Geospatial Analysis of Urban Heat Island Effects and Tree Equity

In recent decades, Urban Heat Island Effects have become more pronounced and more widely examined. Despite great technological advances, our current societies still experience great spatial disparity in urban forest access. Urban Heat Island Effects are measurable phenomenon that are being experienced by the world’s most urbanized areas, including increased summer high temperatures and lower evapotranspiration from having impervious surfaces instead of vegetation and trees. Tree canopy cover is our natural mitigation tool that absorbs sunlight for photosynthesis, protects humans from incoming radiation, and releases cooling moisture into the air. Unfortunately, urban areas typically have low levels of vegetation. Vulnerable urban communities are lower-income areas of inner cities with less access to heat protection like air conditioners. This study uses mean evapotranspiration levels to assess the variability of urban heat island effects across the state of Tennessee. Results show that increased developed land surface cover in Tennessee creates measurable changes in atmospheric evapotranspiration. As a result, the mean evapotranspiration levels in areas with less tree vegetation are significantly lower than the surrounding forested areas. Central areas of urban cities in Tennessee had lower mean evapotranspiration recordings than surrounding areas with less development. This work demonstrates the need for increased tree canopy coverage.

Modelling potential distribution of a pine bark beetle in Mexican temperate forests using forecast data and spatial analysis tools

Accurate and reliable predictions of pest species distributions in forest ecosystems are urgently needed by forest managers to develop management plans and monitor new areas of potential establishment.Presence-only species distribution models are commonly used in these evaluations.The maximum entropy algorithm(MaxEnt)has gained popularity for modelling species distribution.Here,MaxEnt was used to model the spatial distribution of the Mexican pine bark beetle(Dendroctonus mexicanus)in a daily fashion by using forecast data from the Weather Research and Forecasting model.This study aimed to exploit freely available geographic and environmental data and software and thus provide a pathway to overcome the lack of costly data and technical guidance that are a challenge to implementing national monitoring and management strategies in developing countries.Our results showed overall agreement values between 60 and 87%.The results of this research can be used for D.mexicanus monitoring and management and may aid as a model to monitor similar species.

Spatial analysis of synergies and trade-offs between the Sustainable Development Goals (SDGs) in Africa

Challenges faced by African countries in achieving the goals of sustainable development are similar and trans-boundary. Previous analysis of Africa’s progress on the Sustainable Development Goals (SDGs) has largely beennon-spatial, reducing the ability to find spatial relationships between countries and SDGs to help cooperationand proffer country-specific interventions. This study adopted techniques of exploratory and inferential spatialstatistics to assess the successes of African countries from 2016 to 2020 in achieving the goals of sustainable de-velopment. Also, the study sought to understand how the spatial synergies and trade-offs between SDGs vary percountry and time. The results revealed that spatial hotspots of countries with high SDGs scores were mostly con-fined to northern African countries with significant coldspots within central and eastern Africa and few patchesin western and southern Africa for 2016. In 2020, the number of countries forming hotspots reduced, with Cen-tral African countries as significant cold spots. Five main spatial relationships: positive linear, negative linear,concave, convex and undefined complex, were found among countries and the SDGs. However, these spatialrelationships were fluid as they changed over time and with different levels of influence from 2016 to 2020.The study concludes that generic solutions and policies by development agencies, governments, developmentfinance instiutions and other impact investors will not be enough in achieving the SDGs because of the spatialheterogeneity of the continent. Tailored and country-specific policies based on results of spatial statistics matter.

The MET Analysis of Yield Performance of Advanced Sorghum [<i>Sorghum Bicolor</i>(L.) Moench] Lines under Moisture Stress Areas Using Spatial Analysis

Sorghum is one of the most widely cultivated cereal crops in Ethiopia which is grown for food and feed uses. It’s far an indigenous crop that’s grown in incredibly diverse environments of getting diverse water strain, soil fertility, and temperature situations. Trait of sorghum varieties tolerant to drought and producing desirable grain yield at the same time as addressing the biomass requirement is one of the techniques within the sorghum breeding program to the dry lowland surroundings so one can feed the growing population in Ethiopia. A total of 126 superior early maturing sorghum elite lines had been evaluated through along with recently released popular trendy check Melkam and Argiti to estimate the grain yield and stability of overall performance throughout the testing environments. Based on the overall performance of grain yield, flowering time, plant height, and the stability of grain yield genotype ETSC14501-2-2 and 14MWLSDT7196 become top ranked followed by genotype 14MWLSDT7176, 14MWLSDT7241 and 13MWF6#6037 which could be a capability candidate for production to the target environments. The varieties had better grain yield performance and stability across the environment, which may be used as capacity parental lines for genetic improvement in the sorghum improvement program. Finally based on the presented result on early maturing variety ETSC14501-2-2 with the pedigree of Redswazi/Meko-1 identified and registered for variety verification across locations on stations and on farms to confirm the stability and preference by farmers with their own farming practices.

