As urbanization continues to advance globally, the issue of “urban ailments” has become increasingly prominent. To promptly identify problems arising from urban development, it is crucial to investigate not only the quality of human habitats at the city and regional scales but also the micro-level living environments. Indeed, studying residential living conditions enables the specific problems within urban planning to be unearthed, facilitating timely adjustments for the improvement of urban habitation. However, a precise and objective methodology for accurately measuring the quality of residential living environments is still lacking. In recent years, the urban renewal concept has proven to be proactive in enhancing the living environment quality of residential areas. In this study, we focus on residential areas within Hangzhou’s Binjiang District, China, and integrate diverse datasets including real estate websites, digital mapping platforms, remote sensing imagery, points of interest (POIs), and land-use planning data. By examining and analyzing the urban renewal concept, we establish a comprehensive set of evaluation indicators for the living environment quality in residential areas, including five aspects: residential and environmental factors; transportation and communication; education and culture; lifestyle and leisure; and healthcare and well-being. Subsequently, a holistic assessment of the Binjiang District is conducted. Our research findings demonstrate that in the context of urban renewal, the proposed living environment quality evaluation method, based on multi-source data, exhibits significantly higher practicality and effectiveness. The residential environment in Binjiang District exhibits a spatial pattern with higher quality in the northern regions and lower quality in the southern regions. Across different dimensional layers, the residential and environmental quality shows a higher trend along the river areas and a lower trend in the internal areas, as well as higher quality in newly developed sections compared to older ones. The transportation and communication quality exhibits a decreasing trend radiating from multiple core areas. In other dimensions, there is a concentration of high-quality residential areas in the administrative vicinity and surrounding Binjiang University Town. Hotspot analysis further substantiates a significant spatial correlation between the quality of the living environment and the degree of agglomeration, highlighting a positive relationship between the two factors. This study provides a solid basis for the spatial planning of urban public service facilities and holds significant research and practical value.

From the perspective of household economy, the application of a multiscale spatial econometric model to realize the objective evaluation of county-level poverty alleviation stability is a core issue in rural economics research. The improvement of economic income and livelihood conditions for small farm holders are significant manifestations of poverty alleviation stability. Quantitative evaluation of the county-level poverty alleviation stability can provide a scientific basis for the adjustment of rural economic policy and high-quality development of regional economy by the multiscale spatial econometric model. This study realizes the quantitative evaluation of county-level poverty alleviation stability by constructing the evaluation index system, taking five counties in China’s Yunnan Province as an example, using the exact 2242 survey datasets, and adopting the multiscale spatial econometric model. The main idea of the model is to obtain the score of poverty alleviation stability by weighted summing of dimensions on the basis of weight calculation of each evaluation index. Results revealed the following: (1) County-level poverty alleviation stability includes the stability of regional poverty alleviation and the stability of farmers’ poverty alleviation, which is mainly affected by the combined effect of five factors, including economic and ability status, cognitive level, supporting facilities, and social governance. (2) Based on the multiscale spatial econometric model, the overall poverty alleviation stability in the five counties is relatively low, with Zhaoyang District showing the highest stability, followed by Yiliang, Yuanyang, Honghe, and Gongshan. (3) Farmers’ poverty alleviation stability in all counties, except Zhaoyang, is higher than that in the region. County-level gross domestic product and fiscal revenue are the dominant factors affecting the stability of poverty alleviation in the region, while the dominant factors affecting the farmers’ poverty alleviation stability are the level of per capita net income and labor force proportion in the household population. (4) To enhance poverty alleviation stability, this study suggested enhancing the level of economic development in counties and strengthening the collective economy of the village, innovating the form of economic development of the village, taking the enhancement of the development capacity of counties, relying on resource advantages to actively develop special industries, and improving the stability and sustainability of income generation for farmers. Meanwhile, we propose to further improve the conditions of regional infrastructure and enhance the capacity of public services. The findings can help enrich the theoretical research system of rural economics, expand the scope of research on small-holder farming systems, and provide a reference for diversification of small farm holders economy, the improvement of agricultural farming technology, and the high-quality development of regional economy in China.

From the perspective of tourism space optimization, the application of computer technology in creating computational simulations of correlation effects in tourism space systems is a core issue in research related to ports and hinterlands. Using a computer simulation analysis of the gray correlation, taking Mohan port–Yunnan economic hinterland as an example, the relationship between Mohan port and the Yunnan economic hinterland was quantitatively measured based on the indicators of cross-border tourism from 2006 to 2020. The study aimed to identify the driving mechanisms behind the synchronized development of Mohan port–Yunnan economic hinterland. The results are as follows: (1) due to the influence of administrative interventions and the competition of the neighboring ports, the correlation between the Mohan port and the Yunnan hinterland from 2006 to 2020 showed a rising–falling trend; (2) the correlation between the Mohan port and Xishuangbanna prefecture showed an obvious fluctuating trend, and the original port–city development relationship evolved to a competitive status; (3) the degree of spatial correlation of the Mohan port–Yunnan hinterland system evolved in a north–south-central–south direction, with “border zone–central region–northern region” distribution characteristics; (4) the natural conditions of the location, national policies, competition of nearby ports, infrastructure and traffic conditions, and economic strength are the main driving factors affecting the correlation change between Mohan port and the Yunnan hinterland. These findings can help enrich the theoretical research system of buildings economics, and expand the application of computational decision-making support in tourism spatial optimization.