With the rapid development of China’s economy, the demand for labor in the coastal cities continues to grow. Due to restrictions imposed by China’s household registration system, a large number of floating populations have subsequently appeared. The relationship between floating populations and crime, however, is not well understood. This paper investigates the impact of a floating population on residential burglary on a fine spatial scale. The floating population was divided into the floating population from other provinces (FPFOP) and the floating population from the same province as ZG city (FPFSP), because of the high heterogeneity. Univariate spatial patterns in residential burglary and the floating population in ZG were explored using Moran’s I and LISA (local indicators of spatial association) models. Furthermore, a geographically weighted Poisson regression model, which addressed the spatial effects in the data, was employed to explore the relationship between the floating population and residential burglary. The results revealed that the impact of the floating population on residential burglary is complex. The floating population from the same province did not have a significant impact on residential burglary in most parts of the city, while the floating population from other provinces had a significantly positive impact on residential burglary in most of the study areas and the magnitude of this impact varied across the study area.

Culture and distance are two major factors for geographically segmenting tourists in tourism marketing and advertising. Previous empirical studies on the destination image, however, have examined extensively the effect of the culture while inadequately the effect of the distance, let alone comparing the effects of the two variables. Using social media data, this study compares the effect of distance-based segments of tourists with that of culture-based segments in producing diverse perceived images of a destination. From Sina Weibo data, 282,532 Chinese mainland tourists who visited Suzhou, China during 2012–2016 and their perceived destination images are extracted and analyzed. The main results include: 1) for distance-based segments, the image differences increased with distance and the short-haul tourists perceived a more comprehensive image than the long-haul tourists; 2) for culture-based segments, the image differences were clear and relatively complex, while tourists from Wuyue cultural region had similar image perceptions with the local visitors; 3) the q-statistic of the Geodetector method shows that the culture-based segmentation can explain 65.8% of image variations while the distance-based segmentation can explain 46.6% of image variations, suggesting that culture is a more appropriate variable to segment the tourism market.

Both rural to urban migration and urban crime are well researched topics in China. But few studies have attempted to explore the possible relationships between the two. Using calls for service data of ZG city in 2014, the Sixth Census data in 2010, this study examines relationships between migrants and crime by using structural equation models. Two hypotheses were tested: (1) the distribution of migrants has direct effects on the spatial distribution of burglaries, and (2) migrants also indirectly affect burglary rate through mediating variables such as residential mobility and socio-economic disadvantage of their resident communities. The results showed that migrants have significant direct and indirect effects contributing to burglaries, although the indirect effect is much larger than the direct effect, indicating that community characteristics play a more important role than the migrants themselves.

The importance of combining spatial and temporal aspects has been increasingly recognized over recent years, yet pertinent pattern analysis methods in place-based crime research still need further development to explicitly indicate spatial-temporal localities of pertinent factors’ influence ranges. This paper proposes an approach, Spatial-Temporal Indication of Crime Association (STICA), to facilitate identifying the main contributing factors of crime, which are operated at diverse spatial-temporal scales. The method’s rationale is to progressively discern the spatial zones with diverse temporal crime patterns. A specific implementation of the STICA approach, by combining kernel density estimation, k-median-centers clustering, and thematic mapping, is applied to understand the burglary in an urban peninsula, China. The empirical findings include: (1) both the main time-stable and time-varying factors of crime can be indicated with the disparities of temporal crime patterns for different spatial zones based on the STICA results. (2) The spatial range of these factors can enlighten the understanding of interactions for generating crime patterns, especially with regards to how temporally transient and spatially global factors can produce a locally crime-ridden zone through the mediation of stable factors. (3) The STICA results can reveal the spatially contextual effects of stable factors, which are of great value to improve modeling crime patterns. As demonstrated, the STICA approach is effective in exploring contributing factors of crime and has shown great potential for providing a new vision in place-based crime research.