scholarly journals Towards a PS-InSAR Based Prediction Model for Building Collapse: Spatiotemporal Patterns of Vertical Surface Motion in Collapsed Building Areas—Case Study of Alexandria, Egypt

2020 ◽  
Vol 12 (20) ◽  
pp. 3307
Author(s):  
Bahaa Mohamadi ◽  
Timo Balz ◽  
Ali Younes
Keyword(s):  
Time Series ◽  
Hot Spot ◽  
Vertical Surface ◽  
Spatiotemporal Patterns ◽  
Cold Spot ◽  
Vertical Deformation ◽  
Building Collapse ◽  
Collapsed Buildings ◽  
The City ◽  
Collapsed Building

Buildings are vulnerable to collapse incidents. We adopt a workflow to detect unusual vertical surface motions before building collapses based on PS-InSAR time series analysis and spatiotemporal data mining techniques. Sentinel-1 ascending and descending data are integrated to decompose vertical deformation in the city of Alexandria, Egypt. Collapsed building data were collected from official sources, and overlayed on PS-InSAR vertical deformation results. Time series deformation residuals are used to create a space–time cube in the ArcGIS software environment and analyzed by emerging hot spot analysis to extract spatiotemporal patterns for vertical deformation around collapsed buildings. Our results show two spatiotemporal patterns of new cold spot or new hot spot before the incidents in 66 out of 68 collapsed buildings between May 2015 and December 2018. The method was validated in detail on four collapsed buildings between January and May 2019, proving the applicability of this workflow to create a temporal vulnerability map for building collapse monitoring. This study is a step forward to create a PS-InSAR based model for building collapse prediction in the city.

scholarly journals Population characteristics and distribution patterns of slum areas in Palembang City: Getis ord gi* analysis

2020 ◽  
Vol 200 ◽  
pp. 04005
Author(s):  
Sukmaniar ◽  
Andri Kurniawan ◽  
Agus Joko Pitoyo
Keyword(s):  
Hot Spot ◽  
Sampling Technique ◽  
Distribution Patterns ◽  
Population Characteristics ◽  
Cold Spot ◽  
City Center ◽  
Sample Distribution ◽  
Cross Tabulation ◽  
The City ◽  
Average Occupation

The paper aims to describe the population characteristics and the distribution patterns of slums in Palembang City. The research employs a quantitative method with 382 respondents. The data are analyzed using cross-tabulation of IBM SPSS 23 to know the population characteristics. Meanwhile, the distribution patterns of slums are analyzed by observing the sample distribution through the proportional random sampling technique. It is carried out by calculating the number of buildings of each area and noting the coordinates of each sample using GPS essentials application. The data are recorded and inserted into the sample spots on the map, which were then analyzed using the High-Low Clustering Report of Getis Ord General Gi*, to see the distribution pattern, especially the cold spot and hot spot, through ArcMap 103 program. The research found that non-migrant married Moslems dominate the population of Palembang city, with the average occupation is labor or manual worker. The slum distribution forms a low cluster pattern, meaning that it has a low value. The value is due to the government’s effort to manage the city and the development of the market sector, limiting the slum distribution. Getis Ord Gi* analysis revealed that the slum area in the city center and within a dense population is a cold spot (low cluster), while those far from the city center yet are still crowded are hot spots (high cluster).

scholarly journals A STATISTICAL TEXTURE FEATURE FOR BUILDING COLLAPSE INFORMATION EXTRACTION OF SAR IMAGE

2018 ◽  
Vol XLII-3 ◽  
pp. 871-876
Author(s):  
L. Li ◽  
H. Yang ◽  
Q. Chen ◽  
X. Liu
Keyword(s):  
Information Extraction ◽  
Texture Feature ◽  
Statistical Distribution ◽  
Sar Images ◽  
Yushu Earthquake ◽  
Building Collapse ◽  
Building Information ◽  
Sar Data ◽  
Collapsed Buildings ◽  
Collapsed Building

Synthetic Aperture Radar (SAR) has become one of the most important ways to extract post-disaster collapsed building information, due to its extreme versatility and almost all-weather, day-and-night working capability, etc. In view of the fact that the inherent statistical distribution of speckle in SAR images is not used to extract collapsed building information, this paper proposed a novel texture feature of statistical models of SAR images to extract the collapsed buildings. In the proposed feature, the texture parameter of G<sup>0</sup> distribution from SAR images is used to reflect the uniformity of the target to extract the collapsed building. This feature not only considers the statistical distribution of SAR images, providing more accurate description of the object texture, but also is applied to extract collapsed building information of single-, dual- or full-polarization SAR data. The RADARSAT-2 data of Yushu earthquake which acquired on April 21, 2010 is used to present and analyze the performance of the proposed method. In addition, the applicability of this feature to SAR data with different polarizations is also analysed, which provides decision support for the data selection of collapsed building information extraction.

