Use of community mobile phone big location data to recognize unusual patterns close to a pipeline which may indicate unauthorized activities and possible risk of damage

Damage caused by people and organizations unconnected with the pipeline management is a major risk faced by pipelines,and its consequences can have a huge impact.However,the present measures to monitor this have major problems such as time delays,overlooking threats,and false alarms.To overcome the disadvantages of these methods,analysis of big location data from mobile phone systems was applied to prevent third-party damage to pipelines,and a third-party damage prevention system was developed for pipelines including encryption mobile phone data,data preprocessing,and extraction of characteristic patterns.By applying this to natural gas pipelines,a large amount of location data was collected for data feature recognition and model analysis.Third-party illegal construction and occupation activities were discovered in a timely manner.This is important for preventing third-party damage to pipelines.

Visualization and analysis of local and distant population flows on the Qinghai-Tibet Plateau using crowd-sourced data

Human migration between cities is one important aspect of spatial interaction that not only reflects urban attractiveness but also denotes interactions amongst agglomerations.We therefore implemented a web-based visualization system to analyze and interactively explore local and distant population flow patterns between cities on the Qinghai-Tibet Plateau(QTP).We utilized 2017 Tencent population flow data from which we initially constructed inbound and outbound vectors for cities on the QTP.We then used multidimensional scaling to examine and visualize migration patterns and similarities between cities.Results reveal the presence of six local and three distant human mobility patterns on the QTP as well as average summer monthly migrations more than twice the level of those in the winter.