Background There is a sharp contradiction between the supply and demand of medical resources in the provincial capitals of China. Understanding the spatial patterns of medical resources and identifying their spatial association and heterogeneity is a prerequisite to ensuring that limited resources are allocated fairly and optimally, which, along with improvements to urban residents’ quality of life, is a key aim of healthy city planning. However, the existing studies on medical resources pattern mainly focus on their spatial distribution and evolution characteristics, and lack the analyses of the spatial co-location between medical resources from the global and local perspectives. It is worth noting that the research on the spatial relationship between medical resources is an important way to realize the spatial equity and operation efficiency of urban medical resources. Methods Localized colocation quotient (LCLQ) analysis has been used successfully to measure directional spatial associations and heterogeneity between categorical point data. Using point of interest (POI) data and the LCLQ method, this paper presents the first analysis of spatial patterns and directional spatial associations between six medical resources across Wuhan city. Results (1) Pharmacies, clinics and community hospitals show “multicentre + multicircle”, “centre + axis + dot” and “banded” distribution characteristics, respectively, but specialized hospitals and general hospitals present “single core” and “double core” modes. (2) Overall, medical resources show agglomeration characteristics. The degrees of spatial agglomeration of the five medical resources, are ranked from high to low as follows: pharmacy, clinic, community hospital, special hospital, general hospital and 3A hospital. (3) Although pharmacies, clinics, and community hospitals of basic medical resources are interdependent, specialized hospitals, general hospitals and 3A hospitals of professional medical resources are also interdependent; furthermore, basic medical resources and professional medical resources are mutually exclusive. Conclusions Government and urban planners should pay great attention to the spatial distribution characteristics and association intensity of medical resources when formulating relevant policies. The findings of this study contribute to health equity and health policy discussions around basic medical services and professional medical services.

Forests play a vital role in sequestering carbon dioxide from the atmosphere. Vegetation phenology is sensitive to climate changes and natural environments. Exploring the patterns in phenological events of the forests can provide useful insights for understanding the dynamics of vegetation growth and their responses to climate variations. Deciduous forest in North America is an important part of global forests. Here we apply time-series remote sensing imagery to map the critical dates of vegetation phenological events, including the start of season (SOS), end of season (EOS), and growth length (GL) of the deciduous forests in North America during the past two decades. The findings show that the SOS and EOS present considerable spatial and temporal variations. Earlier SOS, delayed EOS, and therefore extended GL are detected in a large part of the study area from temporal trend analysis over the years, though the magnitude of the trend varies at different locations. The phenological events are found to correlate to the environmental factors and the impact on the vegetation phenology from the factors is location-dependent. The findings confirm that the phenology of the deciduous forests in North America is updated such as advanced SOS and delayed EOS in the last two decades and the climate variations are likely among the driving forces for the updates. Considering that previous studies warn that shifts in vegetation phenology could reverse the role of forests as net emitters or net sinks, we suggest that forest management should be strengthened to forests that experience significant changes in the phenological events.

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Involving stakeholders in decision-making at planning workshops requires a computer system to respond quickly to various scenarios proposed by participants. Due to the increasing complexity of urban systems, such planning support often faces the challenges of "big data" and poor computational performance. This paper proposes a new approach using parallel processing techniques (i.e. MPI Message Passing Interface) to support workshop participants in interactively building planning scenarios and visualizing outputs of job accessibility across the Greater Manchester. MPI-based parallel algorithms have been run on a cluster of computers for reducing computational time cost. The tested results and computational performances are critically evaluated and recommendations for future work provided. Particularly this paper also addresses several key research questions related to a theoretical framework of applying big data analytics for urban planning support.