Big Data and Machine Learning
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AgrImOnIA: The impact of agriculture on air quality and the COVID-19 pandemicAgrImOnIA is the project short name, being the acronym of Agricultural Impact On Italian Air. Also, Agrimonia eupatoria or Common agrimony is a plant and a flower spread in Alpine, Mediterranean and Central Europe, which is the positive symbol of this project. Lombardy has about 10 Million inhabitants and is the first Italian region for agriculture production. Bovines are about 1.5 Million and swine are about 4 Million. On average, there is a swine every 2.5 inhabitants and a bovine every 6.7 inhabitants. Considering that agriculture land is 69% of the region, there are about 245 swine and 92 bovines per rural km2. There is a large scientific consensus on the fact that livestock and fertilizers are responsible for about 95% of the ammonia emissions. After some reactions in the atmosphere, ammonia mainly turns to fine particulate matters known as PM2.5. Since PM2.5 is quite stable in the atmosphere, due to the limited air circulation in the Po Valley, the air quality is often affected by high concentrations of such pollutants. Adding the contributions of vehicle traffic and house heating, the plain of Lombardy is one of the most polluted areas in Europe. As a result, epidemiological studies found Lombardy to be the region with the highest PM2.5-related mortality rate: 164 deaths per 100 thousand inhabitants.Team:Year: 2021Funding: Cariplo Foundation (European Project)
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Statistical estimation of high-dimensional, spatial dependency structures using machine learning methodsThe project deals with an important, fundamental problem of spatial and spatiotemporal statistics – the full estimation of the underlying spatial dependence structure. For these models, the focus has so far been on processes showing a dependence in the conditional means. That is, the mean of a realization of the random process at a particular measurement point depends on the adjacent observations. This finding goes back to Tobler’s first law of Geography. The surrounding observations are defined on the basis of their geographical proximity, although this does not necessarily lead to a dependence of the observations of the random variables, i.e. the covariances. Various application examples will be used to demonstrate how the estimated parameters can be interpreted. Here, the focus will be on natural processes in the environment, such as air pollution or particulate matter. Using freely available sensor data, the results can be used, for example, to obtain local predictions of fine dust pollution in an urban area, which can then be used for optimal routing with respect to air quality.Led by: Prof. Dr. Philipp OttoYear: 2019Funding: Deutsche Forschungsgemeinschaft
Statistics
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Spatial and spatio-temporal GARCH modelsThe project is concerned with a subfield of spatial statistics, which deals in particular with the analysis of random processes in space. When analyzing such processes, it can often be found that observations that are located in spatial proximity to each other are similar. If, for example, land prices in a municipality are high, high prices can also be expected in the surrounding municipalities. In addition to this spatial dependency in the level of observations, a spatial dependency in the dispersion of observations as well as the conditional heteroskedasticity can also be determined. In the project, models for this will be developed and extended. The spatial models are analogous to the ARCH model of Robert F. Engle (1982) in time series analysis, who was awarded the Nobel Prize for Economics in 2003.Led by: Prof. Dr. Philipp OttoYear: 2019Funding: Deutsche Forschungsgemeinschaft