Drought is a stochastic natural hazard that is instigated by intense and persistent shortage of precipitation. Following an initial meteorological phenomenon, subsequent impacts are realized on agr...

A review of drought indices

Being newly immersed in the upstream part of the oil business, I just recently had my first work session with data in ARC–GIS®. The project involves subsurface geographical modeling. Obviously I had considerable interest in discovering if the methodology in this book would enhance my modeling capabilities. The book is for social scientists, but I had no difficulty imagining my own important oil exploration application within the framework of geographically weighted regression (GWR). The first chapter nicely explains what is unique in this book. A standard regression model using geographically oriented data (the example is housing prices across all of England) is a global representation of a spatial relationship, an average that does not account for any local differences. In y = f (x), imagine a whole family of f ’s that are indexed by spatial location. That is the focus of this book. It is about one form of local spatial modeling, which is GWR. A more general resource for this topic is the earlier book by Fotheringham and Wegener (2000), which escaped the notice of Technometrics. Imagine a display of model parameters in a geographical information system (GIS) and you will understand the focus for this book. The authors note, “only where there is no significant spatial variation in a measured relationship can global models be accepted” (p. 10). The second chapter develops the basis of GWR. It analyzes the housing sales prices versus the 33 boroughs in London and begins by fitting a conventional multiple regression model versus housing characteristics. The GWR is motivated by differences in the regression models fitted separately by borough. The GWR is a spatial moving-window approach with all data distances weighted versus a specific data point using a weighting function and a bandwidth. A GIS can then be used to evaluate the spatial dependency of the parameters. As in kriging, local standard errors also are calculated. The chapter also provides all the math. Chapter 3 comprises several further considerations: parameters that are globally constant, outliers, and spatial heteroscedasticity. The first issue leads to hypothesis tests for model comparison using an Akaike information criterion (AIC). Local outliers are hard to detect. Studentized (deletion) residuals are recommended. The outliers can be plotted geographically. Robust regression is suggested as a less computationally intensive alternative. Hetereoscedasticity is harder to handle. Chapter 4 adds statistical inference to the capabilities of GWR: both a confidence interval approach using local likelihood and an AIC method. Four additional methodology chapters present various extensions of GWR. Chapter 5 considers the relationship between GWR and spatial autocorrelation, and includes a combined version of GWR and spatial regression using some complex hybrid models. Chapter 6 examines the relationship of scale and zoning problems in spatial analysis to GWR. Chapter 7 introduces the use of initial exploratory data analysis using geographically weighted statistics, which are based on the idea of using a kernel around each data point to create weights. Univariate statistics and correlation coefficients are defined for exploring local patterns in data. A final set of extensions in Chapter 8 discusses regression models with non-Gaussian errors, logistic regression, local principal components analysis, and local probability density estimation. The methods all use some kind of distributional model. The million-dollar question for me is always, “What about software?” The authors have a stand-alone program, GWR 3, available in CD–ROM by contacting the authors. Basically the drill with GWR 3 is to gather your data, use Excel to transform and reformat the data for GWR 3, use GWR 3 to produce a set of coefficients, and feed those coefficients to your favorite GIS to produce your maps. Forty pages of discussion about using the software are provided. A final epilogue chapter also discusses embedding GWR in R or Matlab and includes some references to people who have done that type of work. I probably would not have read this book if I had not happened to have had it in my briefcase on a visit with the exploration technologists. Though inclusive of appropriate mathematical development, this material is readily approachable because of the many illustrations and the pages and pages of GIS displays. The authors unabashedly present much of the material as their developmental work, so GWR offers a lot of opportunity for research and further development through novel applications and extensions.

Geographically Weighted Regression

As the next-generation manufacturing system, intelligent manufacturing enables better quality, higher productivity, lower cost, and increased manufacturing flexibility. The concept of sustainability is receiving increasing attention, and sustainable manufacturing is evolving. The digital twin is an emerging technology used in intelligent manufacturing that can grasp the state of intelligent manufacturing systems in real-time and predict system failures. Sustainable intelligent manufacturing based on a digital twin has advantages in practical applications. To fully understand the intelligent manufacturing that provides the digital twin, this study reviews both technologies and discusses the sustainability of intelligent manufacturing. Firstly, the relevant content of intelligent manufacturing, including intelligent manufacturing equipment, systems, and services, is analyzed. In addition, the sustainability of intelligent manufacturing is discussed. Subsequently, a digital twin and its application are introduced along with the development of intelligent manufacturing based on the digital twin technology. Finally, combined with the current status, the future development direction of intelligent manufacturing is presented.

