Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Interactive Spatiotemporal Analysis of Oil Spills Using Comap in North Dakota

The aim of the study is to analyze the oil spill pattern from various types of incidents and contaminants to determine the extent that incident data can be used as a baseline to prevent hazardous material releases and improve response activities at a state level. This study addresses the importance of collecting and sharing oil spill incidents as well as analytics using the data. Temporal, spatial and spatiotemporal analysis techniques are employed for the oil-spill related environmental incidents observed in the state of North Dakota, United States of America, from 2000 to 2014, as a result of the oil boom. Specifically, spatiotemporal methods are used to examine how the patterns of environmental incidents in North Dakota, which vary with the time of day, the day, the month, and the season. Results indicate that there were critical spatial and time variations in the distribution of environmental incidents. Application of spatiotemporal interaction visualization techniques, called comap has the potential to help planners and decision makers formulate policy to mitigate the risks associated with environmental incidents, improve safety, and allocate resources

Landslides Hazard Mapping in Rwanda Using Bivariate Statistical Index Method

Landslides hazard mapping (LHM) is essential in delineating hazard prone areas and optimizing low cost mitigation measures. This study applied the Geographic Information System and statistical index method in LHM in Rwanda. Field surveys identified 336 points that were employed to construct a landslides inventory map. Ten landslides predicting factors were analyzed: normalized difference vegetation index, elevation, slope, aspects, lithology, soil texture, distance to rivers, distance to roads, rainfall, and land use. The factor variables were converted into categorized variables according to the percentile divisions of seed cells. Then, values of each factor’s class weight were calculated and summed to create landslides hazard map. The estimated hazard map was split into five hazard classes (very low, low, moderate, high, and very high). The results indicated that the northern, western, and southern provinces are largely exposed to landslides hazard. The major landslides hazard influencing factors are elevation, slope, rainfall, and poor land management. Overall, this LHM would help policy makers to recognize each area’s hazard extent, key triggering factors, and the required hazard mitigation measures. These measures include planting trees to enhance vegetation cover and reduce the runoff, and construction of buildings on low steep slope areas to reduce people’s hazard exposure; while agroforestry and bench terraces would reduce sediments that take out the exposed soil (erosion) and pollute water quality

Development of Geospatial Models for Multi-Criteria Decision Making in Traffic Environmental Impacts of Heavy Vehicle Freight Transportation

Heavy vehicle freight transportation is one of the primary contributors to the socio-economic development, but it has great influence on traffic environment. To comprehensively and more accurately quantify the impacts of heavy vehicles on road infrastructure performance, a series of geospatial models are developed for both geographically global and local assessment of the impacts. The outcomes are applied in flexible multi-criteria decision making for the industrial practice of road maintenance and management

Data-driven disaster management in a smart city

Disasters, both natural and man-made, are complex events that result in the loss of human life and/or the destruction of properties. The advances in Information Technology (IT) and Big Data Analysis represent an opportunity for the development of resilient environments, since from the application of Big Data (BD) technologies it is possible not only to extract patterns of occurrences of events, but also to predict them. The work carried out in this dissertation aims to apply the CRISP-DM methodology to conduct a descriptive and predictive analysis of the events that occurred in the city of Lisbon, with emphasis on the events that affected buildings. Through this research it was verified the existence of temporal and spatial patterns of occurrences with some events occurring in certain periods of the year, such as floods and collapses that are recorded more frequently in periods of high precipitation. The spatial analysis showed that the city center is the area most affected by the occurrences, and it is in these areas where the largest proportion of buildings with major repair needs are concentrated. Finally, machine learning models were applied to the data, and the Random Forest model obtained the best result with an accuracy of 58%. This research contributes to improve the resilience of the city since the analysis developed allowed to extract insights regarding the events and their occurrence patterns that will help the decision-making process.Os desastres, tanto naturais quanto as provocadas pelo homem, são eventos complexos que se traduzem em perdas de vidas e/ou destruição de propriedades. Os avanços na área de Tecnologias de Informação e Big Data Analysis representam uma oportunidade para o desenvolvimento de ambientes resilientes dado que, a partir da aplicação das tecnologias de Big Data (BD), é possível não só extrair padrões de ocorrências dos eventos, mas também fazer a previsão dos mesmos. O trabalho realizado nesta dissertação visa aplicar a metodologia CRISP-DM de forma a conduzir análises descritivas e preditivas sobre os eventos que ocorreram na cidade de Lisboa, com ênfase nos eventos que afetaram os edifícios. A investigação permitiu verificar a existência de padrões temporais e espaciais eventos a ocorrer em certos períodos do ano, como é o caso das cheias e inundações que são registados com maior frequência nos períodos de alta precipitação. A análise espacial permitiu verificar que a área do centro da cidade é a área mais afetada pelas ocorrências sendo nestas áreas onde se concentram a maior proporção de edifícios com grandes necessidades de reparação. Por fim, modelos de aprendizagem automática foram aplicados aos dados tendo o modelo Random Forest obtido o melhor resultado com accuracy de 58%. Esta pesquisa contribui para melhorar o aumento da resiliência da cidade pois, a análise desenvolvida permitiu extrair insights sobre os eventos e os seus padrões de ocorrência que irá ajudar os processos de tomada de decisão

