Depicting urban boundaries from a mobility network of spatial interactions: A case study of Great Britain with geo-located Twitter data

Existing urban boundaries are usually defined by government agencies for administrative, economic, and political purposes. Defining urban boundaries that consider socio-economic relationships and citizen commute patterns is important for many aspects of urban and regional planning. In this paper, we describe a method to delineate urban boundaries based upon human interactions with physical space inferred from social media. Specifically, we depicted the urban boundaries of Great Britain using a mobility network of Twitter user spatial interactions, which was inferred from over 69 million geo-located tweets. We define the non-administrative anthropographic boundaries in a hierarchical fashion based on different physical movement ranges of users derived from the collective mobility patterns of Twitter users in Great Britain. The results of strongly connected urban regions in the form of communities in the network space yield geographically cohesive, non-overlapping urban areas, which provide a clear delineation of the non-administrative anthropographic urban boundaries of Great Britain. The method was applied to both national (Great Britain) and municipal scales (the London metropolis). While our results corresponded well with the administrative boundaries, many unexpected and interesting boundaries were identified. Importantly, as the depicted urban boundaries exhibited a strong instance of spatial proximity, we employed a gravity model to understand the distance decay effects in shaping the delineated urban boundaries. The model explains how geographical distances found in the mobility patterns affect the interaction intensity among different non-administrative anthropographic urban areas, which provides new insights into human spatial interactions with urban space.Comment: 32 pages, 7 figures, International Journal of Geographic Information Scienc

CyberGIS-enabled reproducible agent-based modeling for scalable emergency evacuation

Agent-based models represent an effective methodology for studying the complexity of emergency evacuation. However, due to the high computational intensity that increases dramatically with regard to evacuation area size and the number of people to be evacuated, agent-based evacuation models are typically applied to relatively small areas and populations. In order to make agent-based models scalable to large evacuation areas and population sizes for emergency decision support, it is important to not only effectively harness advanced cyberinfrastructure and geospatial big data, but also make modeling workflows accessible and reproducible by researchers and decision makers. In this dissertation research, a novel cyberGIS-based approach to reproducible and scalable modeling of emergency evacuation is developed to encompass 1) systematic design of the approach for examining the reproducibility of scalable modeling scenarios for researchers and decision makers; 2) algorithmic innovation for achieving desirable computational scalability of agent-based evacuation modeling; and 3) novel geospatial big data analytics for modeling fine-scale population distribution that is important to agent-based evacuation modeling. An agent-based evacuation model is developed based on a reproducible cyberGIS science gateway framework named CyberGIS-Jupyter; enhanced by a novel network-partition algorithm for computational scalability; and improved using fine-scale population distributions derived from location-based social media data. The central contribution of this dissertation research is to achieve computational scalability and reproducibility for spatially explicit agent-based modeling to gain new fundamental knowledge of mass emergency evacuation.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

CyberGIS-enabled reproducible agent-based modeling for scalable emergency evacuation

Agent-based models represent an effective methodology for studying the complexity of emergency evacuation. However, due to the high computational intensity that increases dramatically with regard to evacuation area size and the number of people to be evacuated, agent-based evacuation models are typically applied to relatively small areas and populations. In order to make agent-based models scalable to large evacuation areas and population sizes for emergency decision support, it is important to not only effectively harness advanced cyberinfrastructure and geospatial big data, but also make modeling workflows accessible and reproducible by researchers and decision makers. In this dissertation research, a novel cyberGIS-based approach to reproducible and scalable modeling of emergency evacuation is developed to encompass 1) systematic design of the approach for examining the reproducibility of scalable modeling scenarios for researchers and decision makers; 2) algorithmic innovation for achieving desirable computational scalability of agent-based evacuation modeling; and 3) novel geospatial big data analytics for modeling fine-scale population distribution that is important to agent-based evacuation modeling. An agent-based evacuation model is developed based on a reproducible cyberGIS science gateway framework named CyberGIS-Jupyter; enhanced by a novel network-partition algorithm for computational scalability; and improved using fine-scale population distributions derived from location-based social media data. The central contribution of this dissertation research is to achieve computational scalability and reproducibility for spatially explicit agent-based modeling to gain new fundamental knowledge of mass emergency evacuation.U of I OnlyAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD syste

