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2.2 行人与车辆的观察分析Select 20 locations in Jimei News Agency for fixed-point observation, once on weekdays and once on holidays. The observation is divided into 6 periods, each period is 2h, the time range is 8:00-20:00, and the observation time of one observation point is 3min. Record the pedestrian flow and traffic flow, as shown in Figures 9, 10 and 11.在集美大社选择20个地点进行定点观察，工作日与休假日各观察一次。观察分为6个时段，每一个时段为2h，时间范围为8:00-20:00，一个观察点观察时间为3min。 纪录经过的行人流量与车流量情况，如图9，10，11所示。2.2.1 Observation and analysis of pedestrians in the six observation periods of working days, the order of total pedestrian flow from high to low is 10:00-12:00, 8:00-10:00, 14:00-16:00, 12:00-14:00, 16:00-18:00, 18:00-20:00. In the classification of crowd, the order of number from high to low is office workers, middle-aged people, the elderly, College Students Children and middle school students. The place with the largest pedestrian flow on weekdays is observation point 16, followed by observation point 5, the third highest flow is observation point 15, and the places with the smallest pedestrian flow are observation points 8, 18 and 9. The Pearson correlation analysis results of pedestrian flow on weekdays show that the elderly (r = 0.59, P < 0.01), children (r = 0.60, P < 0.01) and office workers (r = 0.77, P < 0.01) There was a significant positive correlation with middle-aged people; Children (r = 0.50, P < 0.05) and middle school students (r = 0.88, P < 0.01) were significantly positively correlated with college students; There was a significant positive correlation between children (r = 0.59, P < 0.05) and middle school students; There was a significant positive correlation between the elderly (r = 0.63, P < 0.01) and children. In the six observation periods on the rest day, the positions of the top three high pedestrian flows were observation points 3, 10 and 11 respectively, and the lowest sum was observation points 19, 18 and 9 respectively. In the six periods, the crowd was the most from 18:00 to 20:00, followed by 10:00-12:00, Then it is 8:00-10:00, 16:00-18:00, 14:00-16:00 and 12:00-14:00. The number of people on rest days observed in this study is nearly 1.5 times higher than that on working days. The largest number of individuals is middle-aged people, followed by office workers; Then, the number of people is ranked from high to low as middle school students, the elderly, college students and children. Compared with working days, middle-aged people and office workers are the crowd category with the largest number of people using street space. The places with the largest and second largest pedestrian flow on rest days are observation point 20, and the third highest is observation point 3, The Pearson correlation analysis of pedestrian flow on rest days showed that there was a significant correlation between children (r = 0.63, P < 0.01) and the elderly; The elderly (r = 0.58, P < 0.01), children (r = 0.48, P < 0.05) and office workers (r = 0.75, P < 0.01) were significantly correlated with middle-aged people. The results of these two analyses were the same as those of working days.2.2.1 行人的观察分析在工作日的6个观察时段，行人流量总和由高到低的排序为10:00-12:00，8:00-10:00，14:00-16:00，12:00-14:00，16:00-18:00，18:00-20:00．在人潮的分类当中，数量的 排序由高至低分别是上班族、中年人、老人、大学生、儿童和中学生．工作日行人流量最多的地方是观测点16，其次位于观测点5，第3高流量位于观测点15，行人流量最小的地点是观测点8,18和9．工作日行人流量的Pearson相关分析结果显示：老人（r＝0.59，P＜0.01）、儿童（r＝0.60，P＜0.01）、上班族 （r＝0.77，P＜0.01）和中年人有显著正相关；儿童（r＝0.50，P＜0.05）和中学生（r＝0.88，P＜0.01） 与大学生有显著正相关；儿童（r＝0.59，P＜0.05）与中学生有显著正相关；老人（r＝0.63，P＜0.01）与儿童有显著正相关． 在休息日的6个观察时段中，行人流量前三高的位置分别是观察点3,10与11，总和最低的分别是 观察点19，18与9．6个时段中的人潮是18:00－20:00最多，其次是10:00-12：00,然后依序是8：00-10：00，16：00-18：00，14：00-16：00和12：00-14：00．本研究观察到的休息日人群比工作日多了将近 1.5倍的数量．个别人数最多的是中年人，其次是上班族；然后人数由高而低的排序分别为中学生、老人、大学生和儿童.与工作日比较，中年人与上班族是使用街道空间人数最多的人潮类别．休息日行人流 量最多与次多的地点都是观测点20，第三高是观测点3，然后是观测点10和13．休息日行人流量的Pearson相关分析结果显示：儿童（r＝0.63，P＜0.01）和老人有显著相关性；老人（r＝0.58，P＜0.01）、 儿童（r＝0.48，P＜0.05）和上班族（r＝0.75，P＜0.01）与中年人有显著相关性．这两个分析结果与工作日相同．

