LISA (Local Indicators of Spatial Association)

Forest land is the carrier for growing forests. It is of great significance to evaluate the forest land quality scientifically and delineate forestland protection zones reasonably for realizing better forest land management, promoting ecological civilization construction, and coping with global climate change. In this study, taking Hefeng County, Hubei Province, a subtropical humid evergreen broad-leaved forest region in China, as the study area, 14 indicators were selected from four dimensions—climatic conditions, terrain, soil conditions, and socioeconomics—to construct a forest land quality evaluation index system. Based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model, we introduced the Particle Swarm Optimization (PSO) algorithm to design the evaluation model to evaluate the forest land quality and analyze the distribution of forest land quality in Hefeng. Further, we used the Local Indicator of Spatial Association (LISA) to explore the spatial distribution of forest land quality and delineate the forest land protection zones. The results showed the following: (1) the overall quality of forest land was high, with some variability between regions. The range of Forest Land Quality Index (FLQI) in Hefeng was 0.4091–0.8601, with a mean value of 0.6337. The forest land quality grades were mainly first and second grade, with the higher-grade forest land mainly distributed in the central and southeastern low mountain regions of Zouma, Wuli, and Yanzi. The lower-grade forest land was mainly distributed in the northwestern middle and high mountain regions of Zhongying, Taiping, and Rongmei. (2) The global spatial autocorrelation index of forest land quality in Hefeng County was 0.7562, indicating that the forest land quality in the county had a strong spatial similarity. The spatial distribution of similarity types high-high (HH) and low-low (LL) was more clustered, while the spatial distribution of dissimilarity types high-low (HL) and low-high (LH) was generally dispersed. (3) Based on the LISA of forest land quality, forest land protection zones were divided into three types: key protection zones (KPZs), active protection zones (APZs), and general protection zones (GPZs). The forest land protection zoning basically coincided with the forest land quality. Combining the characteristics of self-correlated types in different forestland protection zones, corresponding management and protection measures were proposed. This showed that the PSO-TOPSIS model can be effectively used for forest land quality evaluation. At the same time, the spatial attributes of forest land were incorporated into the development of forest land protection zoning scheme, which expands the method of forest land protection zoning, and can provide a scientific basis and methodological reference for the reasonable formulation of forest land use planning in Hefeng County, while also serving as a reference for similar regions and countries.

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Multi-step real-time wind speed prediction based on convolution memory network

Wind Engineering ◽

10.1177/0309524x21998266 ◽

2021 ◽

pp. 0309524X2199826

Author(s):

Guowei Cai ◽

Yuqing Yang ◽

Chao Pan ◽

Dian Wang ◽

Fengjiao Yu ◽

...

Keyword(s):

Feature Extraction ◽

Wind Speed ◽

Real Time ◽

Wind Power ◽

Spatial Correlation ◽

Prediction Accuracy ◽

Spatial Information ◽

Spatial Association ◽

Spatial Feature ◽

Time Prediction

Multi-step real-time prediction based on the spatial correlation of wind speed is a research hotspot for large-scale wind power grid integration, and this paper proposes a multi-location multi-step wind speed combination prediction method based on the spatial correlation of wind speed. The correlation coefficients were determined by gray relational analysis for each turbine in the wind farm. Based on this, timing-control spatial association optimization is used for optimization and scheduling, obtaining spatial information on the typical turbine and its neighborhood information. This spatial information is reconstructed to improve the efficiency of spatial feature extraction. The reconstructed spatio-temporal information is input into a convolutional neural network with memory cells. Spatial feature extraction and multi-step real-time prediction are carried out, avoiding the problem of missing information affecting prediction accuracy. The method is innovative in terms of both efficiency and accuracy, and the prediction accuracy and generalization ability of the proposed method is verified by predicting wind speed and wind power for different wind farms.

