基于GIS空间融合技术的北京都市圈空间识别及通勤率特征研究

在京津冀区域一体化背景下,利用手机信令数据,对以城市中心区为通勤中央核的通勤空间特征进行研究,以此判别北京都市圈的空间边界及拓展趋势,同时为京津冀一体化背景下的城市群与都市圈综合交通网络融合和面向通勤群体提供高品质通勤服务的公交服务给予政策借鉴。采取基于GIS的空间融合分析技术,有效识别手机用户居住地、就业地和通勤OD链,通过测算外围地区到中央核的通勤率判定都市圈通勤范围空间特征。分析显示都市圈范围的空间层次与通勤率相关。以北京中心城区为通勤中央核,第一圈层30%通勤率等值线对应约在30 km之内,这是城市通勤行为最活跃密集的地带。第二圈层10%通勤率等值线对应30-50 km的不规则圈层范围,约为都市圈边界涵盖范围。最外圈层5%通勤率等值线对应的50 km之外的通勤偶发地带,在空间上呈现不连续分布的特征。

多源空间数据融合技术探讨

地理信息系统的迅速发展和广泛应用导致了空间多源数据的产生,给数据的集成和信息共享带来不便。不同数据源,不同数据精度和不同数据模型的地理数据融合理论与方法的研究,对于降低地理数据的生产成本,加快现有地理信息更新速度,提高地理数据质量有着重要的现实意义。本文对多种空间数据融合的原理与方法进行了讨论,给出了空间数据融合的多种应用,并对数据融合的发展方向进行了展望。

Heterogeneous sensors data fusion method based on peak picking in probability density space

The multi-sensor multi-target localization and data fusion problem is discussed, and a new data fusion method called joint probability density matrix （JPDM） has been proposed, which can associate with and fuse measurements from spatially distributed heterogeneous sensors to produce good estimates of the targets. Based on probabilistic grids representation, the uncertainty regions of all the measurements are numerically combined in a general framework. The NP-hard multi-sensor data fusion problem has been converted to a peak picking problem in the grids map. Unlike most of the existing data fusion methods, the JPDM method does not need association processing, and will not lead to combinatorial explosion. Its convergence to the CRB with a diminishing grid size has been proved. Simulation results are presented to illustrate the effectiveness of the proposed technique.