基于激光扫描点云的隧道断面提取方法

传统断面测量方法存在效率低、精度差等特点,为优化断面测量方法,提出利用地面激光点云提取断面的方法。其主要包括:中轴线拟合、点云分割、断面提取三步骤。在基于点云双向投影确定初始中轴线的基础上,运用弦高偏移算法,提取骨架特征拐点,对中轴线分段再次拟合,获取二次中轴线;点云分割是基于隧道中轴线进行的,对隧道的点云数据进行连续分割,对截取的断面进行椭圆拟合。实验表明,通过此方法快速提取中轴线上任意位置的断面。     

Rapid PPP ambiguity resolution using GPS+GLONASS observations

Integer ambiguity resolution (IAR) in precise point positioning (PPP) using GPS observations has been well studied. The main challenge remaining is that the first ambiguity fixing takes about 30 min. This paper presents improvements made using GPS+GLONASS observations, especially improvements in the initial fixing time and correct fixing rate compared with GPS-only solutions. As a result of the frequency division multiple access strategy of GLONASS, there are two obstacles to GLONASS PPP-IAR: first and most importantly, there is distinct code inter-frequency bias (IFB) between satellites, and second, simultaneously observed satellites have different wavelengths. To overcome the problem resulting from GLONASS code IFB, we used a network of homogeneous receivers for GLONASS wide-lane fractional cycle bias (FCB) estimation and wide-lane ambiguity resolution. The integer satellite clock of the GPS and GLONASS was then estimated with the wide-lane FCB products. The effect of the different wavelengths on FCB estimation and PPP-IAR is discussed in detail. We used a 21-day data set of 67 stations, where data from 26 stations were processed to generate satellite wide-lane FCBs and integer clocks and the other 41 stations were selected as users to perform PPP-IAR. We found that GLONASS FCB estimates are qualitatively similar to GPS FCB estimates. Generally, 98.8% of a posteriori residuals of wide-lane ambiguities are within \(\pm 0.25\) cycles for GPS, and 96.6% for GLONASS. Meanwhile, 94.5 and 94.4% of narrow-lane residuals are within 0.1 cycles for GPS and GLONASS, respectively. For a critical value of 2.0, the correct fixing rate for kinematic PPP is only 75.2% for GPS alone and as large as 98.8% for GPS+GLONASS. The fixing percentage for GPS alone is only 11.70 and 46.80% within 5 and 10 min, respectively, and improves to 73.71 and 95.83% when adding GLONASS. Adding GLONASS thus improves the fixing percentage significantly for a short time span. We also used global ionosphere maps (GIMs) to assist the wide-lane carrier-phase combination to directly fix the wide-lane ambiguity. Employing this method, the effect of the code IFB is eliminated and numerical results show that GLONASS FCB estimation can be performed across heterogeneous receivers. However, because of the relatively low accuracy of GIMs, the fixing percentage of GIM-aided GPS+GLONASS PPP ambiguity resolution is very low. We expect better GIM accuracy to enable rapid GPS+GLONASS PPP-IAR with heterogeneous receivers.     

基于位置轨迹挖掘的城市居民职住地识别方法研究

城市居民居住与就业的空间组织在城市空间结构的研究中非常重要,居民职住地的识别则是城市居民职住空间组织研究的首要任务。本文从位置轨迹入手,在基于时间聚类提取出停留点序列的基础上,重点对停留点序列的时间特征进行分析,对其做进一步的归纳,筛选出职住地出候选类,最终完成居住地、工作地的识别。实验结果表明,该方法能够有效地从位置轨迹数据中识别出城市居民的居住地和工作地。     

论提高不动产产权保护精准度的若干问题

所谓产权保护精准度,是指产权保护在执法和司法实践方面符合规定标准的程度.评价不动产产权的执法保护精准度,应当以确权登记中对不动产物权法律关系的客体、主体和权利义务的明晰程度为基础.其中,不动产产权客体界定精准度在证据法的意义上具有证明性和法定性,不动产客体界定的精准度应是评价产权保护精准度的一个重要指标.因为不动产产权客体界定是确权登记的前置条件,客体界定的精准度不仅事关确权登记质量的优劣而且事关不良后果的法津责任,所以为保证政府的公信力,必须重视客体界定的精准度,在落实产权保护的管理制度和技术法规方面,应当严肃态度、严格要求、严苛执行.     

Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata

Cellular automata (CA) have been widely used to simulate complex urban development processes. Previous studies indicated that vector-based cellular automata (VCA) could be applied to simulate urban land-use changes at a realistic land parcel level. Because of the complexity of VCA, these studies were conducted at small scales or did not adequately consider the highly fragmented processes of urban development. This study aims to build an effective framework called dynamic land parcel subdivision (DLPS)-VCA to accurately simulate urban land-use change processes at the land parcel level. We introduce this model in urban land-use change simulations to reasonably divide land parcels and introduce a random forest algorithm (RFA) model to explore the transition rules of urban land-use changes. Finally, we simulate the land-use changes in Shenzhen between 2009 and 2014 via the proposed DLPS-VCA model. Compared to the advanced Patch-CA and RFA-VCA models, the DLPS-VCA model achieves the highest simulation accuracy (Figure-of-Merit = 0.232), which is 32.57% and 18.97% higher respectively, and is most similar to the actual land-use scenario (similarity = 94.73%) at the pattern level. These results indicate that the DLPS-VCA model can both accurately split the land during urban land-use changes and significantly simulate urban expansion and urban land-use changes at a fine scale. Furthermore, the land-use change rules that are based on DPLS-VCA mining and the simulation results of several future urban development scenarios can act as guides for future urban planning policy formulation.     

