Football player dominant region determined by a novel model based on instantaneous kinematics variables

Dominant regions are defined as regions of the pitch where a player can reach before any other and are commonly determined without considering the free-spaces in the pitch. We presented an approach to football players’ dominant regions analysis, based on movement models created from players’ positions, displacement, velocity, and acceleration vectors. 109 Brazilian male professional football players were analysed during official matches, computing over 15 million positional data obtained by video-based tracking system. Movement models were created based on players’ instantaneous vectorial kinematics variables, then probabilities models and dominant regions were determined. Accuracy in determining dominant regions by the proposed model was tested for different time-lag windows. We calculated the areas of dominant, free-spaces, and Voronoi regions. Mean correct predictions of dominant region were 96.56%, 88.64%, and 72.31% for one, two, and three seconds, respectively. Dominant regions areas were lower than the ones computed by Voronoi, with median values of 73 and 171 m2, respectively. A median value of 5537 m2 was presented for free-space regions, representing a large part of the pitch. The proposed movement model proved to be more realistic, representing the match dynamics and can be a useful method to evaluate the players’ tactical behaviours during matches.

Contextual movement models based on normalizing flows

Movement models predict positions of players (or objects in general) over time and are thus key to analyzing spatiotemporal data as it is often used in sports analytics. Existing movement models are either designed from physical principles or are entirely data-driven. However, the former suffers from oversimplifications to achieve feasible and interpretable models, while the latter relies on computationally costly, from a current point of view, nonparametric density estimations and require maintaining multiple estimators, each responsible for different types of movements (e.g., such as different velocities). In this paper, we propose a unified contextual probabilistic movement model based on normalizing flows. Our approach learns the desired densities by directly optimizing the likelihood and maintains only a single contextual model that can be conditioned on auxiliary variables. Training is simultaneously performed on all observed types of movements, resulting in an effective and efficient movement model. We empirically evaluate our approach on spatiotemporal data from professional soccer. Our findings show that our approach outperforms the state of the art while being orders of magnitude more efficient with respect to computation time and memory requirements.

Hierarchical, Memory-Based Movement Models for Translocated Elk (Cervus canadensis)

The use of spatial memory is well-documented in many animal species and has been shown to be critical for the emergence of spatial learning. Adaptive behaviors based on learning can emerge thanks to an interdependence between the acquisition of information over time and movement decisions. The study of how spatio-ecological knowledge is constructed throughout the life of an individual has not been carried out in a quantitative and comprehensive way, hindered by the lack of knowledge of the information an animal already has of its environment at the time monitoring begins. Identifying how animals use memory to make beneficial decisions is fundamental to developing a general theory of animal movement and space use. Here we propose several mobility models based on memory and perform hierarchical Bayesian inference on 11-month trajectories of 21 elk after they were released in a completely new environment. Almost all the observed animals exhibited preferential returns to previously visited patches, such that memory and random exploration phases occurred. Memory decay was mild or negligible over the study period. The fact that individual elk rapidly become used to a relatively small number of patches was consistent with the hypothesis that they seek places with predictable resources and reduced mortality risks such as predation.

Hierarchical, Memory-based Movement Models for Translocated Elk (Cervus canadensis)

The use of spatial memory is well documented in many animal species and has been shown to be critical for the emergence of spatial learning. Adaptive behaviors based on learning can emerge thanks to an interdependence between the acquisition of information over time and movement decisions. The study of how spatio-ecological knowledge is constructed throughout the life of an individual has not been carried out in a quantitative and comprehensive way, hindered by the lack of knowledge of the information an animal already has of its environment at the time monitoring begins. Identifying how animals use memory to make beneficial decisions is fundamental to developing a general theory of animal movement and space use. Here we propose several mobility models based on memory and perform hierarchical Bayesian inference on 11-month trajectories of 21 elk after they were released in a completely new environment. Almost all the observed animals exhibited preferential returns to previously visited patches, such that memory and random exploration phases occurred. Memory decay was mild or negligible over the study period. The fact that individual elk rapidly become used to a relatively small number of patches was consistent with the hypothesis that they seek places with predictable resources and reduced mortality risks such as predation.

Temporal Non-stationarity of Path-Selection Movement Models and Connectivity: An Example of African Elephants in Kruger National Park

