Getis–Ord’s hot- and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree data

Peeters, Aviva; Zude, Manuela; Käthner, Jana; Ünlü, Mustafa; Kanber, Rıza; Hetzroni, Amots; Gebbers, Robin; Ben‐Gal, Alon

doi:10.1016/j.compag.2014.12.011

Cited by 146 publications

(96 citation statements)

References 46 publications

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“…After planting, the trees within the orchards grow at the rate of 0.4-0.5 m per year. 19 During our experiment, the trees had an average height of 6.57 m. The trees are periodically trimmed from time to time every year. Irrigation tubes were installed along the rows, 0.5 m away from the roots.…”

Section: Experimental Data Collection For Determining Representative mentioning

confidence: 99%

Practical deployment of an in-field wireless sensor network in date palm orchard

Rao

Zhu

et al. 2017

International Journal of Distributed Sensor Networks

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Recent advances in agriculture-oriented sensors and wireless communication technologies offer great opportunities for the development and application of precision agriculture. This article describes our experience during the deployment of an experimental in-field sensor network that provided real-time monitoring in a 10-year-old date palm orchard. Taking into account the spatial variability of multiple growth-determining soil variables, the sub-regions of the sensor placement were determined by delineating the orchard into several monitoring zones. Then, the specific number of the required sensors was calculated based on the requirement of data accuracy. The sensors were distributed into the target orchard by combining the tree number with the coefficient variability of growth-determining soil variables in the monitoring zones. Finally, the networking was realized by means of synthesizing the received wireless signal strength distribution, the layout of monitoring zones, and the number of the required sensors. Some data from the deployed wireless sensor network are presented and discussed.

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Section: Experimental Data Collection For Determining Representative mentioning

confidence: 99%

Practical deployment of an in-field wireless sensor network in date palm orchard

Rao

Zhu

et al. 2017

International Journal of Distributed Sensor Networks

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“…Large Z-values indicate that hotspots are clustered together, whereas low Z-values indicate that cold spots are clustered together. The Getis-Ord Gi* local statistic is given as: and beyond which all locations are not neighbors (indicated by o in the W matrix) [54]. n is equal to the total number of features and:…”

Section: Network Kernel Density Estimationmentioning

confidence: 99%

A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China

Nie

Wang

et al. 2015

Sustainability

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Abstract:Research on spatial cluster detection of traffic crash (TC) at the city level plays an essential role in safety improvement and urban development. This study aimed to detect spatial cluster pattern and identify riskier road segments (RRSs) of TC constrained by network with a two-step integrated method, called NKDE-GLINCS combining density estimation and spatial autocorrelation. The first step is novel and involves in spreading TC count to a density surface using Network-constrained Kernel Density Estimation (NKDE). The second step is the process of calculating local indicators of spatial association (LISA) using Network-constrained Getis-Ord Gi* (GLINCS). GLINCS takes the smoothed TC density as input value to identify locations of road segments with high risk. This method was tested using the TC data in 2007 in Wuhan, China. The results demonstrated that the method was valid to delineate TC cluster and identify risk road segments. Besides, it was more effective compared with traditional GLINCS using TC counting as input. Moreover, the top 20 road segments with high-high TC density at the significance level of 0.1 were listed. These results can promote a better identification of RRS, which is valuable in the pursuit of improving transit safety and sustainability in urban road network. Further research should address spatial-temporal analysis and TC factors exploration. OPEN ACCESSSustainability 2015, 7 2663

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“…The Getis-Ord General G statistic computes a single statistic for the entire study area, while the Getis-Ord Gi * statistic serves as an indicator for local autocorrelation, i.e., it measures how spatial autocorrelation varies locally over the study area and computes a statistic for each data point [29]. Compared with the Moran Index, another important spatial autocorrelation analysis method, the Getis-Ord statistic gives more intuitive results and a better visual exploration, and has the advantage of distinguishing high value clusters or low value clusters.…”

Section: Spatial Getis-ord Statistical Analysismentioning

confidence: 99%

“…where Gi * (d) is the local G statistic for a feature (i) within a distance (d), and Wij (d) represents the spatial weight for the target-neighbor i and j pair [29]. In order to improve the statistical testing, Ord and Getis (1995) …”

Section: Spatial Getis-ord Statistical Analysismentioning

confidence: 99%

“…Notably, spatial-temporal hotspot pattern analysis has been widely used in describing geographical events or phenomena [24,25]. The prediction accuracy index (PAI) [26], the Kernel density estimation (KDE) [24,27], Spatial Autocorrelation, and Spatial Getis-Ord Gi * statistic methods have all been mainly introduced to analyze spatial patterns [28][29][30]. The main purpose of this paper is to reveal the temporal-spatial hotspot pattern of provincial environmental pollution incidents and its relationship with social-economic activities and regional sustainable development for the period 1995-2012 in China.…”

Section: Introductionmentioning

confidence: 99%

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Spatial-Temporal Hotspot Pattern Analysis of Provincial Environmental Pollution Incidents and Related Regional Sustainable Management in China in the Period 1995–2012

et al. 2015

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Spatial-temporal hotspot pattern analysis of environmental pollution incidents provides an indispensable source of information for the further development of incident prevention measures. In this study, the spatial-temporal patterns of environmental pollution incidents in China in the period of 1995-2012 were analyzed, using the Spatial Getis-Ord statistic and an Improved Prediction Accuracy Index (IAPI). The results show that, in this period, the occurrence of environmental incidents exhibited a dynamic growth pattern but then dropped and continued to drop after the year 2006, which was considered a crucial turning point. Not coincidentally, this corresponds to the year when the State Council issued its National Environmental Emergency Plan, and following the examination of major incidents, special actions were taken to strengthen the control of incidents and emergency responses. The results from Getis-Ord General G statistical analysis show that the spatial agglomeration phenomenon was statistically significant after 1999 and that the level of spatial agglomeration was rising, while the Getis-Ord Gi * statistical analysis reveals that environmental pollution incidents were mainly agglomerated in the Pan Yangtze River OPEN ACCESSSustainability 2015, 7 14386Delta and Pan Pearl River Delta regions. Accordingly, the spatial-temporal hotspot pattern based on the IAPI values at the provincial scale could be categorized into: stable hotspots, unstable hotspots, and cold-spot areas. The stable hotspots category was further divided into three subtypes: industrial distribution type, industrial transfer type, and extensive economic growth type. Finally, the corresponding measures for sustainable management were proposed: stable hotspots were classified as essential regions requiring the immediate prevention and control of environmental pollution incidents; unstable hotspots were characterized by their need for ongoing and continual prevention measures, and cold-spots were those areas that required strengthened environmental monitoring. Meanwhile, it was identified that a multi-level environmental pollution incident emergency response and regional (incident) joint control plan needed to be well prepared and carried out effectively. To prevent environmental pollution and the regional transfer of pollution from incidents, measures towards achieving regional environmental planning, environmental risk prevention, environmental emergency monitoring and adequate emergency responses must be efficiently implemented.

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Getis–Ord’s hot- and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree data

Cited by 146 publications

References 46 publications

Practical deployment of an in-field wireless sensor network in date palm orchard

Practical deployment of an in-field wireless sensor network in date palm orchard

A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China

Spatial-Temporal Hotspot Pattern Analysis of Provincial Environmental Pollution Incidents and Related Regional Sustainable Management in China in the Period 1995–2012

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