Explaining human mobility predictions through a pattern matching algorithm

Understanding what impacts the predictability of human movement is a key element for the further improvement of mobility prediction models. Up to this day, such analyses have been conducted using the upper bound of predictability of human mobility. However, later works indicated discrepancies between the upper bound of predictability and accuracy of actual predictions suggesting that the predictability estimation is not accurate. In this work, we confirm these discrepancies and, instead of predictability measure, we focus on explaining what impacts the actual accuracy of human mobility predictions. We show that the accuracy of predictions is dependent on the similarity of transitions observed in the training and test sets derived from the mobility data. We propose and evaluate five pattern matching based-measures, which allow us to quickly estimate the potential prediction accuracy of human mobility. As a result, we find that our metrics can explain up to 90% of its variability. We also find that measures that were proved to explain the variability of predictability measure, fail to explain the variability of predictions accuracy. This suggests that predictability measure and accuracy of predictions should not be compared. Our metrics can be used to quickly assess how predictable the data will be for prediction algorithms. We share developed metrics as a part of HuMobi, the open-source Python library

Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models

Water demand forecasting is a crucial task in the efficient management of the water supply system. This paper compares classical and adapted machine learning algorithms used for water usage predictions including ARIMA, support vector regression, random forests and extremely randomized trees. These models were enriched with human mobility data to improve the predictive power of water demand forecasting. Furthermore, a framework for processing mobility data into time-series correlated with water usage data is proposed. This study uses 51 days of water consumption readings and over 7 million geolocated mobility records from urban areas. Results show that using human mobility data improves water demand prediction. The best forecasting algorithm employing a random forest method achieved 90.4% accuracy (measured by the mean absolute percentage error) and is better by 1% than the same algorithm using only water data, while classic ARIMA approach achieved 90.0%. The Blind (copying) prediction achieved 85.1% of accuracy

Urban Hourly Water Demand Prediction Using Human Mobility Data

The efficient management of a water supply system requires precise water demand forecasts as inputs. This paper compares existing prediction methods and improves their performance by integrating human-related factors with water consumption in an urban area. Furthermore, a framework for processing and transforming mobility data into time-series is presented. Results show that using human mobility data improves forecasting accuracy reaching 87.6%

The impact of human mobility data scales and processing on movement predictability

Predictability of human movement is a theoretical upper bound for the accuracy of movement prediction models, which serves as a reference value showing how regular a dataset is and to what extent mobility can be predicted. Over the years, the predictability of various human mobility datasets was found to vary when estimated for differently processed datasets. Although attempts at the explanation of this variability have been made, the extent of these experiments was limited. In this study, we use high-precision movement trajectories of individuals to analyse how the way we represent the movement impacts its predictability and thus, the outcomes of analyses made on these data. We adopt a number of methods used in the last 11 years of research on human mobility and apply them to a wide range of spatio-temporal data scales, thoroughly analysing changes in predictability and produced data. We find that spatio-temporal resolution and data processing methods have a large impact on the predictability as well as geometrical and numerical properties of human mobility data, and we present their nonlinear dependencies

Identification of genetic variants associated with Huntington's disease progression: a genome-wide association study

Background Huntington's disease is caused by a CAG repeat expansion in the huntingtin gene, HTT. Age at onset has been used as a quantitative phenotype in genetic analysis looking for Huntington's disease modifiers, but is hard to define and not always available. Therefore, we aimed to generate a novel measure of disease progression and to identify genetic markers associated with this progression measure. Methods We generated a progression score on the basis of principal component analysis of prospectively acquired longitudinal changes in motor, cognitive, and imaging measures in the 218 indivduals in the TRACK-HD cohort of Huntington's disease gene mutation carriers (data collected 2008–11). We generated a parallel progression score using data from 1773 previously genotyped participants from the European Huntington's Disease Network REGISTRY study of Huntington's disease mutation carriers (data collected 2003–13). We did a genome-wide association analyses in terms of progression for 216 TRACK-HD participants and 1773 REGISTRY participants, then a meta-analysis of these results was undertaken. Findings Longitudinal motor, cognitive, and imaging scores were correlated with each other in TRACK-HD participants, justifying use of a single, cross-domain measure of disease progression in both studies. The TRACK-HD and REGISTRY progression measures were correlated with each other (r=0·674), and with age at onset (TRACK-HD, r=0·315; REGISTRY, r=0·234). The meta-analysis of progression in TRACK-HD and REGISTRY gave a genome-wide significant signal (p=1·12 × 10−10) on chromosome 5 spanning three genes: MSH3, DHFR, and MTRNR2L2. The genes in this locus were associated with progression in TRACK-HD (MSH3 p=2·94 × 10−8 DHFR p=8·37 × 10−7 MTRNR2L2 p=2·15 × 10−9) and to a lesser extent in REGISTRY (MSH3 p=9·36 × 10−4 DHFR p=8·45 × 10−4 MTRNR2L2 p=1·20 × 10−3). The lead single nucleotide polymorphism (SNP) in TRACK-HD (rs557874766) was genome-wide significant in the meta-analysis (p=1·58 × 10−8), and encodes an aminoacid change (Pro67Ala) in MSH3. In TRACK-HD, each copy of the minor allele at this SNP was associated with a 0·4 units per year (95% CI 0·16–0·66) reduction in the rate of change of the Unified Huntington's Disease Rating Scale (UHDRS) Total Motor Score, and a reduction of 0·12 units per year (95% CI 0·06–0·18) in the rate of change of UHDRS Total Functional Capacity score. These associations remained significant after adjusting for age of onset. Interpretation The multidomain progression measure in TRACK-HD was associated with a functional variant that was genome-wide significant in our meta-analysis. The association in only 216 participants implies that the progression measure is a sensitive reflection of disease burden, that the effect size at this locus is large, or both. Knockout of Msh3 reduces somatic expansion in Huntington's disease mouse models, suggesting this mechanism as an area for future therapeutic investigation

