Spatial roost networks and resource selection of female wild turkeys.

Wildlife demography is influenced by behavioural decisions, with sleep being a crucial avian behaviour. Avian species use roost sites to minimize thermoregulation costs, predation risk and enhance foraging efficiency. Sleep locations are often reused, forming networks within the home range. Our study, focusing on female eastern wild turkeys (Meleagris gallopavo silvestris) during the reproductive season, used social network analysis to quantify both roost site selection and network structure. We identified roost networks which were composed of a small percentage of hub roost sites connecting satellite roosts. Hub roosts were characterized by greater values of betweenness (ß = 0.62, s.e. = 0.02), closeness (ß = 0.59, s.e. = 0.03) and eigenvalue centrality (ß = 1.15, s.e. = 0.05), indicating their importance as connectors and proximity to the network's functional centre. The probability of a roost being a hub increased significantly with greater eigenvalue centrality. Female wild turkeys consistently chose roost sites at lower elevations and with greater topographical ruggedness. Hub roost probability was higher near secondary roads and further from water. Our research highlights well-organized roost site networks around hub roosts, emphasizing the importance of further investigations into how these networks influence conspecific interactions, reproduction and resource utilization in wild turkeys.

4D dynamic spatial brain networks at rest linked to cognition show atypical variability and coupling in schizophrenia.

Despite increasing interest in the dynamics of functional brain networks, most studies focus on the changing relationships over time between spatially static networks or regions. Here we propose an approach to study dynamic spatial brain networks in human resting state functional magnetic resonance imaging (rsfMRI) data and evaluate the temporal changes in the volumes of these 4D networks. Our results show significant volumetric coupling (i.e., synchronized shrinkage and growth) between networks during the scan, that we refer to as dynamic spatial network connectivity (dSNC). We find that several features of such dynamic spatial brain networks are associated with cognition, with higher dynamic variability in these networks and higher volumetric coupling between network pairs positively associated with cognitive performance. We show that these networks are modulated differently in individuals with schizophrenia versus typical controls, resulting in network growth or shrinkage, as well as altered focus of activity within a network. Schizophrenia also shows lower spatial dynamical variability in several networks, and lower volumetric coupling between pairs of networks, thus upholding the role of dynamic spatial brain networks in cognitive impairment seen in schizophrenia. Our data show evidence for the importance of studying the typically overlooked voxel-wise changes within and between brain networks.

Dynamic impact assessment of urban floods on the compound spatial network of buildings-roads-emergency service facilities.

There are complex interactions among urban spatial elements in rainstorm scenarios. Road interruptions may cause isolation between partially submerged buildings and emergency service facilities, thereby affecting the recovery capability of flooded buildings and the accessibility of emergency service facilities. This study constructed a compound spatial network of urban buildings-roads-emergency service facilities, and analyzed the complex dynamic impacts of time-varying dynamic floods on building risk, road risk, and emergency service accessibility. Firstly, a refined flood risk assessment at the building scale was carried out in combination with flood inundation, building vulnerability, population, vulnerable areas, underground buildings, and life facilities. Secondly, betweenness centrality indicator in complex networks was used to calculate road traffic capacity and collapse threshold, and the accessibility of emergency service facilities is calculated based on road traffic capacity. Finally, the interaction and feedback relationships between the compound spatial network were analyzed. The results show that: (1) Flooded building comprehensive risk analysis reveals that floods have a significant impact on residential and commercial land use, and high-risk flood areas are mainly concentrated in the central region of study area. (2) The road network has a collapse threshold, and the fluctuating state of emergency service accessibility is significantly influenced by road traffic capacity. (3) Roads act as "bridges" connecting buildings and emergency service facilities, leading to the redistribution of building flood comprehensive risk and emergency service accessibility, presenting complex dynamic changes over time. Additionally, submerged roads also affect the accessibility of emergency service facilities around flooded buildings, reducing the recovery capacity of flooded buildings, and exhibiting significant heterogeneity characteristics. Future research should consider the complex impacts of floods on urban elements in order to better manage dynamic flood risk.

What are the factors affecting primary care choice when the access under health insurance scheme is limited?: a cross-sectional study in Bandung, Indonesia.