Spatial Hotspot Analysis of Bucharest’s Urban Heat Island (UHI) Using Modis Data

2018 ◽  
Vol 18 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Georgiana Grigoraș ◽  
Bogdan Urițescu
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Hot Spots ◽  
Land Surface ◽  
Hot Spot ◽  
Monitoring Network ◽  
Residential Areas ◽  
Hotspot Analysis ◽  
The City

Abstract The aim of the study is to find the relationship between the land surface temperature and air temperature and to determine the hot spots in the urban area of Bucharest, the capital of Romania. The analysis was based on images from both moderate-resolution imaging spectroradiometer (MODIS), located on both Terra and Aqua platforms, as well as on data recorded by the four automatic weather stations existing in the endowment of The National Air Quality Monitoring Network, from the summer of 2017. Correlation coefficients between land surface temperature and air temperature were higher at night (0.8-0.87) and slightly lower during the day (0.71-0.77). After the validation of satellite data with in-situ temperature measurements, the hot spots in the metropolitan area of Bucharest were identified using Getis-Ord spatial statistics analysis. It has been achieved that the “very hot” areas are grouped in the center of the city and along the main traffic streets and dense residential areas. During the day the "very hot spots” represent 33.2% of the city's surface, and during the night 31.6%. The area where the mentioned spots persist, falls into the "very hot spot" category both day and night, it represents 27.1% of the city’s surface and it is mainly represented by the city center.

Organic, Planned or Both: Alserkal Avenue: An Art District by Entrepreneurial Action in an Organic Evolutionary Context

2020 ◽  
Vol 46 (2) ◽  
pp. 102-118
Author(s):  
Damien D. Nouvel
Keyword(s):  
Hot Spot ◽  
Industrial Area ◽  
Evolutionary Processes ◽  
Short Period ◽  
Grassroots Initiatives ◽  
Evolutionary Context ◽  
Urban Scene ◽  
Entrepreneurial Action ◽  
The City

While Dubai's urban scene is dominated by planned and pre-designed developments, grassroots initiatives have always been present and have helped shape the trajectory of the city's evolution. In one case, an industrial area, Al Quoz, has seen the clustering of art businesses over a relatively short period turning it into a cultural destination. Accounting for most of such clustering, Alserkal Avenue became Dubai's art hot-spot that changed the cultural map of the city. This article describes the rise of Alserkal Avenue, not only as the result of the entrepreneurial action of the proprietors but also as a product of a complex melange of economic, cultural, and urban evolutionary processes that intertwine with the rise of the city itself.

scholarly journals Impact of COVID-19 pandemic lockdown on myocardial infarction care

2021 ◽  
Author(s):  
Timo Schmitz ◽  
Christa Meisinger ◽  
Inge Kirchberger ◽  
Christian Thilo ◽  
Ute Amann ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Hot Spot ◽  
Population Based ◽  
Downward Trend ◽  
German Model ◽  
Model Region ◽  
The Impact ◽  
The City

AbstractThe aim of this study was to evaluate the impact of the COVID-19 pandemic lockdown on acute myocardial infarction (AMI) care, and to identify underlying stressors in the German model region for complete AMI registration. The analysis was based on data from the population-based KORA Myocardial Infarction Registry located in the region of Augsburg, Germany. All cases of AMI (n = 210) admitted to one of four hospitals in the city of Augsburg or the county of Augsburg from February 10th, 2020, to May 19, 2020, were included. Patients were divided into three groups, namely pre-lockdown, strict lockdown, and attenuated lockdown period. An additional survey was conducted asking the patients for stress and fears in the 4 weeks prior to their AMI. The AMI rate declined by 44% in the strict lockdown period; in the attenuated lockdown period the rate was 17% lower compared to the pre-lockdown period. The downward trend in AMI rates during lockdown was seen in STEMI and NSTEMI patients, and independent of sex and age. The door-to-device time decreased by 70–80% in the lockdown-periods. In the time prior to the infarction, patients felt stressed mainly due to fear of infection with Sars-CoV-2 and less because of the restrictions and consequences of the lockdown. A strict lockdown due to the Covid-19 pandemic had a marked impact on AMI care even in a non-hot-spot region with relatively few cases of COVID-19. Fear of infection with the virus is presumably the main reason for the drop in hospitalizations due to AMI.