Effective conservation and land management require robust understanding of how landscape features spatially and temporally affect population distribution, abundance and connectivity. This is especially important for keystone species known to shape ecosystems, such as the African elephant (Loxodonta africana). This work investigates monthly patterns of elephant movement and connectivity in Kruger National Park (KNP; South Africa), and their temporal relationship with landscape features over a 12-month period associated with the occurrence of a severe drought. Based on elephant locations from GPS collars with a short acquisition interval, we explored the monthly patterns of spatial-autocorrelation of elephant movement using Mantel correlograms, and we developed scale-optimized monthly path-selection movement and resistant kernel connectivity models. Our results showed high variability in patterns of autocorrelation in elephant movements across individuals and months, with a preponderance of directional movement, which we believe is related to drought induced range shifts. We also found high non-stationarity of monthly movement and connectivity models; most models exhibited qualitative similarity in the general nature of the predicted ecological relationships, but large quantitative differences in predicted landscape resistance and connectivity across the year. This suggests high variation in space-utilization and temporal shifts of core habitat areas for elephants in KNP. Even during extreme drought, rainfall itself was not a strong driver of elephant movement; elephant movements, instead, were strongly driven by selection for green vegetation and areas near waterholes and small rivers. Our findings highlight a potentially serious problem in using movement models from a particular temporal snapshot to infer general landscape effects on movement. Conservation and management strategies focusing only on certain areas identified by temporarily idiosyncratic models might not be appropriate or efficient as a guide for allocating scarce resources for management or for understanding general ecological relationships.

Understanding Radicalists and Fundamentalist Islamic Groups in Indonesia: Ideology and Model of Movement

Since the Reformation Era, various Islamic organizations or groups have emerged in Indonesia with different ideologies and movement models, such as Hizbut Tahrir Indonesia (HTI), the Indonesian Mujahidin Council (MMI), the Indonesian Muslim Brotherhood, the Islamic Defenders Front (FPI), and Jemaah Islamiyah. Most Indonesian Muslims do not warmly welcome their presence as the spirit of their struggle is not appropriate with the character and culture of the Indonesian people. They often use violence and even terror in struggling for Islam with the reason of amr ma’ruf nahi munkar (enjoining good and forbidding evil). So it is not surprising that the government recently dissolved the HTI and FPI. By using a qualitative approach, this study aims to uncover their ideology and movement model and the ideologies that influence them. From the data obtained from various related literature such as textbooks, journals, and the web, this study finds that these Islamic groups are ideologically influenced by trans-national Islamic groups with fundamentalism and even radicalism characters. They disagree with the Indonesian government system, which is considered secular and incompatible with Islamic values. They often use violence and terror to enforce Islamic shari'ah and even change the NKRI system with the system of Khilafat Islamiyah (Islamic caliphate). Keywords: Radicalist, fundamentalist, Islamic caliphate, shari'ah. Abstrak: Sejak Era Reformasi, berbagai ormas atau kelompok Islam bermunculan di Indonesia dengan ideologi dan model gerakan yang berbeda, seperti Hizbut Tahrir Indonesia (HTI), Majelis Mujahidin Indonesia (MMI), Ikhwanul Muslimin Indonesia, Front Pembela Islam (FPI), dan Jemaah Islamiyah. Sebagian besar umat Islam Indonesia tidak menyambut baik kehadiran mereka karena semangat perjuangan mereka tidak sesuai dengan karakter dan budaya masyarakat Indonesia. Mereka sering menggunakan kekerasan bahkan teror dalam memperjuangkan Islam dengan dalih amr ma'ruf nahi munkar. Maka tidak heran jika belakangan ini organisasi tersebut, khususnya HTI dan FPI, dibubarkan oleh pemerintah. Dengan menggunakan pendekatan kualitatif, penelitian ini bertujuan untuk mengungkap ideologi dan model gerakan mereka serta ideologi yang mempengaruhinya. Dari data yang diperoleh dari berbagai literatur terkait seperti buku teks, jurnal, dan web, penelitian ini menemukan bahwa kelompok-kelompok Islam tersebut secara ideologis dipengaruhi oleh kelompok-kelompok Islam transnasional dengan karakter fundamentalisme bahkan radikalisme. Mereka tidak setuju dengan sistem pemerintahan Indonesia yang dianggap sekuler dan bertentangan dengan nilai-nilai Islam. Mereka kerap menggunakan kekerasan dan teror untuk menegakkan syari'at Islam bahkan mengubah bentuk NKRI (Negara Kesatuan Republik Indonesia) dengan sistem Khilafah Islamiyah. Kata kunci: Radikalisme, fundamentalisme, khilafah Islamiyah, syari'at Islam.

Ensemble model for estimating continental-scale patterns of human movement: a case study of Australia

Understanding human movement patterns at local, national and international scales is critical in a range of fields, including transportation, logistics and epidemiology. Data on human movement is increasingly available, and when combined with statistical models, enables predictions of movement patterns across broad regions. Movement characteristics, however, strongly depend on the scale and type of movement captured for a given study. The models that have so far been proposed for human movement are best suited to specific spatial scales and types of movement. Selecting both the scale of data collection, and the appropriate model for the data remains a key challenge in predicting human movements. We used two different data sources on human movement in Australia, at different spatial scales, to train a range of statistical movement models and evaluate their ability to predict movement patterns for each data type and scale. Whilst the five commonly-used movement models we evaluated varied markedly between datasets in their predictive ability, we show that an ensemble modelling approach that combines the predictions of these models consistently outperformed all individual models against hold-out data.