Search Results (4)

A scientific foundation for the sustainable development of ecosystems and the improvement of the ecological spatial security pattern in the area is provided by carrying out a scientific assessment of ecological risk levels in coastal counties. An ecological risk assessment model was established in Pulandian district, Dalian, based on four periods of land use data from 1990 to 2020 combined with the PSR model. The spatial and temporal evolution of ecological risk in Pulandian was analyzed on this basis, and an exploratory regression analysis and a geographically weighted regression model were then used to explore the driving role of natural and social factors on comprehensive ecological risk in coastal counties. The findings demonstrate that there is an obvious ecological landscape type of conversion, with the majority of arable land being converted to forest land in northcentral and southwest areas, reflecting an improvement in the ecological environment and air quality, and the majority of coastal beach land being converted to construction land among the volumes transferred out and in. The area of high risk increased by 73.17% during the course of 30 years, with the majority of it concentrated in the research areas southern Fengrong, Tiexi, and Taiping regions in 1990 before expanding to the northeast, southeast, and central regions. The status index and response index both show a decline followed by an increase in change, while the pressure index shows a rising tendency with socioeconomic progress. The comprehensive ecological risk in the study area is significantly influenced by the urbanization rate, the ratio of environmental protection investment to GDP, the ecosystem service index, and the ecological space–land use ratio, with the urbanization rate displaying more overt negative correlation-driving characteristics, the ratio of environmental protection investment to GDP displaying significant spatial division characteristics, and the ecological space–land use ratio being an important factor. The findings serve as a foundation for decisions on ecological risk avoidance, control, and construction in Pulandian. Full article

Taking Dalian, a typical square city in China, as an example based on data from remote sensing images, questionnaires, spatial statistics, social economy, etc., 48 squares in the main districts were constructed from the perspective of human settlements in order to build five systems: nature, humanity, society, residence and support. The aim was to explore the spatio-temporal differentiation characteristics and their driving mechanism. The results show the following: (1) The index system was constructed based on the human settlements perspective, and PCA was used to comprehensively evaluate it. Four principal component factors were extracted, and their cumulative contribution rate is 78.701%. On this basis, city squares were divided into four types: comprehensive square, recreational square, commercial service square and traffic square. (2) Using Mapinfo to visualize the square space, and taking the People’s Square as the center, the squares from the Tsarist Russia and Japanese colonial rule time periods were mainly distributed within 5 km, mostly in the direction of NE-SEE. During the construction of New China, city squares were distributed in all directions of the city, mainly between NE-SE and NNW-SSW. (3) ArcGIS was used to create an analysis chart of square service scope. Compared with 1999, it was more concentrated in central cities in 2016, and the service scope was relatively small. However, a square with high popularity has a wider influence. (4) The formation and evolution of the spatial pattern of city squares are affected by many factors, such as nature, economy, society, politics, ecological environment and technology. In the planning and development of city squares, Dalian should pay full attention to human settlements perpectives and add luster to the development of livable cities. Full article

Vitality can effectively test the quality of regional space, put forward the concept of urban human settlement vitality, and explore the development status of urban human settlement vitality space, which is of great significance in promoting the high-quality development of urban human settlements. By constructing an evaluation index system of urban human settlement vitality and comprehensively using projection pursuit models, spatial correlation analysis, and spatial measurement models, the spatial pattern and influencing factors of the vitality of urban human settlements in the four districts of Dalian were studied. The results are as follows: (1) The spatial differentiation characteristics of the vitality of urban human settlements in Dalian are remarkable. Overall, it gradually decreased from the city center to the administrative boundary. (2) The spatial dependence of the vitality of urban human settlements among regions is relatively strong, with a more obvious “Matthew effect”. Among them, urban human settlement vitality hot spots were mainly distributed in the southeast of Dalian, showing a concentrated distribution trend, while the cold spots were distributed in the northern fringe area of Dalian, with spatial homogeneity characteristics. (3) Topography, ecological environment, social economy, commercial development, spatial structure, spatial form, regional scale, etc. have different impacts on the vitality of urban human settlements. Full article

Since 2019, the novel coronavirus has spread rapidly worldwide, greatly affecting social stability and human health. Pandemic prevention has become China’s primary task in responding to the transmission of COVID-19. Risk mapping and the proposal and implementation of epidemic prevention measures emphasize many research efforts. In this study, we collected location information for confirmed COVID-19 cases in Beijing, Shenyang, Dalian, and Shijiazhuang from 5 October 2020 to 5 January 2021, and selected 15 environmental variables to construct a model that comprehensively considered the parameters affecting the outbreak and spread of COVID-19 epidemics. Annual average temperature, catering, medical facilities, and other variables were processed using ArcGIS 10.3 and classified into three groups, including natural environmental variables, positive socio-environmental variables, and benign socio-environmental variables. We modeled the epidemic risk distribution for each area using the MaxEnt model based on the case occurrence data and environmental variables in four regions, and evaluated the key environmental variables influencing the epidemic distribution. The results showed that medium-risk zones were mainly distributed in Changping and Shunyi in Beijing, while Huanggu District in Shenyang and the southern part of Jinzhou District and the eastern part of Ganjingzi District in Dalian also represented areas at moderate risk of epidemics. For Shijiazhuang, Xinle, Gaocheng and other places were key COVID-19 epidemic spread areas. The jackknife assessment results revealed that positive socio-environmental variables are the most important factors affecting the outbreak and spread of COVID-19. The average contribution rate of the seafood market was 21.12%, and this contribution reached as high as 61.3% in Shenyang. The comprehensive analysis showed that improved seafood market management, strengthened crowd control and information recording, industry-catered specifications, and well-trained employees have become urgently needed prevention strategies in different regions. The comprehensive analysis indicated that the niche model could be used to classify the epidemic risk and propose prevention and control strategies when combined with the assessment results of the jackknife test, thus providing a theoretical basis and information support for suppressing the spread of COVID-19 epidemics. Full article