Urban Planning as an Extension of War Planning: The Case of Shenyang, China, 1898-1966

War-city relationships had long been studied by scholars regarding wars’ sudden impact on cities. Studies typically focused on one specific event’s impact on urban military, politics, economy, or society. This approach, however, treated war’s impact on cities as only temporary, hindered opportunities to reveal multiple political regimes’ spatial competition through war-oriented city planning and construction, which is crucial for city development, and their resultant urban form changes through time. In response, this study has examined city planning and construction activities during the short time gaps between multiple military conflicts, with various military objectives, and conducted by different political regimes in Shenyang, China. In accordance with archival research, a space syntax axis analysis has been used to quantify spatial dynamics throughout war-peace-war cycles to explore the impact of military-oriented planning on city-scaled development. We have found these planning strategies, initiated by specific military goals, acted as extensions of war planning, segregating the city and causing urban fragmentation. They also acted as a driving factor which promoted modernization of the city in the early 20th century. We conclude that wars oriented planning can alter a city’s development track and impact its structure and form through the creation of internally connected but isolated urban districts. Copyright © 2018 Journal Of Contemporary Urban Affairs

Real-space Formalism for the Euler Class and Fragile Topology in Quasicrystals and Amorphous Lattices

We propose a real-space formalism of the topological Euler class, which characterizes the fragile topology of two-dimensional systems with real wave functions. This real-space description is characterized by local Euler markers whose macroscopic average coincides with the Euler number, and it applies equally well to periodic and open boundary conditions for both crystals and noncrystalline systems. We validate this by diagnosing topological phase transitions in clean and disordered crystalline systems with the reality endowed by the space-time inversion symmetry $\mathcal{I}_{ST}$. Furthermore, we demonstrated the topological Euler phases in quasicrystals and even in amorphous lattices lacking any spatial symmetries. Our work not only provides a local characterization of the fragile topology but also significantly extends its territory beyond $\mathcal{I}_{ST}$-symmetric crystalline materials.Comment: 41 pages,8 figure

Urban Planning as an Extension of War Planning:

War-city relationships had long been studied by scholars regarding wars\u27 sudden impact on cities. Studies typically focused on one specific event\u27s impact on urban military, politics, economy, or society. This approach, however, treated war\u27s impact on cities as only temporary, hindered opportunities to reveal multiple political regimes\u27 spatial competition through war-oriented city planning and construction, which is crucial for city development, and their resultant urban form changes through time. In response, this study has examined city planning and construction activities during the short time gaps between multiple military conflicts, with various military objectives, and conducted by different political regimes in Shenyang, China. In accordance with archival research, a space syntax axis analysis has been used to quantify spatial dynamics throughout war-peace-war cycles to explore the impact of military-oriented planning on city-scaled development. We have found these planning strategies, initiated by specific military goals, acted as extensions of war planning, segregating the city and causing urban fragmentation. They also acted as a driving factor which promoted modernization of the city in the early 20th century. We conclude that wars oriented planning can alter a city\u27s development track and impact its structure and form through the creation of internally connected but isolated urban districts

A MapReduce-based nearest neighbor approach for big-data-driven traffic flow prediction

In big-data-driven traffic flow prediction systems, the robustness of prediction performance depends on accuracy and timeliness. This paper presents a new MapReduce-based nearest neighbor (NN) approach for traffic flow prediction using correlation analysis (TFPC) on a Hadoop platform. In particular, we develop a real-time prediction system including two key modules, i.e., offline distributed training (ODT) and online parallel prediction (OPP). Moreover, we build a parallel k-nearest neighbor optimization classifier, which incorporates correlation information among traffic flows into the classification process. Finally, we propose a novel prediction calculation method, combining the current data observed in OPP and the classification results obtained from large-scale historical data in ODT, to generate traffic flow prediction in real time. The empirical study on real-world traffic flow big data using the leave-one-out cross validation method shows that TFPC significantly outperforms four state-of-the-art prediction approaches, i.e., autoregressive integrated moving average, Naïve Bayes, multilayer perceptron neural networks, and NN regression, in terms of accuracy, which can be improved 90.07% in the best case, with an average mean absolute percent error of 5.53%. In addition, it displays excellent speedup, scaleup, and sizeup