Extracting and analyzing semantic relatedness between cities using news articles

News articles capture a variety of topics about our society. They reflect not only the socioeconomic activities that happened in our physical world, but also some of the cultures, human interests, and public concerns that exist only in the perceptions of people. Cities are frequently mentioned in news articles, and two or more cities may co-occur in the same article. Such co-occurrence often suggests certain relatedness between the mentioned cities, and the relatedness may be under different topics depending on the contents of the news articles. We consider the relatedness under different topics as semantic relatedness. By reading news articles, one can grasp the general semantic relatedness between cities; yet, given hundreds of thousands of news articles, it is very difficult, if not impossible, for anyone to manually read them. This paper proposes a computational framework which can ‘read’ a large number of news articles and extract the semantic relatedness between cities. This framework is based on a natural language processing model and employs a machine learning process to identify the main topics of news articles. We describe the overall structure of this framework and its individual modules, and then apply it to an experimental dataset with more than 500,000 news articles covering the top 100 US cities spanning a 10-year period. We perform exploratory visualizations of the extracted semantic relatedness under different topics and over multiple years. We also analyze the impact of geographic distance on semantic relatedness and find varied distance decay effects. The proposed framework can be used to support large-scale content analysis in city network research.     

中国春运人口省际流动的时空与结构特征

本文通过构建人口省际流动的关系矩阵,透视2015年中国春节期间人口省际流动的动态过程、网络特征,并对各省人口流入、流出的规模差异进行测度。研究结论如下：①春节前后中国各省每日人口净流入人次存在显著的规律性特征,2月13-17日和2月25日-3月1日为各省节前返乡流和节后返工流期间人口净流入、流出峰值时段,可作为基于春运研究中国人口省际流动的重要依据;②识别了14个人口净流入省、17个人口净流出省,净流入位序前六省和后八省分别吸纳和提供了全国9成人口的集聚与扩散。人口主要流入省的沿海绵延和主要流出省的中部“人口塌陷”共同构成了双纵格局;③各省流出首位流的指向特征显著,南方人口流出省的流出指向存在共性,京津、长三角、珠三角地区的人口集聚路径存在差异。④移动数据为发掘人口流动过程中蕴含的丰富信息提供了平台,基于节后、节前净流入值差值的方法可以有效识别中国人口流动的规模差异、属性特征。通过与以往研究对比,证实了移动数据与基于人口普查数据研究的诸多结论相似性。     

东营石油装备制造业创新网络演化研究

采用社会网络分析方法,借助Ucinet、ArcGIS等软件工具,从多维邻近的视角出发,对东营石油装备业创新网络演化进行分析研究,研究发现：首先,多维邻近对东营创新网络发展产生了重要影响,且不同网络发展阶段（培育、起步、成长）主导的邻近因子不同。这一方面证实了Boschma多维邻近性的相关理论假说,另一方面指出多维邻近因子与东营创新网络演变的不同关联。其次,高校、大型国有企业是东营创新网络的知识源泉和组织者。中国石油大学（华东)、胜利油田集团企业发挥了主导作用,这与国外中小型创新企业发挥主导作用的情形有很大差异。     

用深井观测的地震研究东川地区随深度变化的场地放大因子.

采用中日合作，分别布设于东川垂直竖井中地下200ｍ、100ｍ 和地面３个位置的高精度数字化地震仪记录的地方震资料（震中距8.5～95ｋｍ，震级ML1.3～5.1），用频谱比的分析方法计算了东川地区不同深度（－100ｍ 和地面）的场地放大因子．在８个频率（f ＝0.75，1.0，1.5，2，3，4，5和12Ｈｚ）上所对应的在-100ｍ 处的3个分向（N-S，E-W，U-D）的场地放大因子（与-200ｍ 处相比），当0.75Ｈｚ f 2Ｈｚ时，为1.0～1.49；当2Ｈｚ＜ f ＜12Ｈｚ时，为0.47～0.99；在地面，3个分向在8个频率上所对应的场地放大因子（与-200ｍ 处相比）均大于1，其值为1.6～7.63．-100ｍ 处的Ｓ波和尾波场地放大因子与地面的Ｓ波和尾波的场地放大因子的相关系数，当0.75Ｈｚ f 2Ｈｚ频段时，分别为0.82和0.81；当2Ｈｚ＜ f＜12Ｈｚ频段时，分别为0.47和0.45．