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Kernel Based Data-Adaptive Support Vector Machines for Multi-Class Classification

Mathematics ◽

10.3390/math9090936 ◽

2021 ◽

Vol 9 (9) ◽

pp. 936

Author(s):

Jianli Shao ◽

Xin Liu ◽

Wenqing He

Keyword(s):

Machine Learning ◽

Spatial Association ◽

Class Imbalance ◽

Imbalanced Data ◽

Real Data ◽

Kernel Functions ◽

Support Vector ◽

Classification Problems ◽

Rare Class ◽

Data Adaptive

Imbalanced data exist in many classification problems. The classification of imbalanced data has remarkable challenges in machine learning. The support vector machine (SVM) and its variants are popularly used in machine learning among different classifiers thanks to their flexibility and interpretability. However, the performance of SVMs is impacted when the data are imbalanced, which is a typical data structure in the multi-category classification problem. In this paper, we employ the data-adaptive SVM with scaled kernel functions to classify instances for a multi-class population. We propose a multi-class data-dependent kernel function for the SVM by considering class imbalance and the spatial association among instances so that the classification accuracy is enhanced. Simulation studies demonstrate the superb performance of the proposed method, and a real multi-class prostate cancer image dataset is employed as an illustration. Not only does the proposed method outperform the competitor methods in terms of the commonly used accuracy measures such as the F-score and G-means, but also successfully detects more than 60% of instances from the rare class in the real data, while the competitors can only detect less than 20% of the rare class instances. The proposed method will benefit other scientific research fields, such as multiple region boundary detection.

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Distribution of rorquals and Atlantic cod in relation to their prey in the Norwegian high Arctic

Polar Biology ◽

10.1007/s00300-021-02835-2 ◽

2021 ◽

Author(s):

Hiroko K. Solvang ◽

Tore Haug ◽

Tor Knutsen ◽

Harald Gjøsæter ◽

Bjarte Bogstad ◽

...

Keyword(s):

Prey Species ◽

Barents Sea ◽

Atlantic Cod ◽

Spatial Association ◽

High Arctic ◽

Humpback Whales ◽

Predatory Fish ◽

Detection Range ◽

Minke Whales ◽

Acoustic Methods

AbstractRecent warming in the Barents Sea has led to changes in the spatial distribution of both zooplankton and fish, with boreal communities expanding northwards. A similar northward expansion has been observed in several rorqual species that migrate into northern waters to take advantage of high summer productivity, hence feeding opportunities. Based on ecosystem surveys conducted during August–September in 2014–2017, we investigated the spatial associations among the three rorqual species of blue, fin, and common minke whales, the predatory fish Atlantic cod, and their main prey groups (zooplankton, 0-group fish, Atlantic cod, and capelin) in Arctic Ocean waters to the west and north of Svalbard. During the surveys, whale sightings were recorded by dedicated whale observers on the bridge of the vessel, whereas the distribution and abundance of cod and prey species were assessed using trawling and acoustic methods. Based on existing knowledge on the dive habits of these rorquals, we divided our analyses into two depth regions: the upper 200 m of the water column and waters below 200 m. Since humpback whales were absent in the area in 2016 and 2017, they were not included in the subsequent analyses of spatial association. No association or spatial overlap between fin and blue whales and any of the prey species investigated was found, while associations and overlaps were found between minke whales and zooplankton/0-group fish in the upper 200 m and between minke whales and Atlantic cod at depths below 200 m. A prey detection range of more than 10 km was suggested for minke whales in the upper water layers.

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Spatial Accessibility to Local Public Services in an Unequal Place: An Analysis from Patterns of Residential Segregation in the Metropolitan Area of Santiago, Chile

Sustainability ◽

10.3390/su13020442 ◽

2021 ◽

Vol 13 (2) ◽

pp. 442

Author(s):

Yasna Cortés

Keyword(s):

Residential Segregation ◽

Metropolitan Area ◽

Low Income ◽

Public Services ◽

Housing Prices ◽

Spatial Association ◽

Spatial Accessibility ◽

Urban Systems ◽

Local Public Services ◽

Low Income Groups

The study of the relationship between the provision of local public services and residential segregation is critical when it might be the social manifestation of spatial income inequality. This paper analyzes how the spatial accessibility to local public services is distributed equitably among different social and economic groups in the Metropolitan Area of Santiago (MR), Chile. To accomplish this objective, I use accessibility measures to local public services such as transportation, public education, healthcare, kindergartens, parks, fire and police stations, cultural infrastructure, and information about housing prices and exempted housing units from local taxes by block, as well as quantile regressions and bivariate Local Indicators of Spatial Association (LISA). The main results confirm the accessibility to local public services is unequally distributed among residents. However, it affects more low-income groups who are suffering from significant deficits in the provision of local public services. In this scenario, poor residents face a double disadvantage due to their social exclusion from urban systems and their limited access to essential services such as education, healthcare, or transportation. In particular, I found that social residential segregation might be reinforced by insufficient access to local infrastructure that the most impoverished population should assume.