BACKGROUND: Ensuring equal access to primary care services is crucial, as the gateway to a higher level of care. Indonesia has been trying to increase financial access to medical care by administering national health insurance known as BPJS-Health (Badan Penyelenggara Jaminan Sosial Kesehatan) since 2014. However, BPJS-Health beneficiaries can only use their benefits at a limited number of registered primary care providers (BPJS-Health partners). This study investigated the geographical coverage of BPJS-Health and BPJS-Health beneficiaries' primary care choices, based on their characteristics and healthcare preferences in the target areas of Bandung, Indonesia. METHODS: The setting of this cross-sectional study was the areas with low physical access to BPJS-Health partners but high physical access to non-BPJS-Health partners. Physical access was determined by spatial network analysis, resulting in a geographical coverage map. A total of 216 adults were recruited and they completed the questionnaire about their primary care choice. All participants had been registered with the BPJS-Health system and living in the study areas. Their participation in non-BPJS-Health was also evaluated. Participants' choice of care was assessed in three different scenarios, when the individual was experiencing mild, chronic, and serious illnesses. RESULTS: BPJS-Health partners' geographical coverage was unequally distributed in Bandung. Being registered with non-BPJS-Health company was negatively associated with the more frequent choice of using BPJS-Health partners' services (AOR = 0.18; 95% CI, 0.06-0.58, P = 0.004) among BPJS-Health beneficiaries. For serious illnesses, having a high income was associated with choosing non-BPJS-Health partners and hospitals (AOR = 4.90; 95% CI, 1.16-20.77, P = 0.031). When dealing with mild and chronic illnesses, participants were concerned about the quality of treatment they would receive as a major factor in choosing a primary care provider. However, receiving better treatment quality was negatively associated with choosing BPJS-Health partners in all cases of illness severities. CONCLUSIONS: Sociodemographic characteristics, healthcare preference factors, and health insurance status were associated with participants' primary care choices in the target areas of Bandung, Indonesia. BPJS-Health partners' coverage map and the preference factors are potentially important for policymakers, especially for the development of future BPJS-Health partnerships.

Spatial network and influencing factors of green water use efficiency in the YREB: considering carbon emissions and pollution indicators.

Improving green water use efficiency is based on the synergy of reducing pollutants and carbon emissions, and effectively identifying its spatial development structure is a prerequisite for realizing the green transformation of water management. This paper adopted the slacks-based measure- "energy-environment-economy" (SBM-3E) model with carbon emission and pollution indicators as unanticipated outputs to measure the green water use efficiency of the 11 provinces (cities) in the Yangtze River Economic Belt from 2000 to 2018, then analyzed the efficiency network structure evolution by using the social network analysis method, and finally examined the drivers of efficiency networks. The results show that (1) the value of green water utilization efficiency decreases from 0.78 to 0.51, and its spatial distribution is downstream > middlestream > upstream, with the values of 0.41, 0.61, and 0.86, respectively. (2) The overall spatial network efficiency is gradually interconnected, with the density increasing from 0.32 to 0.6; it has undergone a structural evolution from "flat" to "inverted tower." The core-edge structure of the main body status within the basin has been strengthened. (3) The efficiency network is significantly influenced by water resource endowment, industrial structure, and foreign investment level. The conclusions are expected to provide useful insights for designing the basin's water protection policy and greening the region's development.

Differential effects of specific emotions on spatial decision-making: evidence from cross-frequency functionally independent brain networks.

Emotions significantly shape the way humans make decisions. However, the underlying neural mechanisms of this influence remain elusive. In this study, we designed an experiment to investigate how emotions (specifically happiness, fear, and sadness) impact spatial decision-making, utilizing EEG data. To address the inherent limitations of sensor-level investigations previously conducted, we employed standard low-resolution brain electromagnetic tomography and functional independent component analysis to analyze the EEG data at the cortical source level. Our findings showed that across various spectral-spatial networks, positive emotion activated the decision-making network in the left middle temporal gyrus and inferior temporal gyrus, in contrast to negative emotions. We also identified the common spectral-spatial networks and observed significant differences in network strength across emotions. These insights further revealed the important role of the gamma-band prefrontal network. Our research provides a basis for deciphering the roles of brain networks in the impact of emotions on decision-making.

Synthesis and Performance Evaluation of a Novel High-Temperature-Resistant Thickener.