2.2.2 Observation And Analysis Of VehiclesIn the six observation periods of working days, the top three places of traffic flow are observation points 15, 10 and 11 respectively, and the three places with the lowest total are observation points 14, 7 and 8 respectively. The traffic flow is the highest from 10:00 to 12:00, followed by 8:00 to 10:00; Then, the order of traffic flow from high to low is 16:00-18:00, 12:00-14:00, 18:00-20:00 and 14:00-16:00. In the category analysis of vehicles, the largest number of vehicles is motorcycles, followed by most private vehicles on the roads around the settlement, followed by bicycles, tricycles Taxis and minivans. Observation point 15 is the location with the most traffic flow on weekdays, and the second and third highest traffic flow are also in this location. Pearson correlation analysis of weekday traffic flow shows that there is a significant positive correlation between taxis (r = 0.75, P < 0.01) and private cars; Taxis (r = 0.56, P < 0.01) and private cars (r = 0.54, P < 0.05) were significantly positively correlated with bicycles; Taxis (r = 0.66, P < 0.01) and private cars (r = 0.80, P < 0.01) were significantly positively correlated with minivans; Taxis (r = 0.78, P < 0.01), private cars (r = 0.72, P < 0.01), bicycles (r = 0.50, P < 0.01) were significantly positively correlated with minivans (r = 0.56, P < 0.01) and tricycles; Bicycle (r = 0.78, P < 0.01) has a significant positive correlation with tricycle (r = 0.45, P < 0.05) and motorcycle.In the six observation periods of rest days, the traffic flow gradually decreases from 8:00-10:00 to 12:00-14:00, and increases greatly from 14:00-16:00, 16: From 00 to 18:00, there was a slight decrease, and the traffic flow in the last period was the largest. In the classification of vehicles, the largest number of motorcycles was observed, followed by private vehicles, followed by bicycles, tricycles, minivans, minibuses, taxis, trucks and buses. The place with the largest traffic flow on rest days was observation point 10, The second highest position is observation points 3 and 10, and the third highest is observation points 10 and 14. Observation point 10 is included in the first three high places of traffic flow on rest days, because this street is the main road of the city. Pearson correlation analysis of traffic flow on rest days shows that there is a significant positive correlation between minibus (r = 0.59, P < 0.01) and taxi; There was a significant positive correlation between private cars and minibuses (r = 0.88, P < 0.01) and taxis (r = 0.83, P < 0.01); There was a significant positive correlation between minibus (r = 0.65, P < 0.01) and private car (r = 0.54, P < 0.05) and minivan; There was a significant positive correlation between bicycle (r = 0.62, P < 0.01) and motorcycle.2.2.2车辆的观察分析在工作日的6个观察时段，车流量前三高的地点分别是观测点15，10与11， 总和最低的3个地方分别是观测点14，7与8．车流量在10:00－12:00最高，其次是8:00－10:00；然后 车流量由高至低的顺序分别为16:00－18:00，12:00－14:00，18:00－20:00和14:00－16:00．在车辆的类别分析当中，车辆数量最多的是摩托车，其次是大部分出现于聚落周边道路的私有车，然后依序分别为自行车、三轮车、出租车和小货车．工作日车流量最多的位置是观测点15，车流量第二、第三高也是在这个地点．工作日车流量的Pearson相关分析结果显示：出租车（r＝０．７５，P＜０．０１）和私有车有显著 正相关；出租车（r＝０．５６，P＜０．０１）和私有车（r＝０．５４，P＜０．０５）与自行车有显著正相关；出租车（r＝ ０．６６，P＜０．０１）和私有车（r＝０．８０，P＜０．０１）与小货车有显著正相关；出租车（r＝０．７８，P＜０．０１）、私 有车（r＝０．７２，P＜０．０１）、自行车（r＝０．５０，P＜０．０１）与小货车（r＝０．５６，P＜０．０１）和三轮车有显著正相关；自行车（r＝０．７８，P＜０．０１）与三轮车（r＝０．４５，P＜０．０５）和摩托车有显著正相关．在休息日的６个观察时段中，车流量从８：００－１０：００逐渐向下递减至１２：００－１４：００，到了１４：００－ １６：００车流量大增，１６：００－１８：００略为下降，最后一个时段的车流量最大．在车辆的分类当中，观察到数 量最多的是摩托车，其次是私有车，然后依序分别是自行车、三轮车、小货车、小巴士、出租车、大货车和 大巴士．休息日车流量最多的地点是观测点１０，第二高的位置是观测点３与１０，第三高则是观测点１０ 与１４．休息日车流量前三高的地方都包含了观测点１０，因为这条街道是城市的主干道．休息日车流量的 Ｐｅａｒｓｏｎ相关分析结果显示：小巴士（r＝０．５９，P＜０．０１）和出租车有显著正相关；小巴士（r＝０．８８,P＜ ０．０１）和出租车（r＝０．８３，P＜０．０１）与私有车有显著正相关；小巴士（r＝０．６５，P＜０．０１）与私有车（r＝ ０．５４，P＜０．０５）和小货车有显著正相关；自行车（r＝０．６２，P＜０．０１）与摩托车有显著正相关.

