dataesr/scientific-paragraphs-categorization huggingface dataset:
Policy Design as a Mathematical Program. e wellbeing function depends on a large and varied set of parameters: energy prices, modal tariffs, the structural factors of transit components, and the average speeds of roadway components. e level of public subsidies to transit supply, denoted by S Y , can be assumed exogenous or endogenous. e policy design problem is modeled as a mathematical program for optimizing overall wellbeing, by acting on the decision variables, and subject to the requirement of transit supply profitability as well as technical constraints (quality of service, elemental times, generalized costs of the modes, and traffic operational processes); cf. Leurent et al. [38]. We implemented the model in Python language and used the Optimization algorithm from the SciPy library in order to compute an optimal state.
Acknowledgments e authors express their gratitude to both Supporting Chairs: IDFM for making the  Household Travel Survey (EGT) available and DRIEA  for providing the "MODUS" travel demand model. e employment frame of  Sheng Li  was supported by two Research Chairs at École des Ponts ParisTech: the Chair in the Eco-design of Built Sets and Infrastructures, sponsored by the  VINCI Group , and the Chair in Territorial Mobility, sponsored by  Île-de-France Mobilités (IDFM).
Data Availability ere are three datasets underpinning the study: first, the Household Travel Survey "EGT 2010" which belongs to IDFM, the Ile-de-France Mobility Organizing Authority; second, the network databases of roadways and transit services in the MODUS model, which belongs to DRIEA; and, third, accountancy statistics about transit networks in the Ile-de-France region. None of these datasets are available on a public basis.
Despite cycle hire schemes being perceived as disproportionately benefiting privileged groups, there is some evidence to suggest that the LCHS has indeed helped to normalise cycling. Goodman et al. (2014)  found that the scheme has a higher proportion of women than those cycling in London overall (32% vs. 23%), and mentions data from the London Travel Demand Survey (LTDS) showing that low income groups tend to make more journeys by foot and bus than underground, implying huge potential for bikeshare in low income areas. The Santander Cycles customer survey (TNS, 2017), the sixth wave conducted since 2012, reveals that in 2017 the LCHS was primarily used by those who were male, young, white, and full-time workers. Analysis by  Morton (2018)  highlighted the importance of understanding the socio-demographic make-up of users, using a market segmentation approach to support decision-making.
Data and methods We used the open source statistical environment R (R Core Team, 2020) for data processing and analysis, making use of packages such as sf and ggplot2 for geographic analysis and visualisation respectively  (Pebesma, 2018; Wickham, 2016). To ensure reproducibility and encourage others to build on the work we have done, by saving time spent on data cleaning, the code underlying the [Note: this repository will be made available on publication. ].
Trip and station datasets Data on docking stations was provided by the Consumer Data Research Centre (data. cdrc. ac. uk). This was made available in a '. csv' file containing 1,130 rows with 19 variables including docking station ID, name, location coordinates and its opening date. The other key dataset used in this study was Origin-Destination (OD) trip data, published by Transport for London and accessed via the bikedata R package  (Padgham and Ellison, 2017). Both datasets were linked via a docking station ID. In the trips table, the origin and destination docking station and corresponding timestamps is recorded. The docking station table contains IDs, names and geographic coordinates for each docking station. A script was developed to clean the raw data, remove duplicate trips and ensure that records omitted from the bikedata package (version 0.
Considering the density of docking stations and access to alternative transport options such as bus stops, we opted for the smallest of the buffer distances tested for the results presented. The use of different buffer distances did not change the direction of the results or conclusions drawn from them (see Discussion). Official population data from the 2011 Census were collected at Output Area and 2015 estimates of the Index of Multiple Deprivation and income deciles at the LSOA level. 7.
Location-based social networks (LBSNs) have rapidly prevailed in China with the increase in smart devices use, which has provided a wide range of opportunities to analyze urban behavior in terms of the use of LBSNs. In a LBSN, users socialize by sharing their location (also referred to as "geolocation") in the form of a tweet (also referred to as a "check-in"), which contains information in the form of, but is not limited to, text, audio, video, etc. , which records the visited place, movement patterns, and activities performed (e. g. , eating, living, working, or leisure). Understanding the user's activities and behavior in space and time using LBSN datasets can be achieved by archiving the daily activities, movement patterns, and social media behavior patterns, thus representing the user's daily routine.
LBSN data, including geolocation, venue location, and venue type, are available at a much lower cost in comparison to the aforementioned traditional methods when observing and analyzing urban activities behavior. The development of LBSNs has progressed rapidly due to the integration of smartphones, which allow for the fast sharing of information about what, where, why, and with whom users share information. The sharing of a users' current location and their ability to discover their friends' locations has raise concerns related to user privacy  [8, 9]. However, LBSN privacy primarily depends on the legislative and business-oriented actors involved in data sharing. Although privacy is an individual issue, some of the personal data are shared unintentionally or willingly by the user  [10, 11].