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Digital twin-based sustainable intelligent manufacturing: a review

Calculating cooling extents of green parks using remote sensing: Method and test

The exact location of objects, such as infrastructure, is crucial to the systematic understanding of the built environment. The emergence and development of the Internet of Things (IoT) have attracted growing attention to the low-cost location scheme, which can respond to a dramatic increasing amount of public infrastructure in smart cities. Various Radio Frequency IDentification (RFID)-based locating systems and noise mitigation methods have been developed. However, most of them are impractical for built environments in large areas due to their high cost, computational complexity, and low noise detection capability. In this paper, we proposed a novel noise mitigation solution integrating the low-cost localization scheme with one mobile RFID reader. We designed a filter algorithm to remove the influence of abnormal data. Inspired the sampling concept, a more carefully parameters calibration was carried out for noise data sampling to improve the accuracy and reduce the computational complexity. To achieve robust noise detection results, we employed the powerful noise detection capability of the random sample consensus (RANSAC) algorithm. Our experiments demonstrate the effectiveness and advantages of the proposed method for the localization and noise mitigation in a large area. The proposed scheme has potential applications for location-based services in smart cities.

https://www.mdpi.com/1424-8220/19/9/2143/pdf

A Robust Noise Mitigation Method for the Mobile RFID Location in Built Environment.

Geospatial dashboards have attracted increasing attention from both user communities and academic researchers since the late 1990s. Dashboards can gather, visualize, analyze and advise on urban performance to support sustainable development of smart cities. We conducted a critical review of the research and development of geospatial dashboards, including the integration of maps, spatial data analytics, and geographic visualization for decision support and real-time monitoring of smart city performance. The research about this kind of system has mainly focused on indicators, information models including statistical models and geospatial models, and other related issues. This paper presents an overview of dashboard history and key technologies and applications in smart cities, and summarizes major research progress and representative developments by analyzing their key technical issues. Based on the review, we discuss the visualization model and validity of models for decision support and real-time monitoring that need to be further researched, and recommend some future research directions.

Geospatial Dashboards for Monitoring Smart City Performance

Yunnan suffered from a severe drought from Winter, 2009 to Spring, 2010. Compared with traditional methods for drought monitoring, remote sensing (RS) based methods has the merits of almost real-time and large covering area. In this paper, vegetation water supply index (VWSI) was used to detect the drought condition utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) data in Yunnan province on 7 March, 2010. Merits and pitfalls of this method were analyzed according to the comparison between the RS detected drought and the real drought status developed by the National Meteorological Bureau. Our results showed that VWSI was apt to agriculture fields with dense vegetation covered areas. This means that this method can be used to detect the drought in a regional area.

/pdf/regional-drought-monitoring-and-analyzing-using-modis-data-a-5901zkw456.pdf

Regional Drought Monitoring and Analyzing Using MODIS Data — A Case Study in Yunnan Province

The aim of this study was to provide scientific support for the creation of policy on inbound tourism. A comprehensive and thorough understanding of fine-grained spatiotemporal dynamic patterns is crucial for tourism management, planning and policymaking. In spite of spatiotemporal pattern analysis on tourism movements, environment- and society-related topics have been developed to stimulate tourism. However, few studies have focused on the fine-grained spatiotemporal analysis of tourist behavior. Depending on a fine-grained Flickr data source, we investigated the spatiotemporal dynamic patterns of inbound tourism in the context of fine granularity in the spatial and temporal dimensions. The proposed approach based on fine-grained Flickr data and the emerging spatiotemporal analysis method was to first conduct a refined temporal variation analysis based on the annual, monthly, and daily variation; second, a thorough analysis of the seasonality of tourism was conducted with the kernel density estimation (KDE) method; third, the correlation between the attraction grade and popularity was complementarily exploited with both qualitative and quantitative methods; and finally, the patterns were identified and visualized with the space-time cube in the context of fine granularity. The results from the analysis revealed that the downtown region of Beijing was the most popular place throughout the year due to the many famous Chinese cultural heritage attractions. In contrast, the landscape sites and thematic parks were nearly cold spots because of their strong seasonality. Our approach can also be applied to other crowdsourced data, such as that from Twitter and Instagram. Spatiotemporal analysis and empirical research have interesting implications for other cities in China or other developing countries.

/pdf/fine-grained-spatiotemporal-dynamics-of-inbound-tourists-3a3dwqw60r.pdf

Mingyi Du

Papers

A Robust Noise Mitigation Method for the Mobile RFID Location in Built Environment.

Geospatial Dashboards for Monitoring Smart City Performance

Regional Drought Monitoring and Analyzing Using MODIS Data — A Case Study in Yunnan Province

Fine-Grained Spatiotemporal Dynamics of Inbound Tourists Based on Geotagged Photos: A Case Study in Beijing, China

Impact of the 2008 Olympic Games on urban thermal environment in Beijing, China from satellite images