Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships

Wu, Shengjun; Wang, Zhongyi; Du, Zhenhong; Huang, Bo; Zhang, Feng; Liu, Renyi

doi:10.1080/13658816.2020.1775836

Cited by 41 publications

(27 citation statements)

References 40 publications

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“…It is characterized by a designed spatially weighted neural network (SWNN) that can represent the spatial nonstationary weight matrix in spatial processes. Additionally, a geographically and temporally neural network weighted regression (GTNNWR) model (Wu et al, 2020), which is a temporal extension of GNNWR, was also proposed by the same group for further modeling spatiotemporal nonstationary relationships. GTNNWR can generate a space-time distance by utilizing the so-called spatiotemporal proximity neural network (STPNN), which may address complex nonlinear interactions between time and space.…”

Section: Discussionmentioning

confidence: 99%

A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time

Que

et al. 2020

Geosci. Model Dev.

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Abstract. Local spatiotemporal nonstationarity occurs in various natural and socioeconomic processes. Many studies have attempted to introduce time as a new dimension into a geographically weighted regression (GWR) model, but the actual results are sometimes not satisfying or even worse than the original GWR model. The core issue here is a mechanism for weighting the effects of both temporal variation and spatial variation. In many geographical and temporal weighted regression (GTWR) models, the concept of time distance has been inappropriately treated as a time interval. Consequently, the combined effect of temporal and spatial variation is often inaccurate in the resulting spatiotemporal kernel function. This limitation restricts the configuration and performance of spatiotemporal weights in many existing GTWR models. To address this issue, we propose a new spatiotemporal weighted regression (STWR) model and the calibration method for it. A highlight of STWR is a new temporal kernel function, wherein the method for temporal weighting is based on the degree of impact from each observed point to a regression point. The degree of impact, in turn, is based on the rate of value variation of the nearby observed point during the time interval. The updated spatiotemporal kernel function is based on a weighted combination of the temporal kernel with a commonly used spatial kernel (Gaussian or bi-square) by specifying a linear function of spatial bandwidth versus time. Three simulated datasets of spatiotemporal processes were used to test the performance of GWR, GTWR, and STWR. Results show that STWR significantly improves the quality of fit and accuracy. Similar results were obtained by using real-world data for precipitation hydrogen isotopes (δ2H) in the northeastern United States. The leave-one-out cross-validation (LOOCV) test demonstrates that, compared with GWR, the total prediction error of STWR is reduced by using recent observed points. Prediction surfaces of models in this case study show that STWR is more localized than GWR. Our research validates the ability of STWR to take full advantage of all the value variation of past observed points. We hope STWR can bring fresh ideas and new capabilities for analyzing and interpreting local spatiotemporal nonstationarity in many disciplines.

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Section: Discussionmentioning

confidence: 99%

A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time

Que

et al. 2020

Geosci. Model Dev.

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“…This is the main advantage of deep CNNs in comparison to conventional ML methods where user defined filter weights are needed. This feature of CNNs has been used on a limited basis to automatically define W matrix weights in other spatial applications [24]. Pooling layers aggregate neighboring pixels into a single pixel, reducing the image's overall dimensions [140].…”

Section: Deep Convolutional Neural Networkmentioning

confidence: 99%

“…Machine learning (ML) has become a widely used approach in almost every discipline to solve a broad range of tasks and problems with structured and unstructured data, including but not limited to regression, grouping, classification, and prediction. It has proved itself to be a powerful and effective tool in various disciplinary fields and domains of application where spatial aspects are essential, including the following: land use and land cover classification [1,2], cross-sectional characterization [3,4] and longitudinal change [5], urban growth [6] and gentrification [7], disaster management [8], agriculture and crop yield prediction [9], infectious disease emergence and spread [10], transportation and crash analysis [11], map visualization and cartography [12,13], delineation of geographic regions [14] and habitat mapping [15], geographic information retrieval and text matching [16], POI and region recommendation [17], trajectory and movement pattern prediction [18], point cloud classification [19], spatial interaction [20], spatial interpolation [21], and spatiotemporal prediction [22][23][24].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning of Spatial Data

Nikparvar

Thill

2021

IJGI

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Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue that they offer the most promising prospects for the future of spatial machine learning.

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“…Expanding from the geographically weighted regression (GWR), Du et al [24] integrated GWR with neural networks to account for nonstationary weight metrics that incorporate the spatial distribution of the environmental observations. Similarly, Wu et al [25] expanded the GWRNN into both spatial and temporal weighted regressions that account for spatiotemporal non-stationary dependencies within the environmental observations to enhance the forecasting. Hewage et al [26] trained and compared two deep learning models (LSTM and Convolution RNN) with surface temperature, pressure, wind, precipitation, humidity, snow, and soil temperature that are generated from numerical weather prediction models.…”

Section: B Machine Learning Temperature Predictionmentioning

confidence: 99%

Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for Localized Surface Temperature Forecasting in an Urban Environment

Yu¹,

et al. 2021

IEEE Access

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The rising temperature is one of the key indicators of a warming climate, capable of causing extensive stress to biological systems as well as built structures.Ambient temperature collected at ground level can have higher variability than regional weather forecasts, which fail to capture local dynamics. There remains a clear need for accurate air temperature prediction at the suburban scale at high temporal and spatial resolutions. This research proposed a framework based on a long short-term memory (LSTM) deep learning network to generate day-ahead hourly temperature forecasts with high spatial resolution. Air temperature observations are collected at a very fine scale (∼150m) along major roads of New York City (NYC) through the Internet of Things (IoT) data for 2019-2020. The network is a stacked two layer LSTM network, which is able to process the measurements from all sensor locations at the same time and is able to produce predictions for multiple future time steps simultaneously. Experiments showed that the LSTM network outperformed other traditional time series forecasting techniques, such as the persistence model, historical average, AutoRegressive Integrated Moving Average (ARIMA), and feedforward neural networks (FNN). In addition, historical weather observations are collected from in situ weather sensors (i.e., Weather Underground, WU) within the region for the past five years. Experiments were conducted to compare the performance of the LSTM network with different training datasets: 1) IoT data alone, or 2) IoT data with the historical five years of WU data. By leveraging the historical air temperature from WU, the LSTM model achieved a generally increased accuracy by being exposed to more historical patterns that might not be present in the IoT observations. Meanwhile, by using IoT observations, the spatial resolution of air temperature predictions is significantly improved.INDEX TERMS air temperature, Internet of Things (IoT), long short-term memory (LSTM), urban weather

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Geographically and temporally neural network weighted regression for modeling spatiotemporal non-stationary relationships

Cited by 41 publications

References 40 publications

A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time

A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time

Machine Learning of Spatial Data

Using Long Short-Term Memory (LSTM) and Internet of Things (IoT) for Localized Surface Temperature Forecasting in an Urban Environment

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