Historically, research based on urban geography and activities behavior related to the Weibo dataset has been limited in comparison to Facebook and Twitter. However, this research started to increase in China as of 2015, mainly focusing on the characteristics of people in scenic spots, for example, analyzing the spatiotemporal distribution and spatial autocorrelation of tourists with different attributes in the scenic area  [21]. Urban perception research explored the positive and negative attitudes of people toward urban style using the semantic analysis method  [22] ; for work-life balance, the relationship between home and work was tested in the space and time of check-ins and land use classification  [23] ; the work-life balance and commuting scenario was analyzed by spatial clustering method  [24].
Research on city (group) spaces and hotspots has mainly focused on the internal structure and dynamics of urban land-use patterns  [25] , the characteristics of city (group) residents, spatiotemporal variation  [26, 27] , and hotspots in the city  [28]. Various studies were conducted using LBSN datasets to observe human check-in and activities behavior under domains like privacy  [11, 29, 30] , sex differences  [31] , geographic spaces  [32] , urban emotions  [33] , activity location choice, urban green space  [34] , and lifestyle patterns  [35, 36]. Other studies  [37] [38] [39] [40] [41] [42]  focused on observing human mobility patterns and analyzing check-in data for location prediction and venue tagging in the city using LBSN datasets. Li and Chen  [43]  analyzed the user's profile, characteristics, and activities in the form of correlations and social graphs to study and better understand user motivations for sharing their real-world location.
Ebrahimpour et al. [44]  reviewed and compared the current analysis of multisource crowd approaches. The survey provided the basics of crowd analysis by considering video, spatio-temporal, and social media approaches for crowd analysis. Wakamiya et al. [45]  proposed a novel spatial visualization system based on the Twitter dataset for uncovering the collective spatial attention and interest of users not at, but rather toward, different locations to answer the following questions: What do users collectively talk about when they refer to specific geographical places? What do users at a specific place commonly talk about when they refer to another geographical place? Research based on urban activity space and behavior began to emerge in the 1960s  [46, 47] , which mainly focused on the spatially distributed population  [48, 49]  and the geography over time to better understand humans in terms of behavior and land use and their relationships over time.
This research on urban activity space and behavior encouraged our research on urban activities behavior using LBSN data. The current studies based on daily urban activities in space and time  [50, 51]  observed, e. g. , living space, referred to as residence (spatial structure of the living space and the diversity mechanism  [52, 53] ); workspace, referred to as professional; and leisure space, referred to as sports and leisure (focuses on the evolution of leisure time-space characteristics  [54]  and public leisure space selection and activity path  [55, 56] ). Therefore, along the lines of the research mentioned above, the LBSN dataset not only documents the daily lives, activity patterns, and social media usage behavior of users, but also provides spatiotemporal patterns and dynamics that are related to the daily routines and behavior of users.
The LBSN dataset also reflects the current rapid urbanization of the city. In this direction, we aimed to investigate the spatiotemporal information related to check-ins to identify and determine urban activities behavior. The simple hypothesis is that people are often likely to follow a typical daily routine: e. g. , go to work, eat at some preferred restaurant, and go shopping and return home. We explored the LBSN data to observe urban activities behavior in space and time using Sina Weibo (referred as "Weibo"  [57] ), the most famous Chinese microblog that is considered the Chinese version and replacement of Facebook launched by Sina Corporation [58] in 2009. By the third quarter of 2017, Weibo had 376 million monthly active users (MAU) and 172 million daily active users (DAU), where 93% of the active users used mobile devices to access Weibo  [59, 60].
This enormous number of users were attracted worldwide due to fast information sharing and check-in features  [61]. Therefore, if we have enough data to observe distinctive human activities behaviors, this knowledge can be analyzed to study human daily activities behavior. However, others  [62] [63] [64] [65]  explored the LBSN datasets to examine people's daily check-ins, activities behavior, and mobility patterns in different cities. Most of the existing literature focused on Facebook and Twitter rather than Weibo. Therefore, our findings serve to fill this research gap by focusing on the most popular Chinese local social network site, Weibo. Previous studies  [18, 20]  analyzed sex-based activities behavior in Weibo and suggested that female users are more inclined to use Weibo to provide helpful information to others.
According to statistics [67], 72% of the total Sina Weibo users are aged from 20-35 years, among whom the majority of users are in their 20s  [18]. Hence, by analyzing the spatiotemporal information of the Weibo dataset, we can explore urban activities' behavior. This line of research can help improve our understanding of urban activities behavior by considering LBSN data (a source of social media big data) as supplementary to rather than a substitute of traditional data sources when making policy decisions  [68]  associated with urban planning  [69]  and city functions  [70]. The rest of the paper is organized as follows: Section 2 defines the area of study and dataset. Section 3 describes the methodology. Section 4 presents the experimental results. Finally, Section 5 concludes the paper with a proposal for future research.