Distinguishing of Stable Genotypes and Mega Environment for Grain Yield Performance of Sorghum [<i>Sorghum bicolor</i>(L.) Moench] Genotypes Using Spatial Analysis

Sorghum is a staple food crop in Ethiopia and its production is mainly constrained by drought, other environmental factors, and the use of low-yielding, local sorghum varieties. To improve sorghum productivity, it is crucial to provide farmers with high yielding, stable sorghum cultivars that are tolerant to drought and other constraints. The stable performance of sorghum varieties in a growing region is critical to obtain a high and stable yield. In the 2012-2014 crop year, 24 genotypes, including standard controls, were evaluated at the national variety trial stage over six main dry lowland sorghum growing sites and two years made 7 environments to evaluate their performance, stability and to quantify Genotype by Environment Interaction (GEI) across moisture stress sorghum growing areas of Ethiopia. Spatial modeling has been used to estimate predicted mean (BLUPs) results and Performance and estimation of environmental correlation, heritability, GEI, and other parameters using the ASReml3-R analysis package. The predicted mean yield of the test genotypes across the environment ranged from 3.45 to 1.56 t·ha-1. Based on the result genotype G13, it could be further promoted because of its yield advantage and other important attributes over the standard checks, but it is the least stable. Based on the analyzed result, two mega environments were formed and Environment 1 (E1) is identified as an ideal environment among the testing environments.

A Decision Support System for Spatial Analysis of Agricultural Production in Madagascar

In this article, our research aims to set up a geo-decisional system, more precisely we are particularly interested in the spatial analysis system of agricultural production in Madagascar. For this, we used the spatial data warehouse technique based on the SOLAP spatial analysis tool. After having defined the concepts underlying these systems, we propose to address the research issues related to them from four points of view: needs study of the Malagasy Ministry of Agriculture, modeling of a multidimensional conceptual model according to the MultiDim model and the implementation of the system studied using GeoKettle, PostGIS, GeoServer, SPAGO BI and Géomondrian technologies. This new system helps improve the decision-making process for agricultural production in Madagascar.

Spatial Analysis of Risks and Vulnerabilities to Major Hazards in Madagascar Using the Multi-Criteria Method Based on the Analytical Hierarchy Process (AHP)

Natural disasters are not negligible factors that have significant impacts on a country’s development. Madagascar cannot escape cyclones, floods and drought due to its geographical situation. The objective in this work is to assess the risks and vulnerability to these hazards in order to strengthen the resilience of the Malagasy population. Our approach is based on multi-criteria spatial analysis using the Analytical Hierarchy Process (AHP). The results form decision spatial information that can be used at the strategic level of natural risk and disaster management. This work focuses on the degree of vulnerability and it was found in this study that the Androy and Atsimo-Atsinanana regions are the most vulnerable to major hazards in Madagascar not only because of their exposure to risk but also because of their very low socio-economic status.

Systematized Literature Review on Spatial Analysis of Environmental Risk Factors of Malaria Transmission

Malaria is still the major parasitic disease in the world, with approximately 438,000 deaths in 2015. Environmental risk factors (ERF) have been widely studied, however, there are discrepancies in the results about their influence on malaria transmission. Recently, papers have been published about geospatial analysis of ERF of malaria to explain why malaria varies from place to place. Our primary objective was to identify the environmental variables most used in the geospatial analysis of malaria transmission. The secondary objective was to identify the geo-analytic methods and techniques, as well as geo-analytic statistics commonly related to ERF and malaria. We conducted a systematized review of articles published from January 2004 to March 2015, within Web of Science, Pubmed and LILACS databases. Initially 676 articles were found, after inclusion and exclusion criteria, 29 manuscripts were selected. Temperature, land use and land cover, surface moisture and vector breeding site were the most frequent included variables. As for geo-analytic methods, geostatistical models with Bayesian framework were the most applied. Kriging interpolations, Geographical Weighted Regression as well as Kulldorff’s spatial scan were the techniques more widely used. The main objective of many of these studies was to use these methods and techniques to create malaria risk maps. Spatial analysis performed with satellite images and georeferenced data are increasing in relevance due to the use of remote sensing and Geographic Information System. The combination of these new technologies identifies ERF more accurately, and the use of Bayesian geostatistical models allows a wide diffusion of malaria risk maps. It is known that temperature, humidity vegetation and vector breeding site play a critical role in malaria transmission;however, other environmental risk factors have also been identified. Risk maps have a tremendous potential to enhance the effectiveness of malaria-control programs.