Successful exploitation of carbonate reservoirs relies on the acid-fracturing process, while the thickeners used in this process play a key role. It is a common engineering problem that thickeners usually fail to function when used in high-temperature environments. Until now, no research has ventured into the field of synthesizing thickeners which can be effectively used at ultra-high temperatures up to 180 °C. In our current study, a novel high-temperature-resistant polyacrylamide thickener named SYGT has been developed. The thermal gravimetric analysis (TGA) reveals that SYGT is capable of withstanding temperatures of up to 300 °C. Both our scanning electron microscopy (SEM) and rheological analysis demonstrate that the SYGT exhibits excellent resistance to both temperature and shear. At 180 °C, the viscosity of the SYGT aqueous solution is no lower than 61.7 mPa·s at a 20% H+ concentration or high salt concentration, and the fracture conductivity of the thickened acid reaches 6 D·cm. For the first time, the influence of the polymer spatial network's structural parameters on the viscosity of polymer solutions has been evaluated quantitatively. It was discovered that the length and surrounding area of the SNS skeleton have a synergistic effect on the viscosity of the polymer solution. Our experiments show that SYGT effectively reduces the acid-rock reaction rate and filtration loss under harsh working conditions such as high temperature, strong shear, high salinity, and a high concentration of acid. The synthesized acid-fracturing thickener (SYGT) has wide application potential in the development of carbonate reservoirs under high-temperature conditions.

[Spatial Network of Urban Heat Environment in Beijing-Tianjin-Hebei Urban Agglomeration Based on MSPA and Circuit Theory].

Rapid urbanization has increased the complexity of the urban heat environment system, which has negative impacts on the health of the urban ecological system and the human habitat. By combining theories and technologies such as geographic information systems, remote sensing, morphological spatial pattern analysis, and circuit theory with data from MODIS land surface temperature production, urban heat island patches in the Beijing-Tianjin-Hebei urban agglomeration were quantitatively identified in terms of their spatial and temporal distribution characteristics and their spatial and temporal transfer paths. This foundation revealed the geographical network structure of the urban heat environment as well as the spatial and temporal evolution process of critical corridors. According to the findings, urban heat island patches covered 16610 km2 in 2020, accounting for 7.68% of the study area. In the Beijing-Tianjin-Hebei urban agglomeration, both the area and the number of urban heat island patches considerably increased between 2005 and 2020, going from being dominated by isolated island types of urban heat island patches in 2005 to being dominated by core types in 2020. In particular, the non-urban heat island patches, core type, and edge type of urban heat island patches in 2005 were the major ancestors of the core type and edge type urban heat island patches in 2020. In the Beijing-Tianjin-Hebei urban agglomeration, there were more urban heat environment source sites, corridor length, densities, and present densities in 2020 than there were in 2005. The sensitive corridor was found to be the predominant type of urban heat island corridor in the Beijing-Tianjin-Hebei urban agglomeration in 2020. The number of sensitive corridors increased the highest in the period from 2005-2020. As the coefficient of urban heat environment corridors increased concurrently, it was apparent that the urban heat environment corridor had a propensity to grow continuously in the Beijing-Tianjin-Hebei urban agglomeration. The active adaptation and mitigation measures of the urban heat environment were proposed, and a spatial network model of the urban heat environment was finally provided. To adapt to, mitigate, and promote urban sustainable development risks, these research findings will serve as a paradigm for the identification of the urban heat environment spatial network actively and methodically.

Algal Bloom Ties: Spreading Network Inference and Extreme Eco-Environmental Feedback.