课程记录：由于我们组的重点是两个设计方案的对比，所以围绕这个中心做出以下提纲与表格

翻译：HKURBANlab 工作文件 视觉图分析 （VGA） 视觉介绍作者：Alain Chiaradia1，戴晓玲2，张玲珠32004-21 伦敦、上海、杭州、香港 1 香港大学，FoA，DUPAD2 浙江工业大学3到同济大学 https://www.academia.edu/45094923/HKURBANlab_Working_Paper_Visual_Graph_Analysis_VGA_A_visual_introduction1. 引言2. T型3. 视野中的不透明空间4. 不透明的空间，在视野中看穿空间5. 视野中大不透明空间6. 在可视化图形分析和的可理解性和可见性迷宫中轴向线分析7. 参考书目

2. PROBLEMATISATIONThis section aims to discuss the outcomes of the previous applications of space syntax segment analysis to Rio de Janeiro (Hillier et al., 2012; Krenz et al., 2015). The purpose is to highlight their critical points concerning the object of this study, i.e., the connections between favelas and the global city's network. By this means, this section relates the hypothesis and conclusions of the analyses mentioned above with the argumentations of two extensive urban studies of Rio de Janeiro, i.e., Abreu's history of the urban evolution (1987; 1994) and Perlman's forty-years-based ethnology on Rio's favelas (2010).2.问题本节旨在讨论先前在里约热内卢应用空间句法片段分析的结果（Hillier等，2012； Krenz等，2015）。 目的是突出他们关于本研究目标的关键点，即贫民窟与全球城市网络之间的联系。 通过这种方式，本节将上述分析的假设和结论与里约热内卢的两项广泛的城市研究的论点联系起来，即阿布鲁乌的城市演变史（1987年； 1994年）和佩尔曼基于四十年的民族学 在里约热内卢的贫民窟（2010）。The main points we discuss here are the following: the historical and economic relation between the residential background and foreground and, by which means, this relation causes different levels of marginalisation between the favelas and the urban system as a whole.我们在这里讨论的要点如下：居住背景和前景之间的历史和经济关系，并通过这种关系在贫民窟和整个城市系统之间造成不同程度的边缘化。

On the contrary, the analyses of Istanbul and Rio present stronger local backgrounds, lacking global-to-local structures. The authors suggest a possible explanation for this kind of structure. That is the rapid residential growth led the construction of the city, by subordinating the economic logic of the foreground to the social logic of the background.相反，对伊斯坦布尔和里约热内卢的分析具有较强的地方背景，缺乏全球对地方的结构。 作者提出了这种结构的可能解释。 那就是通过将前景的经济逻辑从属于背景的社会逻辑，快速的住宅增长引领了城市的建设。On the contrary, the analyses of Istanbul and Rio present stronger local backgrounds, lacking global-to-local structures. The authors suggest a possible explanation for this kind of structure. That is the rapid residential growth led the construction of the city, by subordinating the economic logic of the foreground to the social logic of the background.相反，对伊斯坦布尔和里约热内卢的分析具有较强的地方背景，缺乏全球对地方的结构。 作者提出了这种结构的可能解释。 那就是通过将前景的经济逻辑从属于背景的社会逻辑，快速的住宅增长引领了城市的建设。