scholarly journals Generating and Visualizing Spatially Disaggregated Synthetic Population Using a Web-Based Geospatial Service

2021 ◽  
Vol 13 (3) ◽  
pp. 1587
Author(s):  
Jian Liu ◽  
Xiaosu Ma ◽  
Yi Zhu ◽  
Jing Li ◽  
Zong He ◽  
...  
Keyword(s):  
Rapid Development ◽  
Population Data ◽  
Population Characteristics ◽  
Data Sets ◽  
Iterative Proportional Fitting ◽  
Fine Grained ◽  
Synthetic Populations ◽  
Population Information ◽  
Synthetic Population ◽  
Urban Planning And Management

Geographically fine-grained population information is critical for various urban planning and management tasks. This is especially the case for the Chinese cities that are undergoing rapid development and transformation. However, detailed population data are rarely available in comprehensive and timely means. Therefore, appropriate approaches are needed to estimate populations from available data sets in a systematic way to support the continuous demand from urban analytics and planning. Population synthesis approaches such as Iterative Proportional Fitting (IPF) were developed to combine microdata samples with marginal statistics about population characteristics at aggregated spatial levels in order to expand the microdata sample into a complete synthetic population. This paper presents the framework for and the implementation of a geospatial platform for supporting the generation and exploration of spatially detailed urban synthetic populations. The platform provides analytical and visualization tools to support the quick generation of a full urban population with critical attributes based on the latest data available. The case of the synthetic population of Chongqing is used to illustrate the population information and types of visualization that are facilitated.

scholarly journals Synthetic Population Techniques in Activity-Based Research

2014 ◽  
pp. 48-70 ◽  
Author(s):  
Sungjin Cho ◽  
Tom Bellemans ◽  
Lieve Creemers ◽  
Luk Knapen ◽  
Davy Janssens ◽  
...  
Keyword(s):  
Privacy Protection ◽  
Daily Activity ◽  
Input Data ◽  
Population Data ◽  
Demand Forecast ◽  
Working Process ◽  
Iterative Proportional Fitting ◽  
Alternative Source ◽  
Traffic Demand ◽  
Synthetic Population

Activity-based approach, which aims to estimate an individual induced traffic demand derived from activities, has been applied for traffic demand forecast research. The activity-based approach normally uses two types of input data: daily activity-trip schedule and population data, as well as environment information. In general, it seems hard to use those data because of privacy protection and expense. Therefore, it is indispensable to find an alternative source to population data. A synthetic population technique provides a solution to this problem. Previous research has already developed a few techniques for generating a synthetic population (e.g. IPF [Iterative Proportional Fitting] and CO [Combinatorial Optimization]), and the synthetic population techniques have been applied for the activity-based research in transportation. However, using those techniques is not easy for non-expert researchers not only due to the fact that there are no explicit terminologies and concrete solutions to existing issues, but also every synthetic population technique uses different types of data. In this sense, this chapter provides a potential reader with a guideline for using the synthetic population techniques by introducing terminologies, related research, and giving an account for the working process to create a synthetic population for Flanders in Belgium, problematic issues, and solutions.

scholarly journals Investigating the Temporal Effect of User Preferences with Application in Movie Recommendation

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Wen-Jun Li ◽  
Qiang Dong ◽  
Yan Fu
Keyword(s):  
Rapid Development ◽  
User Preferences ◽  
Mobile Internet ◽  
Smart Devices ◽  
Data Sets ◽  
Rating Data ◽  
Temporal Effect ◽  
Proposed Model ◽  
Target User ◽  
The Difference

As the rapid development of mobile Internet and smart devices, more and more online content providers begin to collect the preferences of their customers through various apps on mobile devices. These preferences could be largely reflected by the ratings on the online items with explicit scores. Both of positive and negative ratings are helpful for recommender systems to provide relevant items to a target user. Based on the empirical analysis of three real-world movie-rating data sets, we observe that users’ rating criterions change over time, and past positive and negative ratings have different influences on users’ future preferences. Given this, we propose a recommendation model on a session-based temporal graph, considering the difference of long- and short-term preferences, and the different temporal effect of positive and negative ratings. The extensive experiment results validate the significant accuracy improvement of our proposed model compared with the state-of-the-art methods.

scholarly journals Linking Spinal Cord Injury Data Sets to Describe the Patient Journey Following Injury: A Protocol

2020 ◽  
Vol 26 (4) ◽  
pp. 232-242
Author(s):  
Vanessa K. Noonan ◽  
Susan B. Jaglal ◽  
Suzanne Humphreys ◽  
Shawna Cronin ◽  
Zeina Waheed ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Administrative Data ◽  
Clinical Data ◽  
Linked Data ◽  
Population Data ◽  
Data Sets ◽  
Patient Journey ◽  
Cord Injury ◽  
Incident Cases