Spatial Analysis and Modelling of Wind Farm Site Suitability in Nasarawa State, North-Central Nigeria

There has been an increasing global and local interest in developing renewable, clean, and cheap energy towards achieving Goal number 7 of the Sustainable Development Goals (SDG). However, decisions involving suitable and sustainable locations for renewable energy projects remain an important task. This study employed Geographic Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) to spatially analyze and model wind farm site suitability in Nasarawa State. The aim is to integrate the environmental, social, and economic aspects of decision-making for identifying sustainable wind farm sites. The study distinguished between two sets of decision criteria: decision constraints and decision factors. The former defined the exclusion zones while the latter were standardized based on fuzzy logic to depict varying degrees of suitability across the State. The MCDA applied the weighted linear combination method, with relative weights generated through pairwise comparisons of the analytic hierarchy process to analyze three policy scenarios: equal weights, environmental/social priority, and economic priority scenario. A combination of resulting composite maps from the constraints and the factors gave the final suitability maps. The resulting suitability index (SI) for the respective policy scenario describes the degrees of suitability: Ideal locations were denoted by one (1) and the not suitable locations by zero (0), with values in-between depicting varying degrees of wind farm site suitability. Based on the SI, priority locations indicating areas with good prospects, in addition to the most suitable parcels of land, were identified and delineated. The composite decision constraint revealed that wind farm projects would not be viable in more than half (57.58%) of the State. Wind speed was the major constraint and accounted for the exclusion of 46.25%, with a mean fuzzy membership value of 0.2008 indicating low suitability across the State. Also, the average acceptable wind farm location for the three-policy scenario was 33.33% of the entire study area. Lafia, Obi, Keana, Awe, Nasarawa-Eggon, Wamba and Kokona LGAs were the identified priority Local Government Areas (LGAs). However, only Lafia, Obi, and Nasarawa-Eggon were consistent with changes in the policy objectives. All the priority LGAs have one or more of the most suitable parcels within their administrative boundaries except for Wamba. Despite the severe limitations of wind speed, substantial parts of Nasarawa State still provide great development potentials for wind energy. The “most suitable” locations in Lafia, Nasarawa-Eggon, and Obi LGAs should have first consideration for the development of wind energy in the State.

Spatial Analysis of Traffic Accidents in the City of Medina Using GIS

The general objective of this research is to determine how to use the spatial analysis of traffic accidents in Medina Menorah City through geographic information systems. This research aimed to identify, locate and define the sites where traffic accidents are concentrated and determine the need to apply specific safety standards to reduce accidents and identify their causes thereof. This current research applied the analytical descriptive approach for its relevance with this specific research. This research collected traffic accidents data from the Ministry of the Interior, Department of General Traffic. That data captured the hotspots accidents in Medina Menorah City. Some of the most important results of the study are as follows: many roads were selected as High Accident Location HAL, such as Central Ring Roads, King Faisal bin Abdul-Aziz Road, Prince Abdul Majid bin Abdul-Aziz Road, and King Abdulla bin Abdel-Aziz Road. The high-speed roads are heavily linked to the massive increase of traffic accident rates, and the increase in the street section length led to the soaring number of total accidents. The study recommended performing more studies and different highway safety studies to identify and locate accident patterns on road networks. Due to the fact that the accidents concentration is intensely focused on Medina City center and Prophet’s Mosque, it is a must to increase the number of public transportations to and from Prophet’s Mosque, particularly during the Hajj period, because of the fact that the visitors of Prophet’s Mosque is on the increase during the said period. This study can be applied in other cities because knowing the locations of traffic crash hotspots can provide us with valuable insights into the causes of accidents and this knowledge helps decision-makers to better assess the risk associated with accidents.