Check-In Data In the current study, we used the Weibo dataset during January to December 2017 from Shanghai, China, as it contains information like a unique user ID, time and date of the check-in, geolocation (longitude and latitude), venue type, venue category, and sex, which were collected via web or mobile applications  [76]. A total of 921,581 anonymized records of geo-tagged check-ins related to the study area were acquired. The LBSN dataset could therefore archive the day-to-day activity patterns and behaviors toward using social media, and present the spatiotemporal trends of daily life  [77]. An example of a typical Weibo check-in is shown in Table  1.
Check-In Data In the current study, we used the Weibo dataset during January to December 2017 from Shanghai, China, as it contains information like a unique user ID, time and date of the check-in, geolocation (longitude and latitude), venue type, venue category, and sex, which were collected via web or mobile applications  [76]. A total of 921,581 anonymized records of geo-tagged check-ins related to the study area were acquired. The LBSN dataset could therefore archive the day-to-day activity patterns and behaviors toward using social media, and present the spatiotemporal trends of daily life  [77]. An example of a typical Weibo check-in is shown in Table  1. The POI dataset used in the current study were attained from a web-based mapping platform, "AutoNavi. " In total, 123,658 POIs are attained, and we categorized the points into 10 activity categories (Table  2 ) based on the geolocation and type of the visited locations (but not limited to) obtained from Weibo check-in.
About 95. 6% of the check-ins were associated with the category information; the rest of the check-ins with no valid match with the categories were considered invalid. The POI dataset used in the current study were attained from a web-based mapping platform, "AutoNavi. " In total, 123,658 POIs are attained, and we categorized the points into 10 activity categories (Table  2 ) based on the geolocation and type of the visited locations (but not limited to) obtained from Weibo check-in. About 95. 6% of the check-ins were associated with the category information; the rest of the check-ins with no valid match with the categories were considered invalid.
Methodology We analyzed the Weibo-based geolocation dataset (January-December 2017) from Shanghai, China containing information like the unique user ID, sex, time, day, venue type, venue category, and geolocation. Figure  2  presents the activities analytics framework, where the LBSN data analysis methodology involves the two stages: collection, storage, and analysis of LBSN data. The download of Weibo data was the main task in the data collection and storage stage, which was achieved by using a python-based Weibo application programming interface (API) an open interface of Sina Weibo  [78] , which resulted in separate files in JavaScript Object Notation (JSON) format. However, JSON is used as a standard data format and is considered a dominant programming language with the availability of public modules (reading and writing)  [79, 80].
(3) The range of records within the set date and the prescribed time for the sampled data. (4) Every user checked-in twice in a month. We deleted multiple or duplicate data with the same time, same location, and similar content, and specified that a check-in location performed by the same user on the same day counted as one check-in. From 921,581 anonymized check-in records, 887,008 check-in records of activities were obtained after pre-processing for data noise, incomplete records, and invalid and fake users related to POI association and study area. The distribution of check-ins based on months and sex is shown in Figure  3. Finally, in the data insight stage, we mined and further investigated the features related to LBSN check-in data by considering geolocation, time, venue type, venue category, and sex.
From 921,581 anonymized check-in records, 887,008 check-in records of activities were obtained after pre-processing for data noise, incomplete records, and invalid and fake users related to POI association and study area. The distribution of check-ins based on months and sex is shown in Figure  3. Finally, in the data insight stage, we mined and further investigated the features related to LBSN check-in data by considering geolocation, time, venue type, venue category, and sex. However, in the data analysis stage, the critical task is to mine and investigate the features of LBSN data. During data pre-processing, invalid records were excluded by considering four criteria: (1) Full availability of information, i. e. , user ID, date, time, sex, venue type, venue category, and location (longitude and latitude).
The missing check-in information was added by comparing the information with POI, and accurately identifying the check-in location with high similarity, unifying the standard format, and re-integrating the information. (2) The geolocation of the records in the study area concerning the POI location. (3) The range of records within the set date and the prescribed time for the sampled data. (  4 ) Every user checked-in twice in a month. We deleted multiple or duplicate data with the same time, same location, and similar content, and specified that a check-in location performed by the same user on the same day counted as one check-in. From 921,581 anonymized check-in records, 887,008 check-in records of activities were obtained after pre-processing for data noise, incomplete records, and invalid and fake users related to POI association and study area.
