Human settlement is a necessary factor for human survival and development.The scientific assessments of the natural suitability and appropriateness of human settlements contribute to addressing the discord between ecological environments and economic development and fostering sustainable development in the relationship between humans and nature.Building upon established methods for assessing human settlement suitability,this study investigated the natural suitability of the living environment in the urban agglomeration of mid-southern Liaoning.Based on this exploration,the suitability of the degree of human settlements for the population distribution in the study area from 2000 to 2020 was calculated.The results revealed three important points.(1)Nearly one-fifth of the study area,the area around the offshoot of Changbai Mountain,is a critically suitable area.More than half of this area,generally the buffer zone connecting the Liaohe Plain and the offshoot of Changbai Mountain,is generally suitable for human settlements.The proportion of suitable areas is only 25.53%,mostly on the Liaohe Plain along the Liaodong Peninsula by the Yellow Sea.(2)The overall spatial distribution of human settlement exhibits a pattern that is moderate-low in the middle and high on both sides;and higher in the southern part and lower in the northern part.(3)The human settlement suitability degree is higher in the western regions and lower in the eastern regions,with Shenyang and Panjin in the west having the highest suitability,while Benxi in the east exhibits the lowest habitat suitability.From 2000 to 2020,except for Shenyang and Panjin where suitability remained constant,the suitability degree of other cities has improved.Among them,Dandong experienced the most significant increase in suitability.
Construction land is the leading carrier of human activities such as production and living.Evaluating the construction land suitability(CLS)on the Qinghai-Tibet Plateau(QTP)holds significant implications for harmonizing the relationship between ecological protection and human activity and promoting population and industry layout optimization.However,no relevant studies provide a complete CLS assessment of the QTP.In this study,we developed a model-based CLS evaluation framework coupling of pattern and process to calculate the global CLS on the QTP based on a previously developed CLS evaluation model.Then,using the land-use data of 1990,2000,2010,and 2020,we examined the adaptability of existing construction land(ECL)to the CLS assessment result through the adaptability index and vertical gradient index and further analyzed the limitations of maladaptive construction land.Finally,we calculated the potential area of reserve suitable construction land.This article includes four conclusions:(1)The highly suitable,suitable,moderately suitable,marginally suitable,and unsuitable CLS classes cover areas of 0.33×10^(4)km^(2),10.42×10^(4)km^(2),18.06×10^(4)km^(2),24.12×10^(4)km^(2),and 205.29×10^(4)km^(2),respectively.Only approximately 11%of the study area on the QTP is suitable for large-scale permanent construction land,and approximately 79.50%of the area is unsuitable under current economic and technological conditions.(2)The ECL adaptability index is 85.16%,85.93%,85.18%,and 78.01%during 1990–2020,respectively,with an average adaptability index exceeding 80%on the QTP.The ECL distribution generally conforms to construction land suitable space characteristics but with a significant spatial difference.(3)From 1990 to 2020,the maladaptive ECL was dominated by rural settlement land,transport land,and special land,with a rapidly increasing proportion of urban and other construction land.The maladaptive ECL is constrained by both elevation and slope in the southern Qinghai Plateau,the Hengduan Mountains,and the Qilia
The fluctuating planetary gravitational field influences not only activities on the Sun but also on the Earth. A special correlation function describes the harmonics of these fluctuations. Groups of earthquakes form oscillation patterns that differ significantly from randomly chosen control groups. These patterns are suitable as an element of an AI for the probability of earthquakes.
