Based on the research on the current situation and transformation path of the spatial construction of villages and towns in northern Jiangsu, “compactness” and “convenience” are extracted as the elements of spatial evaluation. With multi-source data, comprehensively using ENVI remote sensing image interpretation, GIS spatial analysis, Fragstats landscape index calculation, entropy weight–TOPSIS comprehensive evaluation, and SPSS cluster analysis, a “digital full cycle” of a research framework for the spatial evaluation of villages and towns is built. In this paper, Suining County is taken as the research object, and the spatial construction level of its villages and towns is studied. The research results show that at the county level, the spatial compactness of villages and towns roughly presents the characteristics of an “X” pattern, decreasing from the middle to the four sides, while facility convenience generally presents the characteristics of a right “人” (Chinese character) pattern. At the town level, facility convenience basically presents the pattern characteristics of the “center-node” differentiation structure. The research aims to guide villages and towns to solve the current dilemma of spatial construction, promote the construction of digital villages and towns, and impel the digital transformation of the village and town evaluation system, data, and methods, so as to provide real-time, quantitative, and accurate data and method support for planning and decision-making in villages and towns.

[Purpose] With the acceleration of China’s urbanization process and the construction of the metropolitan area, the polarization phenomenon (Matthew effect) between cities and cities, cities and villages, and villages and villages has become increasingly prominent, and the relationship between urban and rural construction elements such as economy, society, nature, and population has become increasingly complex. However, due to the huge differences between cities and villages, it is difficult to use a unified “measuring ruler” to compare them horizontally and vertically in the space–time dimension. Therefore, it is necessary to evaluate and measure the spatial vitality of each administrative unit in the region from the perspective of urban development, that is, the sustainable development capacity of space. [Method] Taking Lixiahe Plain as the research object, based on the multi-source data such as POI, night-time light, and land use/cover, on the one hand, the spatial vitality evaluation system of the complex ecosystem is constructed, and the vitality levels and types of different township units are divided. On the other hand, the coupling degree model, coupling degree coordination degree model, spatial correlation analysis, and exploratory space–time data analysis are used to analyze the complex relationship and dynamic evolution characteristics of interaction stress among the spatial vitality of township units. [Conclusion] (1) The spatial vitality status is generally good, but there are great differences among regions, and the trend of fluctuant declines from west to east, and the spatial vitality type is a mainly vigorous type and development type. (2) There is a strong spatial dependence among the subsystems of the township units in the region. The spatial structure of the township units in the central area is more dynamic, while the spatial structure of the surrounding towns is more stable. (3) The 136 township units are divided into 4 different types of villages and towns, namely, coordinated evolution type, maladjustment evolution type, overall invariant type, and stable invariant type, which is conducive to putting forward their own development strategies.

In the process of deepening the strategy of rural revitalization in China, the transformation of rural houses is an important part of promoting rural revitalization. Rural houses are the living places of the majority of rural residents, and there have been problems of poor thermal comfort and high energy consumption in the Xuzhou area for a long time. At present, the starting point of domestic research on the transformation of rural houses is mostly single-objective optimization of thermal comfort or energy conservation, and few studies involve these two aspects at the same time. In this study, based on field investigation, questionnaire surveys, software simulation, orthogonal testing, and other research methods, an adaptive thermal comfort model for rural residents in the Xuzhou area was established, and the factors affecting the indoor thermal comfort and energy consumption of the rural houses were simulated. In order to jointly improve indoor thermal comfort and reduce energy consumption in rural houses, an optimal transformation scheme suitable for existing rural houses in the Xuzhou area was proposed. By comparing the data of indoor thermal comfort and annual energy consumption of a typical rural house before and after the transformation, and then estimating the cost required for the transformation of the house, the advantages of the transformation scheme in terms of effectiveness and economy were verified.

Against the backdrop of urban renewal, the transformation and functional enhancement of Existing Urban Industrial Areas (EUIAs) play a crucial role. Focusing on the rational development of underground space in EUIAs, this study explores forecasting methods for the development demand of such underground space, aiming to alleviate the contradiction between the protection of industrial heritage and intensive land use in EUIAs. This paper systematically sorts out the forecasting methods for the scale demand of underground space. Firstly, through a literature review, two major categories of factors influencing underground space demand—driving factors and conditional factors—are summarized, and an indicator system consisting of 23 indicators is constructed. On this basis, the modified Delphi method is employed to screen 7 dominant indicators, including the protection value of industrial heritage, the spatial distribution of industrial heritage, existing underground space, development functions, rail transit, spatial location, and surrounding supporting facilities. Based on the matrix of industrial heritage protection levels and land use nature, the development potential of underground space is evaluated, and a demand level correction model is introduced. Demand intensity is quantified through expert experience-based assignment with reference to typical domestic cases, thereby establishing a demand forecasting model for the underground space scale in EUIAs. Finally, the model is applied to the Saint-Gobain Industrial Area. Through the analysis of its industrial heritage value assessment, land use planning, and location characteristics, the areas with demand for underground space are delineated and their levels are corrected, forecasting a total underground space demand of 224,600–454,600 m². The research results provide a theoretical basis and methodological support for the underground space planning of EUIAs, and offer references for the development practice of similar regions.