Background: To optimize traumatic spinal cord injury (tSCI) care, administrative and clinical linked data are required to describe the patient’s journey. Objectives: To describe the methods and progress to deterministically link SCI data from multiple databases across the SCI continuum in British Columbia (BC) and Ontario (ON) to answer epidemiological and health service research questions. Methods: Patients with tSCI will be identified from the administrative Hospital Discharge Abstract Database using International Classification of Diseases (ICD) codes from Population Data BC and ICES data repositories in BC and ON, respectively. Admissions for tSCI will range between 1995–2017 for BC and 2009-2017 for ON. Linkage will occur with multiple administrative data holdings from Population Data BC and ICES to create the “Admin SCI Cohorts.” Clinical data from the Rick Hansen SCI Registry (and VerteBase in BC) will be transferred to Population Data BC and ICES. Linkage of the clinical data with the incident cases and administrative data at Population Data BC and ICES will create subsets of patients referred to as the “Clinical SCI Cohorts” for BC and ON. Deidentified patient-level linked data sets will be uploaded to a secure research environment for analysis. Data validation will include several steps, and data analysis plans will be created for each research question. Discussion: The creation of provincially linked tSCI data sets is unique; both clinical and administrative data are included to inform the optimization of care across the SCI continuum. Methods and lessons learned will inform future data-linking projects and care initiatives.

Lingering neighbourhood effects: A framework to account for residential histories and temporal dynamics

Urban Studies ◽  
2016 ◽  
Vol 55 (1) ◽  
pp. 151-174 ◽  
Author(s):  
Emily M Miltenburg ◽  
Tom WG van der Meer
Keyword(s):  
Temporal Dynamics ◽  
Single Point ◽  
Population Data ◽  
Blind Spot ◽  
Neighbourhood Effects ◽  
Temporal Dimension ◽  
Individual Level ◽  
Fine Grained ◽  
Conventional Models ◽  
Total Body

The large and growing body of neighbourhood effect studies has almost exclusively neglected individuals’ particular residential histories. Yet, former residential neighbourhoods are likely to have lingering effects beyond those of the current one and are dependent on exposure times and number of moves. This paper tests to what extent this blind spot induced a misestimation of neighbourhood effects for individuals with differential residential histories. Ultimately, we develop a methodological framework for studying the temporal dynamics of neighbourhood effects, capable of dealing with residential histories (moving behaviour, the passage of time and temporal exposure to different neighbourhoods). We apply cross-classified multi-level models (residents nested in current and former neighbourhoods) to analyse longitudinal individual-level population data from Dutch Statistics, covering fine-grained measures of residential histories. Our systematic comparison to conventional models reveals the necessity of including a temporal dimension: our models reveal an overestimation of the effect of the current neighbourhood by 16–30%, and an underestimation of the total body of neighbourhood effects by at least 13–24%. Our results show that neighbourhood effects are lingering, long-lasting and structural and also cannot be confined to a single point in time.

Electronic Records Management - An Old Solution to a New Problem

Big Data ◽  
2016 ◽  
pp. 2249-2274
Author(s):  
Chinh Nguyen ◽  
Rosemary Stockdale ◽  
Helana Scheepers ◽  
Jason Sargent
Keyword(s):  
Big Data ◽  
Rapid Development ◽  
Large Data ◽  
Large Data Sets ◽  
Electronic Records ◽  
Future Research ◽  
Records Management ◽  
Data Sets ◽  
Interactive Nature ◽  
Electronic Records Management

The rapid development of technology and interactive nature of Government 2.0 (Gov 2.0) is generating large data sets for Government, resulting in a struggle to control, manage, and extract the right information. Therefore, research into these large data sets (termed Big Data) has become necessary. Governments are now spending significant finances on storing and processing vast amounts of information because of the huge proliferation and complexity of Big Data and a lack of effective records management. On the other hand, there is a method called Electronic Records Management (ERM), for controlling and governing the important data of an organisation. This paper investigates the challenges identified from reviewing the literature for Gov 2.0, Big Data, and ERM in order to develop a better understanding of the application of ERM to Big Data to extract useable information in the context of Gov 2.0. The paper suggests that a key building block in providing useable information to stakeholders could potentially be ERM with its well established governance policies. A framework is constructed to illustrate how ERM can play a role in the context of Gov 2.0. Future research is necessary to address the specific constraints and expectations placed on governments in terms of data retention and use.

Social data collection and analyses

2021 ◽  
pp. 53-76
Author(s):  
Marie J. E. Charpentier ◽  
Marie Pelé ◽  
Julien P. Renoult ◽  
Cédric Sueur
Keyword(s):  
Social Relationships ◽  
Information Diffusion ◽  
Social Systems ◽  
Large Data ◽  
Natural Environments ◽  
Data Sets ◽  
Large Array ◽  
Fine Grained ◽  
Network Analyses ◽  
Analytical Tools

Sampling accurate and quantitative behavioural data requires the description of fine-grained patterns of social relationships and/or spatial associations, which is highly challenging, especially in natural environments. Although behavioural ecologists have tackled systematic studies on animals’ societies since the nineteenth century, new biologging technologies have the potential to revolutionise the sampling of animals’ social relationships. However, the tremendous quantity of data sampled and the diversity of biologgers (such as proximity loggers) currently available that allow the sampling of a large array of biological and physiological data bring new analytical challenges. The high spatiotemporal resolution of data needed when studying social processes, such as disease or information diffusion, requires new analytical tools, such as social network analyses, developed to analyse large data sets. The quantity and quality of the data now available on a large array of social systems bring undiscovered outputs, consistently opening new and exciting research avenues.

