The main purpose of this paper is to use regression models to explore the factors affecting housing prices as well as apply spatial aggregation to explore the changes of urban space prices. This study collected data in Taitung City from the year 2013 to 2017, including 3533 real estate transaction price records. The hedonic price method, spatial lag model and spatial error model were used to conduct global spatial self-correlation tests to explore the performance of house price variables and space price aggregation. We compare the three models by R² and Akaike Information Criterion (AIC) to determine the spatial self-correlation ability performance, and explore the spatial distribution of prices and the changes of price regions from the regional local indicators of spatial association spatial distribution map. Actual analysis results show an improvement in the ability to interpret real estate prices through the feature price mode from the R² value assessment, the spatial delay model and the spatial error model. Performance from the AIC values show that the difference of the spatial delay model is smaller than that of the feature price model and the spatial model, demonstrating a better performance from the space delay model and the spatial error model compared to the feature price model; improving upon the estimation bias caused by spatial self-correlation. For variables affecting house pricing, research results show that Moran’s I is more than 0 in real estate price analysis over the years, all of which show spatial positive correlation. From the LISA analysis of the spatial aggregation phenomenon, we see real estate prices rise in spaces surrounded by high-priced real estate contrast with the scope of space surrounded by low-cost real estate shifting in boundary over the years due to changes in the location and attributes of real estate trading transactions. Through the analysis of space price aggregation characteristics, we are able to observe the trajectory of urban development.

Long-term spatiotemporal mapping of landslides is crucial for understanding the dynamics of landslide, their impact on forest carbon stocks, and their interactions with environmental factors, climate variability, and disturbances. This study analyzed 33 years (1990-2022) of Landsat imagery and topography using machine learning (Random Forest) to map landslide dynamics in a 24,386-ha subtropical montane forest in Northeast Taiwan. We also quantified forest aboveground biomass (AGB) losses from landslides using temporally corresponding Landsat and lidar data. We observed pronounced interannual variability, with total landslide coverage ranging from 0.68% to 3.19%, and forest-to-landslide transitions driving annual AGB losses of 2 to 85 Gg yr⁻¹. Temporal analysis revealed exponential declines in landslide frequency (median = 2 events), persistence (one year), and reoccurrence (two times), indicating most landslides were short-lived. However, nearly half of affected sites reoccurred multiple times, indicating spatially persistent susceptibility. Topographic attributes, including elevation, aspect, slope, and local relief, exhibited greater sensitivity to extreme events. Crucially, typhoon-driven extreme rainfall, particularly daily maximum precipitation (r = 0.559, p = 0.004), showed a stronger relationship with newly formed landslides than daily maximum precipitation during the rainy season (r = 0.399, p = 0.026), emphasizing typhoon’s dominant triggering role. AGB losses from typhoon-triggered landslides were roughly 14-fold greater than in quiet years, profoundly impacting the regional forest carbon budget. Post-landslide vegetation recovery exhibited a highly variable trajectory and plateaued at ~63% of pre-disturbance biomass within 25 years, based on a non-linear asymptotic model. As climate change is projected to intensify typhoon activity and extreme rainfall, landslide risks and associated forest carbon losses will increase, particularly in vulnerable, typhoon-prone regions like Asia. These findings highlight typhoons are not only a principal driver of landslide activity but also a major disruptor of forest carbon budgets, underscoring their critical inclusion in carbon accounting frameworks for vulnerable montane ecosystems.