To calculate the fine density of activities at location a, let f KD be a density function at geolocation a and A is a set of activities related to the check-ins data: A = {a 1 ,. , a n } (1) where the geolocation of the activity 1 < i < n: a i = <x,y>, where i is individual, and t is time. f KD (a|A, h) = 1 n n i=1 K h a, a i (2) K h a, a i = 1 2πh exp        - 1 2 a -a i t -1 h a -a i        (3) where a is the geolocation of an activity in dataset A with bandwidth h. To produce fine density around activities dataset A at point a i , we assumed the value of bandwidth h depends on the estimated density f KD. However, in the density estimation, bandwidth (larger/smaller) is assumed to be a vital parameter; too fine a density is produced with a large bandwidth, whereas abrupt changes occur with small bandwidth  [87].
Therefore, constantly setting the bandwidth in comparison with the density surface results in achieving the optimal bandwidth. The subsequent density estimate f KD is interdependent on bandwidth h. To construct the density estimate and calculate the value of bandwidth h, the data-driven approach for the log-probability was used, which is given as follows: L(h) = 1 a t a t i=1 log f KD a i A, h (4) where a t represents the events and a i are data points in dataset A.
Density Variation and Distribution of Activities In the current study, we used the geolocation-based check-in dataset, which was obtained from Weibo, and analyzed the density of activities using KDE. Figure  4  presents the overall check-in distribution of activities in Shanghai. A significant amount of check-ins in Shanghai was associated with activities related to Food and Drink (28%), Residence (16%), and Professional and Others (13%). Generally, more time was spent at home than at food and drink locations (e. g. , restaurants, coffee shops). The high amount of check-ins at food and drink facilities may be because they provide free Internet access. ISPRS Int. J. Geo-Inf. 2020, 9, x FOR PEER REVIEW 10 of 35          The check-in densities for Sales and Services, and Tourism were dense in the city center, and low density dispersed amongst the rest of the study area.
e. , transport, health, entertainment. To investigate the activities behavior, we analyzed the data based on sex during weekdays and weekends in Shanghai. Figure  7  presents the sex-based check-in distribution of activities in Shanghai during the weekdays and weekends. Female users used Weibo more when performing activities related to Food and Drink, Hospital and Healthcare, Residence, Shopping, and Tourism. However, men were more inclined to use Weibo when performing activities related to Education and Learning, Professional and Others, Sales and Services, and Travel and Transport. However, we observed that during weekdays, men were more inclined to use Weibo when performing the activities related to Education and Learning, Professional and Others, and Travel and Transport, whereas women were more inclined to use Weibo when performing the activities related to Food and Drink, Hospital and Healthcare, Residence, Sales and Services, Shopping, and Tourism.
Conclusions In this study, we conducted a spatiotemporal investigation of sex-based differences in human activities behavior to explore hourly and daily check-in patterns, as well as during weekdays and weekends. The results showed that in almost all Shanghai districts, women are more likely to consume social media compared to men during the weekdays. However, during the weekends, nearly the same check-in trend was observed for both men and women. The city center was found to have a high density of check-ins near the subways and highways. Supplementing the prior research on check-in behavior, we considered LBSN data as a supplement rather than a substitute for traditional data sources for observation of, for example, human mobility, activity analysis, defining city boundary, and social issues in a city.
Additionally, compared with other traditional data sources, the LBSN dataset has some advantages, including low cost and high spatial precision, and some disadvantages, including sex prejudice, a low frequency of sampling, and location type prejudice. The spatiotemporal dynamics of SDE based on city and district scales exhibited different human behaviors. On the city scale, we observed an activities behavior trend toward the city center in northeast direction. However, the activities behavior trend on the district scale was significantly different from that at a city scale. In general, the movement rate and direction of SDE for different districts were different for various activities, which may be related to the spatial distribution of check-ins. Based on the results of the current study, the LBSN dataset is a novel source of big data that has the potential to provide a new outlook as a supplement for observing human activities behavior and sex differences in space and time.
Generally, increasing numbers of people are preferring to live near the communities where they have easy access to service, i. e. , transport, health, entertainment. To investigate the activities behavior, we analyzed the data based on sex during weekdays and weekends in Shanghai. Figure7presents the sex-based check-in distribution of activities in Shanghai during the weekdays and weekends. Female users used Weibo more when performing activities related to Food and Drink, Hospital and Healthcare, Residence, Shopping, and Tourism. However, men were more inclined to use Weibo when performing activities related to Education and Learning, Professional and Others, Sales and Services, and Travel and Transport. However, we observed that during weekdays, men were more inclined to use Weibo when performing the activities related to Education and Learning, Professional and Others, and Travel and Transport, whereas women were more inclined to use Weibo when performing the activities related to Food and Drink, Hospital and Healthcare, Residence, Sales and Services, Shopping, and Tourism.