Coastal marine ecosystems worldwide are increasingly affected by tide alterations and anthropogenic disturbances affecting the water quality and leading to frequent algal blooms. Increased bloom persistence is a serious threat due to the long-lasting impacts on ecological processes and services, such as carbon cycling and sequestration. The exploration of eco-environmental feedback and algal bloom patterns remains challenging and poorly investigated, mostly due to the paucity of data and lack of model-free approaches to infer universal bloom dynamics. Florida Bay, taken as an epitome for biodiversity and blooms, has long experienced algal blooms in its central and western regions, and, in 2006, an unprecedented bloom occurred in the eastern habitats rich in corals and vulnerable habitats. With global aims, we analyze the occurrence of blooms in Florida Bay from three perspectives: (1) the spatial spreading networks of chlorophyll-a (CHLa) that pinpoint the source and unbalanced habitats; (2) the fluctuations of water quality factors pre- and post-bloom outbreaks to assess the environmental impacts of ecological imbalances and target the prevention and control of algal blooms; and (3) the topological co-evolution of biogeochemical and spreading networks to quantify ecosystem stability and the likelihood of ecological shifts toward endemic blooms in the long term. Here, we propose the transfer entropy (TE) difference to infer salient dynamical inter actions between the spatial areas and biogeochemical factors (ecosystem connectome) underpinning bloom emergence and spread as well as environmental effects. A Pareto principle, defining the top 20% of areal interactions, is found to identify bloom spreading and the salient eco-environmental interactions of CHLa associated with endemic and epidemic regimes. We quantify the spatial dynamics of algal blooms and, thus, obtain areas in critical need for ecological monitoring and potential bloom control. The results show that algal blooms are increasingly persistent over space with long-term negative effects on water quality factors, in particular, about how blooms affect temperature locally. A dichotomy is reported between spatial ecological corridors of spreading and biogeochemical networks as well as divergence from the optimal eco-organization: randomization of the former due to nutrient overload and temperature increase leads to scale-free CHLa spreading and extreme outbreaks a posteriori. Subsequently, the occurrence of blooms increases bloom persistence, turbidity and salinity with potentially strong ecological effects on highly biodiverse and vulnerable habitats, such as tidal flats, salt-marshes and mangroves. The probabilistic distribution of CHLa is found to be indicative of endemic and epidemic regimes, where the former sets the system to higher energy dissipation, larger instability and lower predictability. Algal blooms are important ecosystem regulators of nutrient cycles; however, chlorophyll-a outbreaks cause vast ecosystem impacts, such as aquatic species mortality and carbon flux alteration due to their effects on water turbidity, nutrient cycling (nitrogen and phosphorus in particular), salinity and temperature. Beyond compromising the local water quality, other socio-ecological services are also compromised at large scales, including carbon sequestration, which affects climate regulation from local to global environments. Yet, ecological assessment models, such as the one presented, inferring bloom regions and their stability to pinpoint risks, are in need of application in aquatic ecosystems, such as subtropical and tropical bays, to assess optimal preventive controls.

Magnetic three-phase single-drop microextraction for highly sensitive detection of aflatoxin B1 in agricultural product samples based on peroxidase-like spatial network structure.

In this work, a highly sensitive method for aflatoxin B1 (AFB1) detection was developed based on a peroxidase-like spatial network structure. The specific antibody and antigen of AFB1 were coated on a histidine-modified Fe3O4 nanozyme to form the capture/detection probes. Based on the competition/affinity effect, the spatial network structure was constructed by the probes, which could be rapidly (8 s) separated by a magnetic three-phase single-drop microextraction process. In this single-drop microreactor, the network structure was applied to catalyze a colorimetric 3,3',5,5'-tetramethylbenzidine oxidation reaction for AFB1 detection. The signal was amplified significantly due to the strong peroxidase-like ability of the spatial network structure and the enrichment effect of the microextraction. Thus, a low detection limit (0.034 pg/mL) was achieved. The matrix effect of real sample can be eliminated by the extraction approach, and the practicability of this method was proved by agricultural product samples analysis.

Exploring Spatial Correlations of Tourism Ecological Security in China: A Perspective from Social Network Analysis.

The imbalance of regional tourism ecological security (TES) is an important barrier to the sustainable development of tourism. Relying on the spatial correlation network to coordinate the regional TES is effective. Taking 31 provinces in China as examples, social network analysis (SNA) and the quadratic assignment procedure (QAP) are used to analyze the spatial network structure of TES and its influencing factors. The results show that (1) the network density and the number of network relationships increased, while the network efficiency remained at approximately 0.7, and the network hierarchy decreased from 0.376 to 0.234. (2) Jiangsu, Guangdong, Shandong, Zhejiang, and Henan were always more central than the average and dominated. Anhui, Shanghai, and Guangxi have much lower centrality degrees than the average, with little effect on other provinces. (3) The TES networks could be divided into four parts: "net spillover", "agent", "bidirectional spillover" and "net benefit". (4) The differences in economic development level, tourism industry dependence, tourism load level, educational attainment, investment in environmental governance, and transportation accessibility all had a negative impact on the TES spatial network, whereas geographic proximity had a positive driving effect. In conclusion, the spatial correlation network of provincial TES in China is increasingly close, but the network structure is loose and hierarchical. The core-edge structure is obvious, and there are significant spatial autocorrelations and spatial spillover effects between provinces. The difference in regional influencing factors has a significant effect on the TES network. This paper presents a new research framework for the spatial correlation of TES and provides a Chinese solution to promote the sustainable development of tourism.

Does geography matter? Implications for future tourism research in light of COVID-19.