Component-wise Interpolation of Solenoidal Vector Fields: A Comparative Numerical Study

Vector-field interpolation is a fundamental task in flow simulation and visualization. The common practice is to interpolate the vector field in a component-wise fashion. When the vector field of interest is solenoidal (divergencefree), such an approach is not conservative and gives rise to artificial divergence. In this work, we numerically compare some recently proposed scalar interpolation methods on the Cartesian and body-centered cubic lattices, and investigate their ability to conserve the solenoidal nature of the vector field. We start with a sampled version of a synthetic solenoidal vector field and use an interpolative component-wise reconstruction method to approximate the vector field and its divergence at arbitrary locations. Our results show that an improved scalar interpolation method does not necessarily lead to a more conservative vector field approximation.N

A cyberGIS-enabled multi-criteria spatial decision support system: A case study on flood emergency management

With the increased frequency of natural hazards and disasters and consequent losses, it is imperative to develop efficient and timely strategies for emergency response and relief operations. In this paper, we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management. It combines a high-performance computing environment (cyberGIS-Jupyter) and multi-criteria decision analysis models (Weighted Sum Model (WSM) and Technique for Order Preference by Similarity to Ideal Solution Model (TOPSIS)) with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation. Social media data (e.g. Twitter data) was used as an additional tool to support the decision-making process. Our case study involves two decision goals generated based on a past flood event in the city of Austin, Texas, U.S.A. As our result shows, WSM produces more diverse values and higher output category estimations than the TOPSIS model. Finally, the model was validated using an innovative questionnaire. This cyberGIS- enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers, where different emergency responders can formulate their decision objectives, select relevant evaluation criteria, and perform interactive weighting and sensitivity analyses

Lymphovascular or perineural invasion is associated with lymph node metastasis and survival outcomes in patients with gastric cancer

Abstract Background Lymphovascular invasion (LVI) and perineural invasion (PNI) are associated with poorer prognosis in several human malignancies, but their significance in gastric cancer (GC) remains to be clearly defined. Our study aimed to investigate the prognostic value of LVI/PNI in patients with curative resected GC. Methods Records of 1488 patients with stage I‐–III GC and 3327 patients with stage I–III colorectal cancer (CRC) were reviewed retrospectively, and difference in the incidence of LVI/PNI between GC and CRC was compared. Univariate and multivariate analyses were used to evaluate whether LVI/PNI was an independent risk factor for lymph node metastasis (LNM) and overall survival (OS) in GC. Results Patients with stage I–III GC had a significantly higher incidence of LVI/PNI than patients with stage I–III CRC (50.54% vs. 21.91%, p  < 0.001). LVI/PNI was significantly associated with higher CEA, higher CA199, deeper tumor invasion, more lymph node metastasis, and advanced TNM stage in GC ( p  < 0.05). Multivariate logistic regression analysis identified LVI/PNI (OR = 2.64, 95%CI: 2.05–3.40, p  < 0.001) as an independent risk factor for LNM in GC. The OS rate was significantly lower in the LVI/PNI‐positive GC group than that in the LVI/PNI‐negative GC group ( p  < 0.001). On multivariate Cox regression analysis, LVI/PNI (HR = 1.34, 95%CI: 1.04–1.71, p  = 0.023) was an independent prognostic factor for OS in GC. Conclusion GC has a high incidence of LVI/PNI, which was closely associated with disease progression. LVI/PNI could serve as an independent risk factor for LNM and the prognosis of patients with curative resected GC. These findings will be helpful in predicting survival outcomes more accurately and establishing individualized treatment plans