Time-series clustering - A decade review

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Mathematical Methods Of Statistics

Shifts in the timing of spring phenology are a central feature of global change research. Long-term observations of plant phenology have been used to track vegetation responses to climate variability but are often limited to particular species and locations and may not represent synoptic patterns. Satellite remote sensing is instead used for continental to global monitoring. Although numerous methods exist to extract phenological timing, in particular start-of-spring (SOS), from time series of reflectance data, a comprehensive intercomparison and interpretation of SOS methods has not been conducted. Here, we assess 10 SOS methods for North America between 1982 and 2006. The techniques include consistent inputs from the 8km Global Inventory Modeling and Mapping Studies Advanced Very High Resolution Radiometer NDVIg dataset, independent data for snow cover, soil thaw, lake ice dynamics, spring streamflow timing, over 16000 individual measurements of ground-based phenology, and two temperature-driven models of spring phenology. Compared with an ensemble of the 10 SOS methods, we found that individual methods differed in average day-of-year estimates by ! 60 days and in standard deviation by ! 20 days. The ability of the satellite methods to retrieve SOS estimates was highest in northern latitudes and lowest in arid, tropical, and Mediterranean ecoregions. The ordinal rank of SOS methods varied geographically, as did the relationships between SOS estimates and the cryospheric/hydrologic metrics. Compared with ground observations, SOS estimates were more related to the first leaf and first flowers expanding phenological stages. We found no evidence for time trends in spring arrival from ground- or model-based data; using an ensemble estimate from two methods that were more closely related to ground observations than other methods, SOS

Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006 M I C H A E L A. W H I T E*, K I R S T E N M. DE BEURS w , K A M E L D I D A Nz, D AV I D W. I N O U Y E § ,

Proximity and Innovation: A Critical Assessment

Unpacking the innovation ecosystem construct: evolution, gaps and trends

This study examines the socio-spatial context of uneven development and the residential accessibility of green space in Dalian of Liaoning Province, China. The social fairness was analyzed with a community scale as the basis. We combined social scientific methods with a GIS method using a behavior accessibility model from the perspectives of fairness of urban green space allocation based on social geography, geographic information science, management science and many other related discipline theories. The results show that: 1) Most of the urban green space distribution presents an unbalanced phenomenon, and it does not match with the population distribution; 2) We found some differences in the accessibility of the population with different attributes and opportunities to use and enjoy the urban green spaces, mainly due to: the dual social and spatial attributes of the residents and the serious stratum differentiation generated were the internal causes; the residential space differentiation and the pursuit of economic and real estate development were the direct causes; and unreasonable planning, in regard to the fact that government policies did not give consideration to efficiency and fairness, was also an important factor.

Urban green space, uneven development and accessibility: a case of Dalian's Xigang District

Urban ventilation corridors and spatiotemporal divergence patterns of urban heat island intensity: a local climate zone perspective

ABSTRACT The urban development boundary (UDB) is a core issue in territorial spatial planning and remains a research focus in urban development. However, in the past, land use simulations of coastal cities have had some limitations. For example, new urban land outside the research boundary could not be effectively simulated, resulting in a large gap between the simulation results and reality. In this study, an artificial neural network (ANN) cellular automata (CA) model based on a new perspective, the Offshore Island Connection Line (collectively, OICL-ANN-CA), was developed to address this problem. We first proposed the delineation principles and methods of OICL and applied it to the Jinpu New Area of Dalian City, Liaoning Province, China. Based on the conversion probability and land use simulation results obtained by ANN and CA, this study validated the UDB simulations for 2000–2020 in the Jinpu New Area and predicted the UDB for 2020–2035 under three scenarios: historical inertia development, ecological security protection, and ecological and economic balance. The results indicate that, compared with the traditional perspective model (the ANN-CA model based on the sea–land boundary), the OICL-ANN-CA model exhibited better simulation accuracy (the figure of merit was approximately 35% higher) and effectively simulated new urban land outside the traditional boundary. This simulation of urban land expansion is more consistent with recent development in the study area. In addition, the predicted results of UDB in 2035 also demonstrate the benefits of this model for detecting reclaimed land. This study illustrates that the OICL-ANN-CA model is a more capable method for capturing changes in new coastal urban land and can produce realistic simulations. It provides a reference for delimiting the UDB and defining the research boundary for the Jinpu New Area and other fast-growing coastal cities in China.

https://www.tandfonline.com/doi/pdf/10.1080/15481603.2022.2071056?needAccess=true

Offshore Island Connection Line: A new perspective of coastal urban development boundary simulation and multi-scenario prediction

Hyperspectral image (HSI) with hundreds of narrow and consecutive spectral bands provides substantial information to discriminate various land-covers. However, the existence of redundant features/b...

Unsupervised hyperspectral feature selection based on fuzzy c-means and grey wolf optimizer

Ice, snow, and rime are wonders of the cold season in an alpine climate zone and climate landscape. With its pure, spectacular, and magical features, these regions attract numerous tourists. Ice and snow landscapes can provide not only visually-stimulating experiences for people, but also opportunities for outdoor play and movement. In China, ice and snow tourism is a new type of recreation; however, the establishment of snow and ice in relation to the suitability of the surrounding has not been clearly expressed. Based on multi-source data, such as tourism, weather, and traffic data, this paper employs the Delphi-analytic hierarchy process (AHP) evaluation method and a spatial analysis method to study the spatial differences of snow and ice tourism suitability in China. China’s ice and snow tourism is located in the latitude from 35°N to 53.33°N and latitude 41.5°N to 45°N and longitude 82°E to 90°E, with the main focus on latitude and terrain factors. A poor fit is concentrated at latitude 20.45°N to 35°N and longitude 100°E to 122°E; the difference is that the latitude is low and affected by the Japanese warm current. The analysis of the suitability of ice and snow tourism can be employed as a reference for the development of ice and snow tourism.

Jun Yang

Papers

Urban green space, uneven development and accessibility: a case of Dalian's Xigang District

Urban ventilation corridors and spatiotemporal divergence patterns of urban heat island intensity: a local climate zone perspective

Offshore Island Connection Line: A new perspective of coastal urban development boundary simulation and multi-scenario prediction

Unsupervised hyperspectral feature selection based on fuzzy c-means and grey wolf optimizer

The Spatial Differentiation of the Suitability of Ice-Snow Tourist Destinations Based on a Comprehensive Evaluation Model in China