Statistics for Spatial Data.

Exploring pathways linking greenspace to health: Theoretical and methodological guidance.

[1] The atmospheric chemistry of volatile organic compounds (VOCs) in urban areas results in the formation of ‘photochemical smog’, including secondary organic aerosol (SOA). State-of-the-art SOA models parameterize the results of simulation chamber experiments that bracket the conditions found in the polluted urban atmosphere. Here we show that in the real urban atmosphere reactive anthropogenic VOCs (AVOCs) produce much larger amounts of SOA than these models predict, even shortly after sunrise. Contrary to current belief, a significant fraction of the excess SOA is formed from first-generation AVOC oxidation products. Global models deem AVOCs a very minor contributor to SOA compared to biogenic VOCs (BVOCs). If our results are extrapolated to other urban areas, AVOCs could be responsible for additional 3–25 Tg yr−1 SOA production globally, and cause up to −0.1 W m−2 additional top-of-the-atmosphere radiative cooling.

Secondary Organic Aerosol Formation from Anthropogenic Air Pollution: Rapid and Higher than Expected

Thirty-three brothels in rural and small-town Nevada, which contain between 225 and 250 prostitutes, are legal or openly tolerated and strictly controlled by state statute, city and county ordinances, and local rules. Twenty-two of the brothels are in places with populations between 500 and 8,000, and the remaining eleven are in rural areas. The legal and quasi-legal restrictions placed on prostitutes severely limit their activities outside brothels. These restrictions in conjunction with historical inertia, perceived benefits of crime and venereal disease control, and the good image of madams contribute to widespread positive local attitudes toward brothel prostitution. Interactions between clients and prostitutes in brothel parlors are also restricted and limited to a few basic types which are largely determined by entrepreneurial philosophy. KEY WORDS : Nevada, Political geography, Prostitution, Restricted activity spaces.

ANNALS of the Association of American Geographers

Background: At a time of increasing disconnectedness from nature, scientific interest in the potential health benefits of nature contact has grown. Research in recent decades has yielded substantia...

https://www.fs.fed.us/pnw/pubs/journals/pnw_2017_frumkin001.pdf

Nature Contact and Human Health: A Research Agenda

Assessing street-level urban greenery using Google Street View and a modified green view index

Using appropriate techniques to fill the data gaps in SLC-off ETM+ imagery may enable more scientific use of the data. The local linear histogram-matching technique chosen by USGS has limitations if the scenes being combined exhibit high temporal variability and radical differences in target radiance due, for example, to the presence of clouds. This study proposes using an alternative interpolation method, the kriging geostatistical technique, for filling the data gaps. The case study shows that the ordinary kriging techniques may provide a powerful tool for interpolating the missing pixels in the SLC-off ETM+ imagery. While the standardized ordinary cokriging has been shown to be particularly useful when samples of the variable to be predicted are sparse and samples of a second, related variable are plentiful, the case study demonstrates that it provides little improvement in interpolating the data gap in the SLC-off imagery.

Gaps-fill of SLC-off Landsat ETM+ satellite image using a geostatistical approach

Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging

Who lives in greener neighborhoods? The distribution of street greenery and its association with residents' socioeconomic conditions in Hartford, Connecticut, USA

Urban building segmentation is a prevalent research domain for very high resolution (VHR) remote sensing; however, various appearances and complicated background of VHR remote sensing imagery make accurate semantic segmentation of urban buildings a challenge in relevant applications. Following the basic architecture of U-Net, an end-to-end deep convolutional neural network (denoted as DeepResUnet) was proposed, which can effectively perform urban building segmentation at pixel scale from VHR imagery and generate accurate segmentation results. The method contains two sub-networks: One is a cascade down-sampling network for extracting feature maps of buildings from the VHR image, and the other is an up-sampling network for reconstructing those extracted feature maps back to the same size of the input VHR image. The deep residual learning approach was adopted to facilitate training in order to alleviate the degradation problem that often occurred in the model training process. The proposed DeepResUnet was tested with aerial images with a spatial resolution of 0.075 m and was compared in performance under the exact same conditions with six other state-of-the-art networks—FCN-8s, SegNet, DeconvNet, U-Net, ResUNet and DeepUNet. Results of extensive experiments indicated that the proposed DeepResUnet outperformed the other six existing networks in semantic segmentation of urban buildings in terms of visual and quantitative evaluation, especially in labeling irregular-shape and small-size buildings with higher accuracy and entirety. Compared with the U-Net, the F1 score, Kappa coefficient and overall accuracy of DeepResUnet were improved by 3.52%, 4.67% and 1.72%, respectively. Moreover, the proposed DeepResUnet required much fewer parameters than the U-Net, highlighting its significant improvement among U-Net applications. Nevertheless, the inference time of DeepResUnet is slightly longer than that of the U-Net, which is subject to further improvement.

https://www.mdpi.com/2072-4292/11/15/1774/pdf

Weidong Li

Papers

Assessing street-level urban greenery using Google Street View and a modified green view index

Gaps-fill of SLC-off Landsat ETM+ satellite image using a geostatistical approach

Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging

Who lives in greener neighborhoods? The distribution of street greenery and its association with residents' socioeconomic conditions in Hartford, Connecticut, USA

Semantic Segmentation of Urban Buildings from VHR Remote Sensing Imagery Using a Deep Convolutional Neural Network