WWLLN Hot and Cold-Spots of Lightning Activity and Their Relation to Climate in an Extended Central America Region 2012–2020

Lightning activity has been recognized to have, historically, social and environmental consequences around the globe. This work analyzes the space-time distribution of lightning-densities (D) in an extended Central America region (ECA). World Wide Lightning Location Network data was analyzed to link D with dominant climate patterns over the ECA for 2012–2020. D associated with cold surges entering the tropics dominate during boreal winter. The highest D (hot-spots) was found to agree well with previously known sites, such as the “Catatumbo” in Venezuela; however, D was lower here due to different detection efficiencies. Previously reported hot-spots showed strong continental signals in CA; however, in this work, they were over the oceans near to coastlines, especially in the eastern tropical Pacific (ETP). Most cold-spots, implying a minimum of vulnerability to human impacts and to some industries, were situated in the Caribbean Sea side of Central America. The Mid-Summer-Drought and the Caribbean-Low-Level-Jet (CLLJ) markedly reduced the D during July-August. The CLLJ in the central CS and across the Yucatan and the southern Gulf of Mexico acts as a lid inhibiting convection due to its strong vertical shear during the boreal summer. The CLLJ vertical wind-shear and its extension to the Gulf of Papagayo also diminished convection and considerably decreased the D over a region extending westward into the ETP for at least 400–450 km. A simple physical mechanism to account for the coupling between the CLLJ, the MSD, and lightning activity is proposed for the latter region.

The Caucasus Territory Hot-Cold Spots Determination And Description Using 2D Surface Waves Tomography

In order to identify and describe Hot-Cold spots inside the earth based on increasing and decreasing wave velocity anomalies, this paper attempts to generate the first 2D tomographic maps of Rayleigh surface wave velocity dispersion curves, by using ~1200 local-regional earthquake data and ~30000 vertical (Z) components of earthquake data waveform energy with magnitude M≥4 from 1999 to 2018 in a periods range of 5 to 70 seconds and a grid spacing of 0.2º×0.5º for a depth of ~200 km. To conduct this, a generalized 2D linear inversion procedure developed by Yanovskaya and Ditmar has been applied to construct the first 2D Rayleigh tomography velocity maps in order to understand better the regional tectonic activities in the enigmatic ongoing collision-compressed edge zone of the Eurasian-Arabic plates. In this study, we assumed that low-velocity (slow) region with dark red shade is hot spot and high-velocity (fast) region with dark blue-green-yellow is a cold spot. In short and medium periods were determined the number of 15 and 2 hot spots with a depth of 7 to 108 km, respectively. In long-periods and a depth of ~200 km, most part of the area study has covered by low-velocity anomaly.

A Spatial Analysis of Robbery Rate in the City of Detroit using Exploratory Data Analysis Approach

Many U.S. cities have experienced rising crime rates in recent years. Crime has inherent geographic quality and tend to concentrate in certain places within the city. To prioritize public safety and crime prevention strategies, it is important to identify where crime is occurring and with what severity. Using spatial statistics including the average nearest neighbour index, Moran’s I, Getis-Ord Gi* statistic, and Anselin Cluster and Outlier Analysis, this study investigates robbery locations within the city of Detroit over 5-year period, 2016 to 2020 to identify hot spots, cold spots and spatial patterns across two different spatial scale – block group and census tracts. The study seeks to understand the effect of data aggregation on each spatial scale on the outcome of the analysis to determine the most optimum spatial scale to study robbery rates. The study concludes that, spatial analysis at small scale like block group level is most informative. Policy implications and areas for further research are provided.

Software-defined cooking using a microwave oven

Despite widespread popularity, today's microwave ovens are limited in their cooking capabilities, given that they heat food blindly, resulting in a nonuniform and unpredictable heating distribution. We present software-defined cooking (SDC), a low-cost closed-loop microwave oven system that aims to heat food in a software-defined thermal trajectory. SDC achieves this through a novel high-resolution heat sensing and actuation system that uses microwave-safe components to augment existing microwaves. SDC first senses the thermal gradient by using arrays of neon lamps that are charged by the electromagnetic (EM) field a microwave produces. SDC then modifies the EM-field strength to desired levels by accurately moving food on a programmable turntable toward sensed hot and cold spots. To create a more skewed arbitrary thermal pattern, SDC further introduces two types of programmable accessories: A microwave shield and a susceptor. We design and implement one experimental test bed by modifying a commercial off-the-shelf microwave oven. Our evaluation shows that SDC can programmatically create temperature deltas at a resolution of 21°C with a spatial resolution of 3 cm without the programmable accessories, and 183°C with them. We further demonstrate how an SDC-enabled microwave can be enlisted to perform unexpected cooking tasks: Cooking meat and fat in bacon discriminatively and heating milk uniformly.

Cosmological particle production and pairwise hotspots on the CMB

Heavy particles with masses much bigger than the inflationary Hubble scale H*, can get non-adiabatically pair produced during inflation through their couplings to the inflaton. If such couplings give rise to time-dependent masses for the heavy particles, then following their production, the heavy particles modify the curvature perturbation around their locations in a time-dependent and scale non-invariant manner. This results into a non-trivial spatial profile of the curvature perturbation that is preserved on superhorizon scales and eventually generates localized hot or cold spots on the CMB. We explore this phenomenon by studying the inflationary production of heavy scalars and derive the final temperature profile of the spots on the CMB by taking into account the subhorizon evolution, focusing in particular on the parameter space where pairwise hot spots (PHS) arise. When the heavy scalar has an $$ \mathcal{O} $$ O (1) coupling to the inflaton, we show that for an idealized situation where the dominant background to the PHS signal comes from the standard CMB fluctuations themselves, a simple position space search based on applying a temperature cut, can be sensitive to heavy particle masses M0/H* ∼ $$ \mathcal{O} $$ O (100). The corresponding PHS signal also modifies the CMB power spectra and bispectra, although the corrections are below (outside) the sensitivity of current measurements (searches).