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Consistency of bootstrap approximation to the null distributions of local spatial statistics with application to house price analysis

Journal of Inequalities and Applications ◽

10.1186/s13660-020-02482-x ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Chang-Lin Mei ◽

Shou-Fang Xu ◽

Feng Chen

Keyword(s):

Spatial Statistics ◽

Bootstrap Method ◽

Spatial Association ◽

House Price ◽

Housing Price ◽

Bootstrap Approximation ◽

Local Statistics ◽

Null Distributions ◽

Local Spatial Statistics ◽

Distribution Of A Statistic

Abstract With the increasing availability of spatially extensive geo-referenced data, much attention has been paid to the use of local statistics to identify local patterns of spatial association, in which the null distributions of local statistics play an essential role in the related statistical inference. As a powerful tool to approximate the distribution of a statistic, the bootstrap method is used in this paper to derive null distributions of the commonly used local spatial statistics including local Getis and Ord’s $G_{i}$ G i , Moran’s $I_{i}$ I i and Geary’s $c_{i}$ c i . Strong consistency of the bootstrap approximation to the null distributions of the statistics is proved under some mild conditions, and the Boston housing price data are analyzed to demonstrate the application of the theoretical results.

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Variability in the spatial association patterns of sponge assemblages in response to environmental heterogeneity

Marine Biology ◽

10.1007/s00227-010-1514-5 ◽

2010 ◽

Vol 157 (11) ◽

pp. 2503-2509 ◽

Cited By ~ 3

Author(s):

James J. Bell ◽

Jade Berman ◽

Timothy Jones ◽

Leanne J. Hepburn

Keyword(s):

Environmental Heterogeneity ◽

Spatial Association ◽

Association Patterns

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Canopy Height Estimation at Landsat Resolution Using Convolutional Neural Networks

Machine Learning and Knowledge Extraction ◽

10.3390/make2010003 ◽

2020 ◽

Vol 2 (1) ◽

pp. 23-36

Author(s):

Syed Aamir Ali Shah ◽

Muhammad Asif Manzoor ◽

Abdul Bais

Keyword(s):

Neural Networks ◽

Random Forest ◽

Convolutional Neural Networks ◽

Forest Structure ◽

State Of The Art ◽

Spatial Association ◽

Canopy Height ◽

Landsat Images ◽

Structure Estimation ◽

Vegetation Height

Forest structure estimation is very important in geological, ecological and environmental studies. It provides the basis for the carbon stock estimation and effective means of sequestration of carbon sources and sinks. Multiple parameters are used to estimate the forest structure like above ground biomass, leaf area index and diameter at breast height. Among all these parameters, vegetation height has unique standing. In addition to forest structure estimation it provides the insight into long term historical changes and the estimates of stand age of the forests as well. There are multiple techniques available to estimate the canopy height. Light detection and ranging (LiDAR) based methods, being the accurate and useful ones, are very expensive to obtain and have no global coverage. There is a need to establish a mechanism to estimate the canopy height using freely available satellite imagery like Landsat images. Multiple studies are available which contribute in this area. The majority use Landsat images with random forest models. Although random forest based models are widely used in remote sensing applications, they lack the ability to utilize the spatial association of neighboring pixels in modeling process. In this research work, we define Convolutional Neural Network based model and analyze that model for three test configurations. We replicate the random forest based setup of Grant et al., which is a similar state-of-the-art study, and compare our results and show that the convolutional neural networks (CNN) based models not only capture the spatial association of neighboring pixels but also outperform the state-of-the-art.

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