Due to the 2019 new coronavirus disease (COVID-19) pandemic, tourism is undergoing fundamental changes that are affecting tourism research. This situation calls for in-depth analyses of tourism research. Scholars have already published review studies on COVID-19-related research within the tourism field; however, these studies do not connect findings, such as the research focus, research methodology and target group, to form a research profile, and the geographical patterns of the findings are not identified. study, COVID-19-related tourism studies were collected and analyzed in depth following the Preferred Reporting Items for systematic reviews and meta-analyses (PRISMA) method. In addition, data-driven methods, such as spatial multilayer networks, frequent patterns and content-based analyses, were applied to identify research profiles and their geographic patterns. This study pointed out the role of geographic patterns in tourism research, going beyond the research of the authors. Moreover, topics, focus destinations, applied methodologies and employed data sources have relevant geographic patterns. Four dominant research profiles that show that a shift can be observed in tourism research toward data sources and research methods were identified. Due to COVID-19, the strengthening of the application of quantitative methods and employment of secondary data sources are needed. Supplementary Information: The online version contains supplementary material available at 10.1007/s11192-022-04615-z.

Group-shrinkage feature selection with a spatial network for mining DNA methylation data.

Identifying disease-related biomarkers from high-dimensional DNA methylation data helps in reducing early screening costs and inferring pathogenesis mechanisms. Good discovery results have been achieved through spatial correlation methods of methylation sites, group-based regularization, and network constraints. However, these methods still have some key limitations as they cannot exclude isolated differential sites and only consider adjacent site ordering. Therefore, we propose a group-shrinkage feature selection algorithm to encourage the selection of clustered sites and discourage the selection of isolated differential sites. Specifically, a network-guided group-shrinkage strategy is developed to penalize weakly-correlated isolated methylation sites through a network structure constraint. The spatial network is constructed based on spatial correlation information of DNA methylation sites, where this information accounts for the uneven site distribution. The experimental simulations and applications demonstrated that the proposed method outperforms the advanced regularization methods, especially in rejecting isolated methylation sites; hence this study provides an efficient and clinical-valuable method for biomarker candidate discovery in DNA methylation data. Additionally, the proposed method exhibits enhanced reliability due to introducing biological prior knowledge into a regularization-based feature selection framework and could promote more research in the integration between biological prior knowledge and classical feature selection methods, thus facilitating their clinical application. Our source codes will be released at https://github.com/SJTUBME-QianLab/Group-shrinkage-Spatial-Network once this manuscript is accepted for publication.

Multiscale analysis of cancer service areas in the United States.

The purpose of delineating Cancer Service Areas (CSAs) is to define a reliable unit of analysis, more meaningful than geopolitical units such as states and counties, for examining geographic variations of the cancer care markets using geographic information systems (GIS). This study aims to provide a multiscale analysis of the U.S. cancer care markets based on the 2014-2015 Medicare claims of cancer-directed surgery, chemotherapy, and radiation. The CSAs are delineated by a scale-flexible network community detection algorithm automated in GIS so that the patient flows are maximized within CSAs and minimized between them. The multiscale CSAs include those comparable in size to those 4 census regions, 9 divisions, 50 states, and also 39 global optimal CSAs that generates the highest modularity value. The CSAs are more effective in capturing the U.S. cancer care markets because of its higher localization index, lower cross-border utilizations, and shorter travel time. The first two comparisons reveal that only a few regions or divisions are representative of the underlying cancer care markets. The last two comparisons find that among the 39 CSAs, 54% CSAs comprise multiple states anchored by cities near inner state borders, 28% are single-state CSAs, and 18% are sub-state CSAs. Their (in)consistencies across state borders or within each state shed new light on where the intervention of cancer care delivery or the adjustment of cancer care costs are needed to meet the challenges in the U.S. cancer care system. The findings could guide stakeholders to target public health policies for more effective coordination of cancer care in improving outcomes and reducing unnecessary costs.

Constructing a simplified interurban road network based on crowdsourced geodata.

The road network that connects cities with the existing road infrastructure of a country is a valuable tool for analyzing its transport routes, connectivity, and urban patterns. Yet, it is challenging to construct, given the data available.•We present a method to construct a simplified and connected urban network. Some network nodes are cities, and others are "transport nodes" representing road crossings or other types of infrastructure.•The result is a simplified connected network of all cities and the existing road infrastructure that maintains road distances and available routes.•The procedure reduces millions of spatial points that sometimes are disconnected polygonal lines or patches into a connected network with only a few edges and nodes.

