Information Tupu provides a research method in the combination with spectrum, quantification and orientation for the regional land use spatial pattern and change. Taking Yuanyang County in Yunnan Province as an example, based on remote sensing, GIS spatial analysis and statistical analysis model, this paper establishes the information Tupu of land use spatial pattern and its change from the following three aspects: land use spatio-temporal change mode, spatial expansion process of land-use, and landscape characteristics. Then it analyzes the characteristics of these Tupu. The results show that: (1) The established land use spatio-temporal change premonition Tupu is more visually revealing the basic pattern of land use change in the study regions, and provides a spatio-temporal expression way.(2) The land use patch shapes and spatial expansion Tupu can provide the macroscopic and microscopic information of land use dynamic change. Through establishing mathematical models to analyze the expansion intensity and expansion patterns of various land use types, the land use spatial expansion process can be visualized, abstracted, and modeled. (3) Geographic information theory and landscape ecology theory are applied, using the VCM curve, to describe the spatial distribution features of different land-use type patches and the change of its spatial distribution features in different study periods.

No abstract

Purpose Due to rapid development, historic city areas are faced with urbanization damage to their characteristic urban identity besides physical deterioration and economic decay. The purpose of this paper is to address the following questions: What are the constituent elements of locality for historic areas? How does one classify historic areas according to locality elements? What are the characteristics of each kind of historic area? How does one identify to-be-protected locality elements according to different historic areas to realize sustainable development? Design/methodology/approach As a historic cultural city with a building history of over 3,000 years, Beijing has a myriad of distinctive historic areas, of which 367 were selected as the research samples. This paper classifies historic areas into the following four categories: distinctive areas, permanent areas, adaptive areas and inherited areas by analyzing the locality elements of 8,905 geo-tagged photos related to Beijing historic areas. The correlation among locality elements – the basis for joint protection – is also examined by Pearson’s correlation analysis. Findings The results are as follows: the reaction degree of carrier elements is generally higher than that of information elements, of which the representative architecture is the main constituent element of locality; folk customs, traditional activities and other intangible cultural heritage in historic areas receive only slight attention and need to be further stressed; controlled by non-human factors, permanent elements bear a high degree of autocorrelation; and emerging tourism and business activities have, to some extent, grown into constituent parts of the locality elements in historic areas. Originality/value This paper seeks to strike a dynamic balance between city renewal and historic area protection, providing a reference for understanding the dynamics of locality.

No abstract

Wildfires are an important disturbance factor in forest ecosystems. Assessing the probability of forest wildfires can assist in forest wildfire prevention, control, and supervision. The logistic regression model is widely used to forecast the probability, spatial patterns, and drivers of forest wildfires. This study used logistic regression to establish a spatial prediction model for forest wildfire susceptibility, which was applied to evaluate the risk of forest wildfires in Central Yunnan Province (CYP), China. A forest wildfire risk classification was implemented for CYP using forest burn scar data for 2001 to 2020 and the logistic spatial prediction model for forest wildfire susceptibility. Climate, vegetation, topographical, human activities, and location were selected as forest wildfire prediction variables. The results showed that: (1) The distributions of temperature, vegetation coverage, distance to water bodies, distance to roads, and precipitation were positively correlated with the occurrence of forest wildfires. Elevation, relative humidity, the global vegetation moisture index, wind speed, slope, latitude, and distance to residential areas were negatively correlated with the occurrence of forest wildfires. (2) The results of the logistic spatial prediction model for forest wildfire susceptibility showed a good fit to observed data, with an overall simulation probability of 81.6%. The optimal threshold for spatial prediction for forest wildfire susceptibility in CYP was determined to be 0.414. A significance level of a selected model variable of < 0.05 resulted in an area under the receiver operating characteristic curve (AUC) of 0.882–0.890. (3) Forest wildfire prevention efforts should focus on Southwest Yuxi City and southern Qujing City since they accounted for a high proportion of the areas at high risk of forest wildfires. Other localities should adjust forest wildfire prevention measures according to local conditions and strengthen existing wildfire prevention and emergency resource planning and allocation. (4) Some factors contributing to forest wildfires where different among the different areas. Forest wildfire risk factors had different degrees of impact under different spatial and temporal scales. The spatial relationships between wildfire disasters and influencing factors should be established in areas with heterogeneous environmental conditions for the selection of relevant factors.