substantial impacts on ecosystems while alive; yet, we know little about the impacts of these ecosys...

Terrestrial megafauna (animals >1,000 kg) have substantial impacts on ecosystems while alive; yet, we know little about the impacts of these ecosys...

A sudden drop in temperature is often associated with hauntings. The question is why? We also look at Mary King’s Close.

I have two datasets that I would like to analyze together. The first dataset has bird nest locations in neighborhoods (nest). The second dataset has bird feeder locations in neighborhoods (food). At each X,Y location of food I also have information about the amount of food (extent of the resource). Each of these are point shapefiles in ArcGIS. I would like to create a smoothed surface (food raster) of food so I can extract a food index value for the location of each nest in my dataset. The food index should depend on the nearness of a birdfeeder to that nest, and the extent of food at that birdfeeder. I used Getis Ord Gi* to generate zscores for each of my food locations (weighting each location based on extent of resource), and then used these values in IDW analysis. However, the Getis Ord Gi* zscores are both positive and negative because they compare the food value at each location to the mean food value at all of the feeders within the neighborhood. The result is that some of my feeders turn out to be "coldspots" in the resulting IDW raster. What I'd like is a food raster where all of the food locations are "hot" (to varying degrees dependent on extent) and unmeasured locations are by definition "colder" than these (because I know there are not feeders there). Does anyone know of a way to do this (e.g. constrain the Zscores so they are all positive or otherwise specify that the measured points are all "hot")? Or do I need to use an alternate analysis altogether? I have tested KDE on these data as well but would prefer IDW if possible because multiple fields of KDE are chosen at the "whim" of the researcher. I think I may have a difficult time defending choices of bandwidth, etc. with my datasets.

This is an old cotton mill that opened in 1896. The mill is in bad shape, but you can still get in. There are blood spots in some rooms, cold spots, and

Mount Kilimanjaro, with its steep elevational gradient (770–5886 m a.s.l.) and pronounced land-use heterogeneity, supports high biodiversity and diverse nature’s contributions to people (NCP), but it is underrepresented in global spatial assessments. We address this gap by mapping NCP supply across 12 ecosystem types on the southern slopes identifying hotspots and coldspots and quantifying synergies and trade-offs among NCP categories. We use 25 context-specific NCP categories that integrate local and scientific knowledge with field measurements and remote-sensing-derived proxies. Combining long-term field data with remote sensing and machine learning, we upscaled plot-scale indicators into standardized supply maps. Total NCP supply is strongly concentrated in mid-elevation ecosystems: the 1100–2200 m band alone accounted for ∼59% of total supply compared with ∼18% in the lowlands (700–1100 m), and the 1100–2800 m belts together provide ∼73%, whereas high-elevation zones (2800–4600 m) contribute

There have been sightings of a tall male figure in the theatre. This figure appears pale blue and has been seen in the broom closet on the left side of

Through a series of fascinating examples, physicist and data-visualisation specialist César Hidalgo shows how scientific laws of time, space and value allow us to chart how knowledge moves and spreads in the 21st century, helping us understand the emergence of hot and coldspots for scientific and economic growth and development. Why is it that Silicon Valley in California or Zhongguancun in Beijing are such successful hubs for innovation, where other locations have failed? What sustains the exponential growth in some technologies, like computers, while we forgot how to make Polaroid film? Watch at: youtu.be

Using survey data from 3,560 hospitals across the USA, AI implementation is shown to be spatially heterogeneous, influenced by the local context and institutional characteristics.