Chongqing is a large municipality in southwestern China, having the characteristics of a vast jurisdiction, complex topography, and a prominent dual urban–rural structure. It is vitally important to optimize the spatial layout of land and efficiency of natural resource allocation, achieve sustainable development, and conduct influence assessment and causation analysis in this region. Here, using the Google Earth Engine platform, we selected Landsat remote-sensing (RS) images from the period 2000–2020 and constructed a remote-sensing ecological index (RSEI) model. Considering the urban spatial pattern division in Chongqing, the Sen + Mann–Kendall analytical approach was employed to assess the fluctuating quality of the ecological environment in different sectors of Chongqing. Subsequently, single-factor and interaction detectors in the Geodetector software tool were used to conduct causation analysis on the RSEI, with the use of eight elements: elevation, slope, aspect, precipitation, temperature, population, land use, and nighttime lighting. Findings indicate that, over the course of the investigation period, the eco-quality in Chongqing displayed a pattern of degradation, succeeded by amelioration. The RSEI decreased from 0.700 in 2000 to 0.590 in 2007, and then gradually recovered to 0.716 in 2018. Overall, the eco-environment quality of Chongqing improved. Spatially, changes in the RSEI were consistent with the planning and positioning of the urban spatial pattern. The main new urban area and periphery of the central urban area showed a slight deterioration, while other regions showed marked improvement. The combined effect of any two elements enhanced the explanatory power of a single factor, with elevation, temperature, and land use being the strongest explanatory elements of eco-quality in Chongqing. The most influential factor explaining the spatial variation of the RSEI was determined to be the combined impact of elevation and land use. At the temporal scale, elements related to human activities showed the most evident trend in explanatory power.

Determining the spatial relations between objects is a primary function of a geographic information system (GIS). One important aspect of spatial relations is topological relations, which remain constant under topological transformations. Describing the geometry of a spatial object using the OpenGIS Simple Features Specification requires only simple features: the interior, boundary and exterior of a spatial object are defined. This paper proposes a comprehensive model, the 27-intersection model (27IM), which considers both the dimensions and the number of intersections. Some propositions are presented to exclude relations that the 27IM cannot implement. The 27IM describes six groups of topological relations: point/point, point/line, point/region, line/line, line/region and region/region. The formalism of the 27IM and the corresponding geometric interpretations between spatial objects are illustrated and then compared to the topological relations represented by the existing models, the nine-intersection model (9IM), the dimensionally-extended nine-intersection matrix (DE-9IM) and the separation number extended nine-intersection matrix (SNE-9IM). The results show that (1) the 27IM can represent the topological relations between two-dimensional spatial objects, (2) the 27IM can distinguish more topological relations than can the 9IM, DE-9IM or the SNE-9IM and that (3) the interoperability of the 27IM with the 9IM, DE-9IM and SNE-9IM is good.

Investigating regional connections and their influencing factors from the perspective of “flow” space is one of the foundations of promoting regional development. In this article, the data we used includes actual highway passenger flow data, National Polar-orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light remote sensing data, and socioeconomic data. We analyzed the spatial distribution pattern, connection intensity and spatial autocorrelation of highway passenger flow in Chongqing during the working day, weekend and May Day and revealed the influencing factors by means of a geographic detector. Three key conclusions resulted from this research. First, highway passenger flow in Chongqing districts exhibits spatial agglomeration that is clearly higher in western Chongqing than in eastern Chongqing and forms an obviously dual-core “star” structure, with the main urban area and Wanzhou serving as the core. Second, a factor detector notes that the nighttime light area index (0.9251, 0.9512, 0.9541) has the strongest explanatory power for the spatial differentiation of passenger traffic in Chongqing districts, which is the key factor. Third, interaction detection shows that the interaction between the two factors displays an enhancement effect at different times. The nighttime light area index shows the strongest explanatory power under the synergy of tourist attractions, which are 0.9850, 0.9903 and 0.9908. But the per capita GDP and highway mileage have the most obvious enhancement effect after interaction (0.9544, 0.9661, 0.9652). Therefore, in future planning and development, we should pay attention to cooperation and exchanges between districts and use the nighttime light area index as an important reference factor to provide a scientific basis for the construction of public transport and economic construction in Chongqing.

Rapid urbanization often exerts massive pressure on the resources relied upon by the ecological environment. It is necessary to quickly evaluate the interaction and mutual influence between regional urbanization and the ecological environment. This paper uses the Google Earth Engine (GEE) platform, integrates MODIS and night light remote sensing data sets, and computes the remote sensing-based ecological index (RSEI) and the coupling coordination degree (CCD) to measure the coupling coordination and analyze the spatiotemporal changes in the Chengdu–Chongqing Economic Circle (CCEC) for 2010, 2015, and 2020. Our results demonstrate four key findings. Firstly, the CCD varies spatially; it peaks at the Chengdu and the West Chongqing Plains, decreasing outwards along the mountains, with the lowest degree of coupling in the central, southern, and northern edge areas of the CCEC. Additionally, it has shown a trend of maintaining unchanged first and then increasing, mainly responding to policy decisions. Secondly, the changes between the different coupling levels were almost stable and mainly occurred between adjacent levels. Thirdly, the coupling level of towns spreads outwards from the centers at Chengdu and Chongqing and has an overall upward trend in time. Fourthly, in the most recent year, the coupling types present a distribution pattern of one developing axis connected with two peaks. Specifically, the environment system lagging type aggregates in Chengdu, Chongqing, and their surrounding areas, and the others mainly are economic system lagging type. The high internal coupling type also mainly occurs in the high and low coupling levels. Under this context, constructive suggestions for developmental optimization in the study area were proposed.