Spatial analysis of colorectal cancer outcomes and socioeconomic factors in Virginia

Background Colorectal cancer (CRC) disparities vary by country and population group, but often have spatial features. This study of the United States state of Virginia assessed CRC outcomes, and identified demographic, socioeconomic and healthcare access contributors to CRC disparities. Methods County- and city-level cross-sectional data for 2011–2015 CRC incidence, mortality, and mortality-incidence ratio (MIR) were analyzed for geographically determined clusters (hotspots and cold spots) and their correlates. Spatial regression examined predictors including proportion of African American (AA) residents, rural-urban status, socioeconomic (SES) index, CRC screening rate, and densities of primary care providers (PCP) and gastroenterologists. Stationarity, which assesses spatial equality, was examined with geographically weighted regression. Results For incidence, one CRC hotspot and two cold spots were identified, including one large hotspot for MIR in southwest Virginia. In the spatial distribution of mortality, no clusters were found. Rurality and AA population were most associated with incidence. SES index, rurality, and PCP density were associated with spatial distribution of mortality. SES index and rurality were associated with MIR. Local coefficients indicated stronger associations of predictor variables in the southwestern region. Conclusions Rurality, low SES, and racial distribution were important predictors of CRC incidence, mortality, and MIR. Regions with concentrations of one or more factors of disparities face additional hurdles to improving CRC outcomes. A large cluster of high MIR in southwest Virginia region requires further investigation to improve early cancer detection and support survivorship. Spatial analysis can identify high-disparity populations and be used to inform targeted cancer control programming.

Major environmental factors and traits of invasive alien plants determine their spatial distribution: a case study in Korea

Background As trade increases, the influx of various alien species and their spread to new regions are prevalent, making them a general problem globally. Anthropogenic activities and climate change have led to alien species becoming distributed beyond their native range. As a result, alien species can be easily found anywhere, with the density of individuals varying across locations. The prevalent distribution of alien species adversely affects invaded ecosystems; thus, strategic management plans must be established to control them effectively. To this end, this study evaluated hotspots and cold-spots in the degree of distribution of invasive alien plant species, and major environmental factors related to hot spots were identified. We analyzed 10,287 distribution points of 126 species of alien plant species collected through a national survey of alien species using the hierarchical model of species communities (HMSC) framework. Results The explanatory and fourfold cross-validation predictive power of the model were 0.91 and 0.75 as area under the curve (AUC) values, respectively. Hotspots of invasive plants were found in the Seoul metropolitan area, Daegu metropolitan city, Chungcheongbuk-do Province, southwest shore, and Jeju Island. Hotspots were generally found where the highest maximum summer temperature, winter precipitation, and road density were observed. In contrast, seasonality in temperature, annual temperature range, precipitation during summer, and distance to rivers and the sea were negatively correlated to hotspots. The model showed that functional traits accounted for 55% of the variance explained by environmental factors. Species with a higher specific leaf area were found where temperature seasonality was low. Taller species were associated with a larger annual temperature range. Heavier seed mass was associated with a maximum summer temperature > 29 °C. Conclusions This study showed that hotspots contained 2.1 times more alien plants on average than cold-spots. Hotspots of invasive plants tended to appear under less stressful climate conditions, such as low fluctuations in temperature and precipitation. In addition, disturbance by anthropogenic factors and water flow positively affected hotspots. These results were consistent with previous reports on the ruderal and competitive strategies of invasive plants, not the stress-tolerant strategy. Our results supported that the functional traits of alien plants are closely related to the ecological strategies of plants by shaping the response of species to various environmental filters. Therefore, to control alien plants effectively, the occurrence of disturbed sites where alien plants can grow in large quantities should be minimized, and the waterfront of rivers must be managed.

Geospatial Analysis of the Impact of Flood and Drought Hazards on Crop Land and Its Relationship with Human Migration at the District Level in Uttar Pradesh, India

The main objective was to explore the connection between flood and drought hazards and their impact on crop land and human migration. The Flood and Drought effect on Cropland Index (FDCI), hot spot analysis and the Global Regression Analysis method was applied for the identification of the relationship between human migration and flood and drought hazards. The spatial pattern and hot and cold spots of FDCI, spatial autocorrelation and Getis-OrdGi* statistic techniques were used respectively. The FDCI was taken as an explanatory variable and human migration was taken as a dependent variable in the environment of the geographically weighted regression (GWR) model which was applied to measure the impact of flood and drought hazards on human migration. FDCI suggests a z-score of 4.9, which shows that the impact of flood and drought frequency on crop land is highly clustered. In the case of the hot spots analysis, out of seventy districts in Uttar Pradesh twenty-one were classified as hot spot and eight were classified as cold spots with a confidence level of 90 to 99%. Hot spot indicate maximum and cold spots show minimum impact of flood and drought hazards on crop land. The impact of flood and drought hazards on human migration show that there are fourteen districts where migration out is far more than predicted while there are ten districts where migration out is far lower.