Change in Adaptability of Residential Architecture:Spatial Analysis on Traditional and Contemporary Houses of Bangladesh

This study focused on spatial analysis to identify the changes in adaptability over the last five decades.The features influencing adaptability were selected from the reference study.An appropriate method was used to analyse these features through spatial analysis.Six distinctive typologies of rural houses were selected from six regions.Unlike the traditional houses,the contemporary houses in the same area reflected a different character.Urban houses built since the early and mid-20th century were compared with contemporary houses.After analysing the openness,generality,flexibility,depth,typicality,construction technique,involvement of end-users,and the feedback from the inhabitants,the study identified a significant decrease in contemporary houses'adaptability.Spatial analysis was used to quantify the different features and comparison between traditional and contemporary houses.Though the adaptability had been reduced over time,the latest houses started to achieve better flexibility in some features due to government policy and implementation of statutory building regulations.Further recommendations were provided to enhance the residential architecture's adaptability in future.The study samples were selected from different regions of Bangladesh.Still,the result and policy recommendations can be helpful for other countries,especially with high population density and a developing economy.

Urban Settlement Spatial Analysis in Permanent Preservation Area of M’boicy Watershed River, Foz do Iguaçu City in Brazil

Irregular urban settlement increases environmental impacts, especially when these occupations occur in fragile location, as the environmental preservation areas. In these areas, also defined as Permanent Preservation Area (PPA), the presence of watersheds is common, which is the factor that increases the need of protecting them from anthropic actions. Those actions deteriorate the environment and mainly the watercourses. This research objective is to identify and estimate the environmental risks of M’Boyci watershed River PPA occupied areas by urban population. The risk analysis approach, at this PPA in Foz do Iguacu City in Brazil, is able to support public interventions in order to reestablish the PPA natural conditions. To reach this goal, it was necessary to use cartographical representation images, generated from digital orthophotos analyzed through free geographical information systems. The overlap and the contrast of geographical data related to preservation in urban areas show that urban occupation reaches almost 40% of the permanent preservation area. Complementarily, it is evidenced that the development of a risk map identifies PPA areas characterized by a greater concentration of irregular settlement, contributing to the planning process of residents, relocation actions and recovery of degraded areas.

Methodological Guidelines for the Use of Geoprocessing Tools:Spatial Analysis Operations—Kernel,Buffer and the Remote Sensing Image Classification

The so-called geotechnology has been used in recent years in the planning, supervision and monitoring of various human activities, both locally and regionally, nationally and internationally, either it in the rural environment, as in urban áreas. This study, based on references and activities in the Geographic Information Analysis Laboratory of the Federal University of Pará (LAIG/UFPA), selects and presents the application of three tools for spatial analysis available in the Terraview [1] and Arcgis softwares, with the main objective being to demonstrate how they can be applied in geographical studies, starting with the spatial information gathered by remote or Field sensors, assisting the activities of researchers who stick to working with the planning and management of natural and human resources. Geotechnologies are important tools in the analysis of geographic space and its use tends to be increased with the advancement of new software and hardware collection, manipulation and generation of new specialized information. So we will do in this manuscript a brief discussion of three spatial analysis tools that can be used in rural areas with agricultural potential.

Environmental Analysis Using Integrated GIS and Spatial Configurations in Israel

The scope for environmental analysis constitutes a critical factor in recent times, yet demanding importance due to the concerns of environmental sustainability. The study aims at analysing the prospects of implementing an integrated GIS and spatial configuration for environment analysis in Israel. The study adopts an empirical study design to consider the multi-dimensional utilisation of an integrated GIS and spatial configuration for environment analysis. The study considers the materials and methods of the GIS system modelling as well, consisting of satellite imagery, GPS-based location identification, Esri ArcGIS, CyberGIS, and BIM integration to present a comprehensive system for the environmental analysis of Israel. The results of the study indicate that the threats of natural disasters and climate change can be identified based on the synergy of spatial data within an integrated GIS modelling. In many cases, it is also used in collaboration with a BIM to ensure that planning and decision-making processes are sustainable, economically beneficial and environmentally considered. Thus, it is concluded that environmental analysis through the projection of visually represented satellite imagery within an integrated GIS with spatial configurations in Israel can minimise the conflicts between the infrastructural designs, human activities, and environmental sustainability.

Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

Small-scale spatial analysis of intermediate and definitive hosts of Angiostrongylus cantonensis

Background:Angiostrongyliasis is a food-borne parasitic zoonosis.Human infection is caused by infection with the third-stage larvae of Angiostrongylus cantonensis.The life cycle of A.cantonensis involves rodents as definitive hosts and molluscs as intermediate hosts.This study aims to investigate on the infection status and characteristics of spatial distribution of these hosts,which are key components in the strategy for the prevention and control of angiostrongyliasis.Methods:Three villages from Nanao Island,Guangdong Province,China,were chosen as study area by stratified random sampling.The density and natural infection of Pomacea canaliculata and various rat species were surveyed every three months from December 2015 to September 2016,with spatial correlations of the positive P.canaliculata and the infection rates analysed by ArcGIS,scan statistics,ordinary least squares(OLS)and geographically weighted regression(GWR)models.Results:A total of 2192 P.canaliculata specimens were collected from the field,of which 1190 were randomly chosen to be examined for third-stage larvae of A.cantonensis.Seventy-two Angiostrongylus-infected snails were found,which represents a larval infection rate of 6.1%(72/1190).In total,110 rats including 85 Rattus norvegicus,10 R.flavipectus,one R.losea and 14 Suncus murinus were captured,and 32 individuals were positive(for adult worms),representing an infection rate of 29.1%of the definitive hosts(32/110).Worms were only found in R.norvegicus and R.flavipectus,representing a prevalence of 36.5%(31/85)and 10%(1/10),respectively in these species,but none in R.losea and S.murinus,despite testing as many as 32 of the latter species.Statistically,spatial correlation and spatial clusters in the spatial distribution of positive P.canaliculata and positive rats existed.Most of the spatial variability of the host infection rates came from spatial autocorrelation.Nine spatial clusters with respect to positive P.canaliculata were identified,but only two correlated to infection rates.The results show that corrected Akaike information criterion,R2,R2 adjusted andσ2 in the GWR model were superior to those in the OLS model.Conclusions:P.canaliculata and rats were widely distributed in Nanao Island and positive infection has also been found in the hosts,demonstrating that there was a risk of angiostrongyliasis in this region of China.The distribution of positive P.canaliculata and rats exhibited spatial correlation,and the GWR model had advantage over the OLS model in the spatial analysis of hosts of A.cantonensis.

Spatial inequality, characteristics of internal migration, and pulmonary tuberculosis in China, 2011–2017: a spatial analysis

Background: Human migration facilitate the spread of tuberculosis(TB).Migrants face an increased risk of TB infection.In this study,we aim to explore the spatial inequity of sputum smear-positive pulmonary TB(SS+PTB)in China;and the spatial heterogeneity between SS+PTB and internal migration.Methods:: Notified SS+PTB cases in 31 provinces in China's Mainland were obtained from the national web-based PTB surveillance system database.Internal migrant data were extracted from the report on China’s migrant population development.Spatial autocorrelations were explored using the global Moran’s statistic and local indicators of spatial association.The spatial variation in temporal trends was performed using Kulldorff’s scan statistic.Fixed effect and spatial autoregressive models were used to explore the spatial inequity between SS+PTB and internal migration.Results: A total of 2380233 SS+PTB cases were reported in China between 2011 and 2017,of which,1716382(72.11%)were male and 663851(27.89%)were female.Over 70%of internal migrants were from rural households and had lower income and less education.The spatial variation in temporal trend results showed that there was an 9.9%average annual decrease in the notification rate of SS+PTB from 2011 to 2017;and spatial clustering of SS+PTB cases was mainly located in western and southern China.The spatial autocorrelation results revealed spatial clustering of internal migration each year(2011–2017),and the clusters were stable within most provinces.Internal emigration,urban-to-rural migration and GDP per capita were significantly associated with SS+PTB,further,internal emigration could explain more variation in SS+PTB in the eastern region in mainland.However,internal immigration and rural-to-urban migration were not significantly associated with SS+PTB across China.Conclusions: Our study found the spatial inequity between SS+PTB and internal migration.Internal emigration,urban-to-rural migration and GDP per capita were statistically associated with SS+PTB;the negative association was identified between internal emigration,urban-to-rural migration and SS+PTB.Further,we found those migrants with lower income and less education,and most of them were from rural households.These findings can help stakeholders to implement effective PTB control strategies for areas at high risk of PTB and those with high rates of internal migration.