Towards Anonymous Mobility Data Through the Modelling of Spatiotemporal Circadian Rhythms

(VLID)452615

Kalman filter for integration of GNSS and InSAR data applied for monitoring of mining deformations

[EN] Ground deformation monitoring can be performed using different measurement methods, e.g., leveling, gravimetry, photogrammetry, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR), and others. In the presented study we introduced an original methodology of integration of the Differential Satellite Interferometric SAR (DInSAR) and Global Navigation Satellite Systems (GNSS) data using Kalman filter. However, technical problems related to invalid GNSS receivers functioning and noisy DInSAR results have a great impact on calculations provided only in the forward Kalman filter mode. To reduce the impact of unexpected discontinuity of observations, a backward Kalman filter was also introduced. The applied algorithm was tested in the Upper Silesian coal mining region in Poland. The paper presents the methodology of DInSAR and GNSS integration appropriate for small-scale and non-linear motions. The verification procedure of the obtained results was performed using an external data source – GNSS campaign measurements. The overall RMS errors reached 18, 16, and 42 mm for the Kalman forward, and 19, 17, and 44 mm for the Kalman backward approaches in North, East, and Up directions, respectively.This study was started in 2016 in the frames of the EPOS-PL project POIR.04.02.00-14-A003/16, and continued in 2021-2022 within the EPOS-PL+ project POIR.04.02.00-00-C005/19-00, that were funded by the Operational Program Smart Growth 2014–2020, Priority IV: Increasing research potential, Action 4.2: Development of modern research infrastructure in the science sector. The presented investigation was accomplished as part of a scientific internship at Delft University of Technology (TU Delft), Netherlands, conducted within the GATHERS project, funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 857612. The authors would like to express their gratitude to Freek van Leijen and Hans van der Marel from TU Delft for the valuable guidance and discussions.Tondaś, D.; Rohm, W.; Ilieva, M.; Kapłon, J. (2023). Kalman filter for integration of GNSS and InSAR data applied for monitoring of mining deformations. Editorial Universitat Politècnica de València. 605-612. http://hdl.handle.net/10251/19204360561

Residuals of Tropospheric Delays from GNSS Data and Ray-Tracing as a Potential Indicator of Rain and Clouds

The Global Navigation Satellite System (GNSS) is commonly recognized by its all-weather capability. However, observations depend on atmospheric conditions which requires the induced tropospheric delay to be estimated as an unknown parameter. In the following study, we investigate the impact of intense weather events on GNSS estimates. GNSS slant total delays (STD) in Precise Point Positioning technique (PPP) strategy were calculated for stations in southwest Poland in a 56 days period covering several heavy precipitation cases. The corresponding delays retrieved from Weather Research and Forecasting (WRF) model by a ray-tracing technique considered only gaseous parts of the atmosphere. The discrepancies are correlated with rain rates and cloud type products from remote sensing platforms. Positive correlation is found as well as GNSS estimates tend to be systematically larger than modeled delays. Mean differences mapped to the zenith direction are showed to vary between 10 mm and 30 mm. The magnitude of discrepancies follows the intensity of phenomena, especially for severe weather events. Results suggest that effects induced by commonly neglected liquid and solid water terms in the troposphere modeling should be considered in precise GNSS applications for the atmosphere monitoring. The state-of-art functional model applied in GNSS processing strategies shows certain deficits. Estimated tropospheric delays with gradients and post-fit residuals could be replaced by a loosely constrained solution without loss of quality

Cloud Detection from Radio Occultation Measurements in Tropical Cyclones

Tropical cyclones (TC) are one of the main producers of clouds in the tropics and subtropics. Hence, most of the clouds in TCs are dense, with large water and ice content, and provide conditions conducive to investigate clouds’ impact on Radio Occultation (RO) measurements. Although the RO technique is considered insensitive to clouds, recent studies show a refractivity positive bias in cloudy conditions. In this study, we analyzed the RO bending angle sensitivity to cloud content during tropical cyclone seasons between 2007 and 2010. Thermodynamic parameters were obtained from the ERA-Interim reanalysis, whereas the water and ice cloud contents were retrieved from the CloudSat profiles. Our experiments confirm the positive mean RO refractivity bias in cloudy conditions that reach up to more than 0.5% at the geometric height of around 7 km. A similar bias but larger and shifted up is visible in bending angle anomaly (1.6%). Our results reveal that the influence of clouds is significant and can exceed the RO bending angle standard deviation for 21 out of 50 (42%) investigated profiles. Mean clouds’ impact is detectable between 9.0 and 10.5 km, while, in the case of single events, clouds in most of the observations are significant between 8 and 14 km. Almost 15% of the detectable clouds reach 16 km height, while the influence of the clouds below 5 km is insignificant. For more than half of the significant cases, the detection range is less than 3 km but for one observation this range spreads to 7⁻8 km