scholarly journals Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 974
Author(s):  
Lorena Alves Santos ◽  
Karine Ferreira ◽  
Michelle Picoli ◽  
Gilberto Camara ◽  
Raul Zurita-Milla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Time Series ◽  
Satellite Image ◽  
Spatiotemporal Patterns ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Self Organizing Maps ◽  
Machine Learning Methods ◽  
Land Use And Cover

The use of satellite image time series analysis and machine learning methods brings new opportunities and challenges for land use and cover changes (LUCC) mapping over large areas. One of these challenges is the need for samples that properly represent the high variability of land used and cover classes over large areas to train supervised machine learning methods and to produce accurate LUCC maps. This paper addresses this challenge and presents a method to identify spatiotemporal patterns in land use and cover samples to infer subclasses through the phenological and spectral information provided by satellite image time series. The proposed method uses self-organizing maps (SOMs) to reduce the data dimensionality creating primary clusters. From these primary clusters, it uses hierarchical clustering to create subclusters that recognize intra-class variability intrinsic to different regions and periods, mainly in large areas and multiple years. To show how the method works, we use MODIS image time series associated to samples of cropland and pasture classes over the Cerrado biome in Brazil. The results prove that the proposed method is suitable for identifying spatiotemporal patterns in land use and cover samples that can be used to infer subclasses, mainly for crop-types.

scholarly journals Effects of climate change on spatiotemporal patterns of tropical cyclone tracks and their implications for coastal agriculture in Myanmar

2021 ◽  
Author(s):  
Akira Hirano
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Considerable Increase ◽  
Hot Spot ◽  
Coastal Region ◽  
Storm Surges ◽  
Coastal Areas ◽  
Spatiotemporal Patterns ◽  
Sea Levels ◽  
Rising Sea Levels

AbstractImportant aspects for understanding the effects of climate change on tropical cyclones (TCs) are the frequency of TCs and their tracking patterns. Coastal areas are increasingly threatened by rising sea levels and associated storm surges brought on by TCs. Rice production in Myanmar relies strongly on low-lying coastal areas. This study aims to provide insights into the effects of global warming on TCs and the implications for sustainable development in vulnerable coastal areas in Myanmar. Using TC records from the International Best Track Archive for Climate Stewardship dataset during the 30-year period from 1983 to 2012, a hot spot analysis based on Getis-Ord (Gi*) statistics was conducted to identify the spatiotemporal patterns of TC tracks along the coast of Myanmar. The results revealed notable changes in some areas along the central to southern coasts during the study period. These included a considerable increase in TC tracks (p value < 0.01) near the Ayeyarwady Delta coast, otherwise known as “the rice bowl” of the nation. This finding aligns with trends in published studies and reinforced the observed trends with spatial statistics. With the intensification of TCs due to global warming, such a significant increase in TC experiences near the major rice-producing coastal region raises concerns about future agricultural sustainability.

scholarly journals Urban influence on the concentration and composition of submicron particulate matter in central Amazonia

2018 ◽  
Author(s):  
Suzane S. de Sá ◽  
Brett B. Palm ◽  
Pedro Campuzano-Jost ◽  
Douglas A. Day ◽  
Weiwei Hu ◽  
...  
Keyword(s):  
Time Series ◽  
Particulate Matter ◽  
Air Quality ◽  
Nitrogen Oxides ◽  
Mass Concentration ◽  
Critical Role ◽  
Metropolitan Region ◽  
Anthropogenic Emissions ◽  
Organic Mass ◽  
The City