Spatial and Temporal Analysis of Big Dataset on PM2.5 Air Pollution in Beijing, China, 2014 to 2018

Air particulate matter (PM2.5) pollution is a critical environment problem worldwide and also in Beijing, China. We gathered five-year PM2.5 contaminate concentrations from 2014 to 2018, from the Beijing Municipal Environmental Monitoring Center and China Air Quality Real-time Distribution Platform. This is a big dataset, and we collected with crawler technology from Python programming. After examining the quality of the recorded data, we determined to conduct the temporal and spatial analysis using 27 observation stations located in both urban and suburb area in the municipality of Beijing. The big dataset of five-year hourly PM2.5 concentrations was sorted to actionable datasets (Selected Datasets and Seasonal Average Selected Datasets) with the help of Python programming. Linear Regression based Fundamental Data Analysis was conducted as the first part of temporal analysis in R studio to gather the temporal patterns of five-year seasonal PM2.5 contaminant concentrations on each observation sites. As the second part of temporal analysis, the Principal Component Analysis (PCA) was conducted in MATLAB to gather the patterns of variations of entire five-year PM2.5 contaminant concentration on each of the sites. Geographic Information System (GIS) was utilized to study the spatial pattern of air pollution distribution from the selected 27 observation sites during selected time periods. The results of this research are, 1) PM2.5 pollutions in winter are the most severe or the highest in each of the natural years. 2) PM2.5 pollution concentrations in Beijing were gradually decrease during 2014 to 2018. 3) In terms of a five-year time perspective, the improvements of air quality and reduction of PM2.5 contaminant appeared in all the seasons based on Fundamental Data Analysis. 4) PM2.5 contaminant concentrations in summer are significantly less than other seasons. 5) The least PM2.5 pollutant influenced area is north and northwest regions in Beijing, and the most PM2.5 pollutant influenced area is south and southeast areas in Beijing. 6) Vehicle concentration and traffic congestion is not the significant impact factor of PM2.5 pollutions in Beijing. 7) Heating supply of buildings and houses generated great contributions to the PM2.5 contaminant concentration in Beijing. While, in the background of rigorous emission reduction policy and management operations by the municipal government, contribution of heating supplies is gradually decreasing. 8) Human activities have limited contributions to the PM2.5 contaminants in Beijing. Meanwhile, type and quantity of fossil fuel energy consumptions might contribute large amount of air pollutions

The Role of big Data in Regional Low-Carbon management: A Case in China

Low-carbon management is an important area of urban study and city management, and it is a critical element of the modern city system. Even though the importance of low-carbon management has been recognized, low-carbon problems are still salient and even worse than ever before in some developing countries, like China. Nowadays, big data techniques may change this dilemma in the regulatory process and innovation of social governance. However, few studies have been conducted to examine the role of big data in regional low-carbon management, especially in developing countries. In this study, by drawing on the experience of other countries and using “big data” methods, we have developed an approach of using a big data model to improve low-carbon management in Beijing (the capital of P.R. China), and we have proposed some policy suggestions