scholarly journals Using Greedy Random Adaptive Procedure to Solve the User Selection Problem in Mobile Crowdsourcing

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3158
Author(s):  
Jian Yang ◽  
Xiaojuan Ban ◽  
Chunxiao Xing
Keyword(s):  
Mobile Networks ◽  
Large Scale ◽  
Rapid Development ◽  
Synthetic Data ◽  
Data Sets ◽  
User Selection ◽  
Adaptive Procedure ◽  
Mobile Crowdsourcing ◽  
Marginal Gain ◽  
The Cost

With the rapid development of mobile networks and smart terminals, mobile crowdsourcing has aroused the interest of relevant scholars and industries. In this paper, we propose a new solution to the problem of user selection in mobile crowdsourcing system. The existing user selection schemes mainly include: (1) find a subset of users to maximize crowdsourcing quality under a given budget constraint; (2) find a subset of users to minimize cost while meeting minimum crowdsourcing quality requirement. However, these solutions have deficiencies in selecting users to maximize the quality of service of the task and minimize costs. Inspired by the marginalism principle in economics, we wish to select a new user only when the marginal gain of the newly joined user is higher than the cost of payment and the marginal cost associated with integration. We modeled the scheme as a marginalism problem of mobile crowdsourcing user selection (MCUS-marginalism). We rigorously prove the MCUS-marginalism problem to be NP-hard, and propose a greedy random adaptive procedure with annealing randomness (GRASP-AR) to achieve maximize the gain and minimize the cost of the task. The effectiveness and efficiency of our proposed approaches are clearly verified by a large scale of experimental evaluations on both real-world and synthetic data sets.

scholarly journals PENGARUH GENDER, DAYA TARIK PROMOSI, KEPEMILIKAN KARTU KREDIT TERHADAP PERILAKU PEMBELIAN IMPULSIF ONLINE PRODUK LIFESTYLE

2018 ◽  
Vol 7 (10) ◽  
pp. 5570
Author(s):  
Made Santika Putra ◽  
I Wayan Santika
Keyword(s):  
Credit Card ◽  
Positive Impact ◽  
Rapid Development ◽  
Linear Regression Analysis ◽  
Population Data ◽  
Purchase Behavior ◽  
Buying Behavior ◽  
New Paradigm ◽  
Business World ◽  
Impulsive Buying

Technological advances create a new paradigm in the business world. The rapid development of internet usage will have a positive impact for online business in Indonesia. One of the most desirable consumer behavior by marketers who use online media is the impulsive purchase behavior. This study aims to determine the effect of gender, attractiveness of promotion and ownership of credit cards against impulsive buying behavior online. This research was conducted in Denpasar City involving 60 respondents through purposive sampling method. This method was chosen because it is not known exactly the number of population. Data were collected through questionnaires. Data analysis technique used is multiple linear regression analysis technique.The results of this study found that gender positively and positively affects online impulsive buying behavior, promotional appeal positively and significantly affects online impulsive buying behavior and credit card ownership positively and significantly affects online impulsive buying behavior. Marketers are expected to be more accurate in determining market segmentation, more creative in promoting and able to provide convenience and convenience in the payment process when shopping online. Keywords: gender, attraction of promotion, credit card ownership, impulsive buying behavior.

The Estimation of Population Microdata by Using Data from Small Area Statistics and Samples of Anonymised Records

1998 ◽  
Vol 30 (5) ◽  
pp. 785-816 ◽  
Author(s):  
P Williamson ◽  
M Birkin ◽  
P H Rees
Keyword(s):  
Small Area ◽  
Census Data ◽  
Population Data ◽  
Combinatorial Optimisation ◽  
Population Characteristics ◽  
Test Statistics ◽  
Enumeration District ◽  
Small Area Statistics ◽  
Using Data ◽  
Combining Information

Census data can be represented both as lists and as tabulations of household/individual attributes. List representation of Census data offers greater flexibility, as the exploration of interrelationships between population characteristics is limited only by the quality and scope of the data collected. Unfortunately, the released lists of household/individual attributes (Samples of Anonymised Records, SARs) are spatially referenced only to areas (single or merged districts) with populations of 120 000 or more, whereas released tabulations are available for units as small as single enumeration districts (Small Area Statistics, SAS). Intuitively, it should be possible to derive list-based estimates of enumeration district populations by combining information contained in the SAR and the SAS. In this paper we explore the range of solutions that could be adapted to this problem which, ultimately, is presented as a complex combinatorial optimisation problem. Various techniques of combinatorial optimisation are tested, and preliminary results from the best performing algorithm are evaluated. Through this process, the lack of suitable test statistics for the comparison of observed and expected tabulations of population data is highlighted.

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