The association between internal migration and pulmonary tuberculosis in China,2005–2015:a spatial analysis

Background:Internal migration places individuals at high risk of contracting tuberculosis(TB).However,there is a scarcity of national-level spatial analyses regarding the association between TB and internal migration in China.In our research,we aimed to explore the spatial variation in cases of sputum smear-positive pulmonary TB(SS+PTB)in China;and the associations between SS+PTB,internal migration,socioeconomic factors,and demographic factors in the country between 2005 and 2015.Methods:Reported cases of SS+PTB were obtained from the national PTB surveillance system database;cases were obtained at the provincial level.Internal migration data were extracted from the national population sampling survey and the census.Spatial autocorrelations were explored using the global Moran’s statistic and local indicators of spatial association.The spatial temporal analysis was performed using Kulldorff’s scan statistic.Fixed effects regression was used to explore the association between SS+PTB and internal migration.Results:A total of 4708563 SS+PTB cases were reported in China between 2005 and 2015,of which 3376011(71.7%)were male and 1332552(28.3%)were female.There was a trend towards decreasing rates of SS+PTB notifications between 2005 and 2015.The result of global spatial autocorrelation indicated that there were significant spatial correlations between SS+PTB rate and internal migration each year(2005–2015).Spatial clustering of SS+PTB cases was mainly located in central and southern China and overlapped with the clusters of emigration.The proportions of emigrants and immigrants were significantly associated with SS+PTB.Per capita GDP and education level were negatively associated with SS+PTB.The internal migration flow maps indicated that migrants preferred neighboring provinces,with most migrating for work or business.Conclusions:This study found a significant spatial autocorrelation between SS+PTB and internal migration.Both emigration and immigration were statistically associated with SS+PTB,and the association with emigration was stronger than that for immigration.Further,we found that SS+PTB clusters overlapped with emigration clusters,and the internal migration flow maps suggested that migrants from SS+PTB clusters may influence the TB epidemic characteristics of neighboring provinces.These findings can help stakeholders to implement effective PTB control strategies for areas at high risk of PTB and those with high rates of internal migrants.

Resampling in neural networks with application to spatial analysis

In developing Artificial Neural Networks(ANNs),the available dataset is split into three categories:training,validation and testing.However,an important problem arises:How to trust the predic-tion provided by a particular ANN?Due to the randomness related to the network itself(architecture,initialization and learning procedure),there is usually no best choice.Considering this issue,we provide a framework,which captures the randomness related to the network itself.The idea is to perform several training and test trials based on the Jackknife resampling method.Jackknife consists of iteratively deleting a single observation each time from the sample and recomputing the ANN on the rest of the sample data.Consequently,interval prediction is available instead of point prediction.The proposed method was applied and tested using pH,Ca and P data obtained by analyzing 118 georeferenced soil points.The results,based on the dataset size simulation,showed that 60%reduction in available dataset offers compatible accuracy in relation to full dataset,and therefore a higher cost of sampling in the field would not be necessary.The re-sampling method spatially characterizes the points of greater or lesser accuracy and uncertainty.The re-sampling method increased the success rate by using interval prediction instead of using the mean as the most probable value.Although we restrict it to the regression neural network model,the resampling method proposed can also be extended to other modern statistical tools,such as Kriging,Least Squares Collocation(LSC),Convolutional Neural Network(CNN),and so on.

Identifying disaster-related tweets and their semantic,spatial and temporal context using deep learning,natural language processing and spatial analysis:a case study of Hurricane Irma

We introduce an analytical framework for analyzing tweets to(1)identify and categorize fine-grained details about a disaster such as affected individuals,damaged infrastructure and disrupted services;(2)distinguish impact areas and time periods,and relative prominence of each category of disaster-related information across space and time.We first identify disaster-related tweets by generating a human-labeled training dataset and experimenting a series of deep learning and machine learning methods for a binary classification of disasterrelatedness.We employ LSTM(Long Short-Term Memory)networks for the classification task because LSTM networks outperform other methods by considering the whole text structure using long-term semantic word and feature dependencies.Second,we employ an unsupervised multi-label classification of tweets using Latent Dirichlet Allocation(LDA),and identify latent categories of tweets such as affected individuals and disrupted services.Third,we employ spatiallyadaptive kernel smoothing and density-based spatial clustering to identify the relative prominence and impact areas for each information category,respectively.Using Hurricane Irma as a case study,we analyze over 500 million keyword-based and geo-located collection of tweets before,during and after the disaster.Our results highlight potential areas with high density of affected individuals and infrastructure damage throughout the temporal progression of the disaster.