Real‐time inverse distance weighting interpolation for streaming sensor data

Abstract

With advances in technology and an increasing variety of inexpensive geosensors, environmental monitoring has become increasingly sensor dense and real time. Using sensor data streams enables real‐time applications such as environmental hazard detection, or earthquake, wildfire, or radiation monitoring. In‐depth analysis of such spatial fields is often based on a continuous representation. With very large numbers of concurrent observation streams, novel algorithms are necessary that integrate streams into rasters, or other continuous representations, continuously in real time. In this article, we present an approach leveraging data stream engines (DSEs) to achieve scalable, high‐throughput inverse distance weighting (IDW). In detail, we designed and implemented a novel stream query operator framework that extends general‐purpose DSEs. The proposed framework includes a two‐panel, spatio‐temporal grid‐based index and several algorithms, namely the Shell and k‐Shell algorithms, to estimate individual grid cells efficiently and adaptively for different sampling scenarios. For our performance experiments, we generated several different spatio‐temporal stream data sets based on the radiation deposits in the Fukushima region after the nuclear accident of 2011 in Japan. Our results showed that the k‐Shell algorithm of the proposed framework produces a raster based on 250k observation streams in under 0.5 s using a state‐of‐the‐art workstation.

Spatial Dynamics of Air Pollution in Tangerang City, Jabodetabek Metropolitan Area

“…GIS is a computerized system that processes data ranging from data entry phases, data analysis and data presentation designed to problems related to location data and or other georeferenced data [10]. The study was conducted with the following steps: Spatial interpolation method Firstly, CO concentration data in interpolation with the IDW method was used to estimate spatial values based on the value of known points of measurement locations [11]. Spatial interpolation (or spatial prediction) aims to predict the values of a target variable located throughout the research area presented in the form of an image or map [12].…”

Spatiotemporal stacking method with daily‐cycle restrictions for reconstructing missing hourly PM2.5 records

“…Generally, these methods utilize a particular mathematical model to forecast missing data (Chen, Gao, et al., 2022). The commonly used spatial interpolator includes inverse distance weighting (IDW) (Liang et al., 2018), Kriging (Jeffrey et al., 2001), radial basis function (Chen, Bei, et al., 2022), and Point interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (P‐BSHADE) (Jiang et al., 2018; Xu et al., 2013, 2022). Meanwhile, two commonly used temporal interpolation methods are simple exponential smoothing (SES) (Cheng & Lu, 2017) and autoregressive integrated moving average (Kotu & Deshpande, 2019).…”

A Decision Tree Approach for Spatially Interpolating Missing Land Cover Data and Classifying Satellite Images

“…Inverse distance weighted interpolation uses spatial autocorrelation to interpolate unknown data by assigning more weight to the data closer to the observation of interest than data, which are geographically further away [24]. This method has been used for remote sensing data for decades [25] and with improvements in computing, it has recently been implemented for real-time analysis of sensor data [26].…”