A Survey on Spatio-temporal Data Analytics Systems

Due to the surge of spatio-temporal data volume, the popularity of location-based services and applications, and the importance of extracted knowledge from spatio-temporal data to solve a wide range of real-world problems, a plethora of research and development work has been done in the area of spatial and spatio-temporal data analytics in the past decade. The main goal of existing works was to develop algorithms and technologies to capture, store, manage, analyze, and visualize spatial or spatio-temporal data. The researchers have contributed either by adding spatio-temporal support with existing systems, by developing a new system from scratch, or by implementing algorithms for processing spatio-temporal data. The existing ecosystem of spatial and spatio-temporal data analytics systems can be categorized into three groups, (1) spatial databases (SQL and NoSQL), (2) big spatial data processing infrastructures, and (3) programming languages and GIS software. Since existing surveys mostly investigated infrastructures for processing big spatial data, this survey has explored the whole ecosystem of spatial and spatio-temporal analytics. This survey also portrays the importance and future of spatial and spatio-temporal data analytics.

Interclutch variability in egg characteristics in two species of rail: Is maternal identity encoded in eggshell patterns?

Maternal signatures are present in the eggs of some birds, but quantifying interclutch variability within populations remains challenging. Maternal assignment of eggs with distinctive appearances could be used to non-invasively identify renesting females, including hens returning among years, as well as to identify cases of conspecific brood parasitism. We explored whether King Rail (Rallus elegans) eggs with shared maternity could be matched based on eggshell pattern. We used NaturePatternMatch (NPM) software to match egg images taken in the field in conjunction with spatial and temporal data on nests. Since we had only a small number of marked breeders, we analyzed similar clutch images from a study of Eurasian Common Moorhens (Gallinula chloropus chloropus) with color-banded breeders for which parentage at many nests had been verified genetically to validate the method. We ran 66 King Rail clutches (n = 338 eggs) and 58 Common Moorhen clutches (n = 364 eggs) through NPM. We performed non-metric multidimensional scaling and permutational analysis of variance using the best egg match output from NPM. We also explored whether eggs could be grouped by clutch using a combination of egg dimensions and pattern data derived from NPM using linear discriminant analyses. We then scrutinized specific matches returned by NPM for King Rail eggs to determine whether multiple matches between the same clutches might reveal maternity among nests and inform our understanding of female laying behavior. To do this, we ran separate NPM analyses for clutches photographed over several years from two spatially distant parts of the site. With these narrower datasets, we were able to identify four instances where hens likely returned to breed among years, four likely cases of conspecific brood parasitism, and a within-season re-nesting attempt. Thus, the matching output was helpful in identifying congruent egg patterns among clutches when used in conjunction with spatial and temporal data, revealing previously unrecognized site fidelity, within-season movements, and reproductive interference by breeding females. Egg pattern data in combination with nest mapping can be used to inform our understanding of female reproductive effort, success, and longevity in King Rails. These methods may also be applied to other secretive birds and species of conservation concern.

Prediction of Air Pollution in Smart Cities Using Machine Learning Techniques

Air-pollution is one of the main threats for developed societies. According to the World Health Organization (WHO), pollution is the main cause of deaths among children aged under five years. Smart cities are called to play a decisive role to increase such pollution in real-time. The increase in air pollution due to fossil fuel consumption as well as its ill effects on the climate has made air pollution forecasting an important research area in today’s times. Deployment of the Internet of things (IoT) based sensors has considerably changed the dynamics of predicting air quality. prediction of spatio-temporal data has been one of the major challenges in creating a good predictive model. There are many different approaches which have been used to create an accurate predictive model. Primitive predictive machine learning algorithms like simple linear regression have failed to produce accurate results primarily due to lack of computing power but also due to lack of optimization techniques. A recent development in deep learning as well as improvements in computing resources has increased the accuracy of predicting time series data. However, with large spatio-temporal data sets spanning over years. Employing regression models on the entire data can cause per date predictions to be corrupted. In this work, we look at dealing with pre-processing the times series. However, pre-processing involves a similarity measure, we explore the use of Dynamic Time Warping (DTW). K-means is then used to classify the spatio-temporal pollution data over a period of 16 years from 2000 to 2016. Here Mean Absolute error (MAE) and Root Mean Square Error (RMSE) have been used as evaluation criteria for the comparison of regression models. Keywords: Spatio-temporal data, Primitive predictive machine learning algorithms, regression models

Network architecture determines vein fate during spontaneous reorganization, with a time delay

Vascular networks continuously reorganize their morphology by growing new or shrinking existing veins to optimize function. Flow shear stress on vein walls has been set forth as the local driver for this continuous adaptation. Yet, shear feedback alone cannot account for the observed diversity of network dynamics -- a puzzle made harder by scarce spatio-temporal data. Here, we resolve network-wide vein dynamics and shear during spontaneous reorganization in the prototypical vascular networks of Physarum polycephalum. Our experiments reveal a plethora of vein dynamics (stable, growing, shrinking) that are not directly proportional to local shear. We observe (a) that shear rate sensing on vein walls occurs with a time delay of 1 to 3 min and (b) that network architecture dependent parameters -- such as relative pressure or relative vein resistance -- are key to determine vein fate. We derive a model for vascular adaptation, based on force balance at the vein walls. Together with the time delay, our model reproduces the diversity of experimentally observed vein dynamics, and confirms the role of network architecture. Finally, we observe avalanches of network reorganization events which cause entire clusters of veins to vanish. Such avalanches are consistent with architectural feedback as the vein connections perpetually change with reorganization. As these network architecture dependent parameters are intrinsically connected with the laminar fluid flow in the veins, we expect our findings to play a role across flow-based vascular networks.