Modelling risk factors in urban residential fires in Helsinki

Asuinrakennuksissa syttyvät tulipalot aiheuttavat merkittäviä henkilö- ja omaisuusvahinkoja erityisesti kaupungeissa. Palojen esiintyvyydellä on todettu olevan voimakas yhteys alueiden ja alueiden asukkaiden piirteisiin, kuten sosioekonomiseen asemaan sekä kotitalouksien ja rakennusten ominaisuuksiin. Vaikuttavat tekijät ovat kuitenkin monimutkaisia ja usein toisiinsa kytkeytyneitä, mikä on vaikeuttanut tarkkojen ennusteiden tekemistä. Riskimallinnus ja paikkatietoanalyysit tarjoavat entistä tehokkaampia ja käytännöllisiä keinoja ilmiön tutkimiseen, erityisesti onnettomuuksien ennaltaehkäisyn ja varautumisen näkökulmasta. Tähän mennessä asuinrakennuspalojen alueelliseen esiintyvyyteen vaikuttavien riskitekijöiden tuntemus Helsingissä on ollut rajallista, mihin tällä tutkielmalla on pyritty tuomaan uutta empiiristä tietoa. Tässä tutkielmassa analysoitiin Helsingissä syttyneitä asuinrakennuspaloja vuosina 2014–2018 250 x 250 metrin ruututasolla. Tulipalojen alueellista riippuvuutta tutkittiin havainnoimalla tilastollisesti merkittäviä palojen keskittymiä. Lisäksi luotiin riskimalli, jolla pyrittiin tunnistamaan tulipalojen alueelliseen esiintyvyyteen vaikuttavia naapurustojen rakenteellisia, sosioekonomisia ja väestöllisiä piirteitä. Menetelminä käytettiin lineaarista regressiota ja spatiaalisen heterogeenisyyden huomioivaa Geographically Weighted Regression (GWR) -menetelmää. Tulokset osoittivat, että asuinrakennuspalot ovat alueellisesti klusteroituneita Helsingissä. Merkittävä suuri keskittymä löytyi kantakaupungin alueelta ja pienempiä keskittymiä Itä-Helsingistä. Tulosten perusteella naapuruston rakenteellisilla piirteillä, sosioekonomisella asemalla ja kotitalouksien ominaisuuksilla on vaikutusta asuinrakennuspalojen esiintyvyyden todennäköisyyteen sekä paloriskiä lisäävinä että vähentävinä tekijöinä. Naapurustotasolla tilastollisesti merkittäviä paloriskiä lisääviä selittäviä muuttujia olivat väestöntiheys, alhainen koulutustaso, työttömyys, asumisväljyys sekä omistusasuminen. Negatiivisesti paloriskiin vaikuttavia tekijöitä olivat asuinrakennusten tiheys, alueen rakennuskannan ikä, korkea koulutustaso sekä myös omistusasuminen. Yleisesti tutkimusalueella tämä kahdeksan muuttujaa selittivät noin puolet asuinrakennuspalojen vaihtelusta. Mallien välisessä vertailussa GWR:n selitysaste oli lineaarista regressiota parempi, ja se myös pystyi tunnistamaan merkittäviä paikallisia eroja selittävien muuttujien vaikutuksissa paloriskiin. Asuinrakennuspalojen riskiin vaikuttavien tekijöiden kokonaisvaltainen ymmärtäminen aluetasolla on tärkeää pelastustoimelle erityisesti valmiuden mitoittamisen ja resurssien tehokkaamman kohdentamisen kannalta. Jatkossa tulisikin kehittää tarkempia malleja, jotta saavutettaisiin entistä kattavampi kokonaiskuva paloriskistä ja siihen vaikuttavista tekijöistä. Erityisesti huomiota tulee kiinnittää tarkemman ja monipuolisemman aineiston ja menetelmien hyödyntämiseen, sekä myös tulipalojen ajallisen ulottuvuuden ja palojen seurauksien sisällyttämiseen mallinnuksessa.Fires in residential buildings can lead to significant personal injury and property damage, especially in cities. Fire incidence has been found to have a strong connection with the characteristics of neighbourhoods and their inhabitants, such as with socioeconomic status and the features of households and buildings. However, the influencing factors are complex and often interconnected, which has made it difficult to make accurate predictions. Risk modelling and spatial data analysis provide effective and practical means of studying the phenomenon, especially from the point of view of accident prevention and preparedness. To date, knowledge of the spatial risk factors affecting residential fire incidence is yet limited in Helsinki. Thus, this study has sought to bring new empirical evidence on the matter. This study analysed residential fires in Helsinki from 2014 to 2018 at a 250 x 250 m grid level. The spatial dependence of fires was investigated by observing statistically significant clusters of fires. In this study, a risk model was created that sought to identify the underlying structural, socioeconomic, and household characteristics of neighbourhoods that affect the likelihood of residential fire incidence. The methods used were linear regression and the Geographically Weighted Regression (GWR), which takes spatial heterogeneity into account. The results showed that residential fires are spatially clustered in Helsinki. A significant large concentration of fires was found in the inner-city area and smaller concentrations in eastern Helsinki. The results indicate that the structural features of the neighbourhoods, socioeconomic status, and household circumstances have an impact on the likelihood of residential fire incidence by both increasing and decreasing the risk of fire. At the neighbourhood level, statistically significant explanatory variables that increased fire risk were population density, low education, unemployment, occupancy rate of dwellings, and home ownership. A negative relationship with fire risk was found with residential building density, age of the buildings, high education, as well as home ownership. Overall, in the study area, these eight variables explained about half of the variance of residential fire incidence. In a comparison between the models, the explanatory power of the GWR was better than linear regression, and it was also able to identify significant local variations in the effects of explanatory variables on fire risk. A comprehensive understanding of the factors influencing residential fire risk at local levels is important for rescue services, especially in terms of planning response readiness and efficient allocation of resources. In the future, more precise models should be developed in order to achieve a more comprehensive understanding of fire risk and the factors affecting it. Particular attention should be paid to the use of more precise and diverse data and methods in modelling, as well as to the temporal dimension and the consequences of fires

Regional Transport and Its Association with Tuberculosis in the Shandong Province of China, 2009-2011

Human mobility has played a major role in the spread of infectious diseases such as tuberculosis (TB) through transportation; however, its pattern and mechanism have remained unclear. This study used transport networks as a proxy for human mobility to generate the spatial process of TB incidence. It examined the association between TB incidence and four types of transport networks at the provincial level: provincial roads, national roads, highways, and railways. Geographical information systems and geospatial analysis were used to examine the spatial distribution of 2217 smear-positive TB cases reported between 2009 and 2011 in the Shandong province. The study involved factors such as population density and elevation difference in conjunction with the types of transport networks to predict the disease occurrence in space. It identified spatial clusters of TB incidence linked not only with transport networks of the regions but also differentiated by elevation. Our research findings provide evidence of targeting populous regions with well-connected transport networks for effective surveillance and control of TB transmission in Shandong.postprin