How is the medical service efficiency in China? An empirical analysis using stochastic frontier approach and gravity models.

The COVID-19 has heightened the focus of medical services. Scientifically evaluating the efficiency of medical services and defining their spatial transmission relationship is crucial for the rational allocation of health resources and the accomplishment of balanced regional medical service growth. We used a Stochastic Frontier Model to calculate medical service performance in Chinese provinces and the Gravity Model to study the spatial relationship of medical service performance across provinces using data from 2009 to 2018. We discover that: (1) population density and proportion of technical personnel are significantly positively correlated with the efficiency of regional medical services, whereas health institution density has a significantly negative influence. Their respective influence coefficients were 1.717, 0.647, and 0.407. (2) In China, the regional development of medical service efficiency is unbalanced. The east, middle, and west multi-year average medical service efficiency were 0.65, 0.46, and 0.53, respectively, and their gaps were narrowing; the south and north average efficiency were 0.591, 0.516, respectively, and their gaps were widening. (3) Our medical efficiency network is not yet widespread in the country. Hubei, Henan, Shandong, Jiangsu, Zhejiang, and Beijing were at the centre of the medical efficiency network, driving and connecting the nation's medical service. Our findings offer specific recommendations for better understanding and improving the efficiency of medical services.

Speeding Up Reachability Queries in Public Transport Networks Using Graph Partitioning.

Computing path queries such as the shortest path in public transport networks is challenging because the path costs between nodes change over time. A reachability query from a node at a given start time on such a network retrieves all points of interest (POIs) that are reachable within a given cost budget. Reachability queries are essential building blocks in many applications, for example, group recommendations, ranking spatial queries, or geomarketing. We propose an efficient solution for reachability queries in public transport networks. Currently, there are two options to solve reachability queries. (1) Execute a modified version of Dijkstra's algorithm that supports time-dependent edge traversal costs; this solution is slow since it must expand edge by edge and does not use an index. (2) Issue a separate path query for each single POI, i.e., a single reachability query requires answering many path queries. None of these solutions scales to large networks with many POIs. We propose a novel and lightweight reachability index. The key idea is to partition the network into cells. Then, in contrast to other approaches, we expand the network cell by cell. Empirical evaluations on synthetic and real-world networks confirm the efficiency and the effectiveness of our index-based reachability query solution.

Habitat Specialization by Wildlife Reduces Pathogen Spread in Urbanizing Landscapes.

AbstractUrban areas are expanding globally with far-reaching ecological consequences, including for wildlife-pathogen interactions. Wildlife show tremendous variation in their responses to urbanization; even within a single population, some individuals can specialize on urban or natural habitat types. This specialization could alter pathogen impacts on host populations via changes to wildlife movement and aggregation. Here, we build a mechanistic model to explore how habitat specialization in urban landscapes affects interactions between a mobile host population and a density-dependent specialist pathogen that confers no immunity. We model movement on a network of resource-stable urban sites and resource-fluctuating natural sites, where hosts are urban specialists, natural specialists, or generalists that use both patch types. We find that for generalists, natural and partially urban landscapes produce the highest infection prevalence and mortality, driven by high movement rates at natural sites and high densities at urban sites. However, habitat specialization protects hosts from these negative effects of partially urban landscapes by limiting movement between patch types. These findings suggest that habitat specialization can benefit populations by reducing infectious disease transmission, but by reducing movement between habitat types it could also carry the cost of reducing other movement-related ecosystem functions, such as seed dispersal and pollination.

On the interplay of regional mobility, social connectedness and the spread of COVID-19 in Germany.

Since the primary mode of respiratory virus transmission is person-to-person interaction, we are required to reconsider physical interaction patterns to mitigate the number of people infected with COVID-19. While research has shown that non-pharmaceutical interventions (NPI) had an evident impact on national mobility patterns, we investigate the relative regional mobility behaviour to assess the effect of human movement on the spread of COVID-19. In particular, we explore the impact of human mobility and social connectivity derived from Facebook activities on the weekly rate of new infections in Germany between 3 March and 22 June 2020. Our results confirm that reduced social activity lowers the infection rate, accounting for regional and temporal patterns. The extent of social distancing, quantified by the percentage of people staying put within a federal administrative district, has an overall negative effect on the incidence of infections. Additionally, our results show spatial infection patterns based on geographical as well as social distances.