Accurately simulating and predicting the urban expansion process, especially in expeditious urbanization areas, is an important aspect of managing limited land resources and adjusting flawed land use policies. This research was conducted on the basis of a high-temporal-resolution land use dataset to precisely model urban expansion in a rapidly developing zone by integrating the Artificial Neural Network (ANN), cellular automata (CA), and Markov Chain (MC). An urban suitability index (USI) map was created using ANN and fed to CA–MC to identify possible changed-to-urban cells. Two ANNs, multiple-layer perceptron (MLP) and long short-term memory network (LSTM), were implemented as simulation models for comparison. Due to its ability to capture more temporal information, LSTM outperformed MLP in modeling urban expansion dynamics over a short temporal interval. The simulated results were validated by (fuzzy) kappa simulation and the results revealed that the combination of ANN and CA–MC can precisely model the urban development locations due to its strength in revealing the nonlinear relationship between the expansion process and its drivers. The same model was applied to southern Auckland, and the compared results show that the most simulated variance is caused by the land use policies implemented by different types of governments.

Auckland experienced phenomenal expansion since 1841. This study assesses the pace of urban sprawl and its control over the natural environment and housing affordability. After the urban built-up area was mapped, its change over time was detected, and correlated with population. From 1842 to 2014 built-up area in Auckland grew from 48 ha to 50,531 ha. The pace of growth was 151 ha/year during 1842–1945 but jumped to 989 ha per annum during 1975–2001. It dropped to only 249 ha per annum in this century. This unchecked sprawl is a direct response to population growth and facilitated by improved transportation. Since the late 1990s urban built-up areas experienced a subdued expansion despite continued population growth. This curtailed sprawl is attributed to the contentious planning regulations implemented to curtail sprawl. Consequently, population density rose to 28 persons/ha, the highest since a century ago. Urban growth has reduced biomass and green fields with mean vegetation index dropped from 129.5 to 118.7 during 2002–2014 with a smaller standard deviation, suggesting the landscape is increasingly homogenized. House prices rose slowly when the growth potential decreased slowly and vice versa (r = − 0.925) while the number of vacant sections suitable for single dwellings declined. Thus, controlled urban sprawl is largely responsible for the skyrocketed price of sections and declined housing affordability.

The Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) and the Suomi National Polar-Orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light (NTL) data provide an adequate proxy for reflecting human and economic activities. In this paper, we first proposed a novel data processing framework to modify the sensor variation and fit the calibrated DMSP/OLS data and NPP/VIIRS data into one unique long-term, sequential, time-series nighttime-lights data at an accuracy higher than 0.950. Both the supersaturation and digital value range have been optimized through a machine learning based process. The calibrated NTL data were regressed against six socioeconomic factors at multi-scales using decision tree regression (DTR) analysis. For a fast-developing city in China—Chongqing, the DTR provides a reliable regression model over 0.8 (R²), as well explains the variation of factor importance. With the multi-scaled analysis, we matched the long-term time-series NTL indices with appropriate study scale to find out that the city and sub-city region are best studied using NTL mean and stander derivation, while NTL sum and standard deviation could be better applied the scale of suburban districts. The significant factor number and importance value also vary with the scale of analysis. More significant factors are related to NTL at a smaller scale. With such information, we can understand how the city develops at different levels through NTL changes and which factors are the most significant in these development processes at a particular scale. The development of an entire city could be comprehensively explained and insightful information can be produced for urban planners to make more accurate development plans in future.

The accurate prediction of path loss is essential for planning and optimizing communication networks, as it directly impacts the user experience. In 5G signal propagation, the mix of varied terrain and dense high-rise buildings poses significant challenges. For example, signals are more prone to multipath effects and occlusion and shadowing occur often, leading to high nonlinearities and uncertainties in the signal path. Traditional and shallow models often fail to accurately depict 5G signal characteristics in complex terrains, limiting the accuracy of path loss modeling. To address this issue, our research introduces innovative feature engineering and prediction models for 5G signals. By utilizing smartphones as signal receivers and creating a multimodal system that captures 3D structures and obstructions in the N1 and N78 bands in China, the study aimed to overcome the shortcomings of traditional linear models, especially in mountainous areas. It employed the XGBoost algorithm with Optuna for hyperparameter tuning, improving model performance. After training on real 5G data, the model achieved a breakthrough in 5G signal path loss prediction, with an R² of 0.76 and an RMSE of 3.81 dBm. Additionally, SHAP values were employed to interpret the results, revealing the relative impact of various environmental features on 5G signal path loss. This research enhances the accuracy and stability of predictions and offers a technical framework and theoretical foundation for planning and optimizing wireless communication networks in complex environments and terrains.