Abstract. Fundamental to quantifying the influence of human activities on climate and air quality is an understanding of how anthropogenic emissions affect the concentrations and composition of airborne particulate matter (PM). The central Amazon basin, especially around the city of Manaus, Brazil, has experienced rapid changes in the past decades due to ongoing urbanization. Herein, changes in the concentration and composition of submicron PM due to pollution downwind of the Manaus metropolitan region are reported as part of the GoAmazon2014/5 experiment. A high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and a suite of other gas- and particle-phase instruments were deployed at the T3 research site, 70 km downwind of Manaus, during the wet season. At this site, organic components represented on average 79 ± 7 % of the non-refractory PM1 mass concentration, which was in the same range as several upwind sites. The organic PM1 was, however, considerably more oxidized at T3 compared to upwind measurements. Positive-matrix factorization (PMF) was applied to the time series of organic mass spectra collected at the T3 site, yielding three factors representing secondary processes (73 ± 15 % of total organic mass concentration) and three factors representing primary anthropogenic emissions (27 ± 15 %). Fuzzy c-means clustering (FCM) was applied to the afternoon time series of concentrations of NOy, ozone, total particle number, black carbon, and sulfate. Four clusters were identified and characterized by distinct airmass origins and particle compositions. Two clusters, Bkgd-1 and Bkgd-2, were associated with background conditions. Bkgd-1 appeared to represent near-field atmospheric PM production and oxidation of a day or less. Bkgd-2 appeared to represent material transported and oxidized for two or more days, often with out-of-basin contributions. Two other clusters, Pol-1 and Pol-2, represented the Manaus influence, one apparently associated with the northern region of Manaus and the other with the southern region of the city. A composite of the PMF and FCM analyses provided insights into the anthropogenic effects on PM concentration and composition. The increase in mass concentration of submicron PM ranged from 25 % to 200 % under polluted compared to background conditions, including contributions from both primary and secondary PM. Furthermore, a comparison of PMF factor loadings for different clusters suggested a shift in the pathways of PM production under polluted conditions. Nitrogen oxides may have played a critical role in these shifts. Increased concentrations of nitrogen oxides can shift pathways of PM production from HO2-dominant to NO-dominant as well as increase the concentrations of oxidants in the atmosphere. Consequently, the oxidation of biogenic and anthropogenic precursor gases as well as the oxidative processing of pre-existing atmospheric PM can be accelerated. The combined set of results demonstrates the susceptibility of atmospheric chemistry, air quality, and associated climate forcing to anthropogenic perturbations over tropical forests.

A Novel Solution to Compute Stress Time Series in Nonlinear Hydro-Structure Simulations

2015 ◽  
Author(s):  
Fabien Bigot ◽  
François-Xavier Sireta ◽  
Eric Baudin ◽  
Quentin Derbanne ◽  
Etienne Tiphine ◽  
...  
Keyword(s):  
Time Series ◽  
Frequency Domain ◽  
Time Domain ◽  
Hot Spot ◽  
Structural Response ◽  
Container Ship ◽  
Time Step ◽  
Computation Cost ◽  
Hull Girder ◽  
Non Linear

Ship transport is growing up rapidly, leading to ships size increase, and particularly for container ships. The last generation of Container Ship is now called Ultra Large Container Ship (ULCS). Due to their increasing sizes they are more flexible and more prone to wave induced vibrations of their hull girder: springing and whipping. The subsequent increase of the structure fatigue damage needs to be evaluated at the design stage, thus pushing the development of hydro-elastic simulation models. Spectral fatigue analysis including the first order springing can be done at a reasonable computational cost since the coupling between the sea-keeping and the Finite Element Method (FEM) structural analysis is performed in frequency domain. On the opposite, the simulation of non-linear phenomena (Non linear springing, whipping) has to be done in time domain, which dramatically increases the computation cost. In the context of ULCS, because of hull girder torsion and structural discontinuities, the hot spot stress time series that are required for fatigue analysis cannot be simply obtained from the hull girder loads in way of the detail. On the other hand, the computation cost to perform a FEM analysis at each time step is too high, so alternative solutions are necessary. In this paper a new solution is proposed, that is derived from a method for the efficient conversion of full scale strain measurements into internal loads. In this context, the process is reversed so that the stresses in the structural details are derived from the internal loads computed by the sea-keeping program. First, a base of distortion modes is built using a structural model of the ship. An original method to build this base using the structural response to wave loading is proposed. Then a conversion matrix is used to project the computed internal loads values on the distortion modes base, and the hot spot stresses are obtained by recombination of their modal values. The Moore-Penrose pseudo-inverse is used to minimize the error. In a first step, the conversion procedure is established and validated using the frequency domain hydro-structure model of a ULCS. Then the method is applied to a non-linear time domain simulation for which the structural response has actually been computed at each time step in order to have a reference stress signal, in order to prove its efficiency.

scholarly journals Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

2017 ◽  
Vol 11 (5) ◽  
pp. 2329-2343 ◽  
Author(s):  
Taylor Smith ◽  
Bodo Bookhagen ◽  
Aljoscha Rheinwalt
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Climate Model ◽  
Single Point ◽  
Passive Microwave ◽  
Spatiotemporal Patterns ◽  
Signal Separation ◽  
High Mountain ◽  
The Tibetan Plateau ◽  
Microwave Data

Abstract. High Mountain Asia (HMA) – encompassing the Tibetan Plateau and surrounding mountain ranges – is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications – such as agriculture, drinking-water generation, and hydropower – rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season – defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3–5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade−1 over the 29-year study period (5–25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002–2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers – such as the Karakoram and Kunlun Shan – see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous.

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