scholarly journals Fine-Resolution Population Mapping from International Space Station Nighttime Photography and Multisource Social Sensing Data Based on Similarity Matching

2019 ◽  
Vol 11 (16) ◽  
pp. 1900 ◽  
Author(s):  
Luyao Wang ◽  
Hong Fan ◽  
Yankun Wang
Keyword(s):  
Social Media ◽  
Population Distribution ◽  
Space Station ◽  
Similarity Matching ◽  
Social Media Data ◽  
International Space ◽  
Sensing Data ◽  
Fine Resolution ◽  
Population Mapping ◽  
Media Data

Previous studies have attempted to disaggregate census data into fine resolution with multisource remote sensing data considering the importance of fine-resolution population distribution in urban planning, environmental protection, resource allocation, and social economy. However, the lack of direct human activity information invariably restricts the accuracy of population mapping and reduces the credibility of the mapping process even when external facility distribution information is adopted. To address these problems, the present study proposed a novel population mapping method by combining International Space Station (ISS) photography nighttime light data, point of interest (POI) data, and location-based social media data. A similarity matching model, consisting of semantic and distance matching models, was established to integrate POI and social media data. Effective information was extracted from the integrated data through principal component analysis and then used along with road density information to train the random forest (RF) model. A comparison with WordPop data proved that our method can generate fine-resolution population distribution with higher accuracy ( R 2 = 0.91 ) than those of previous studies ( R 2 = 0.55 ). To illustrate the advantages of our method, we highlighted the limitations of previous methods that ignore social media data in handling residential regions with similar light intensity. We also discussed the performance of our method in adopting social media data, considering their characteristics, with different volumes and acquisition times. Results showed that social media data acquired between 19:00 and 8:00 with a volume of approximately 300,000 will help our method realize high accuracy with low computation burden. This study showed the great potential of combining social sensing data for disaggregating fine-resolution population.

scholarly journals The Random Forest-Based Method of Fine-Resolution Population Spatialization by Using the International Space Station Nighttime Photography and Social Sensing Data

2018 ◽  
Vol 10 (10) ◽  
pp. 1650 ◽  
Author(s):  
Kangning Li ◽  
Yunhao Chen ◽  
Ying Li
Keyword(s):  
Random Forest ◽  
International Space Station ◽  
Population Distribution ◽  
Space Station ◽  
Small Scale ◽  
International Space ◽  
Point Of Interest ◽  
Scale Population ◽  
Fine Resolution ◽  
Population Mapping

Despite the importance of high-resolution population distribution in urban planning, disaster prevention and response, region economic development, and improvement of urban habitant environment, traditional urban investigations mainly focused on large-scale population spatialization by using coarse-resolution nighttime light (NTL) while few efforts were made to fine-resolution population mapping. To address problems of generating small-scale population distribution, this paper proposed a method based on the Random Forest Regression model to spatialize a 25 m population from the International Space Station (ISS) photography and urban function zones generated from social sensing data—point-of-interest (POI). There were three main steps, namely HSL (hue saturation lightness) transformation and saturation calibration of ISS, generating functional-zone maps based on point-of-interest, and spatializing population based on the Random Forest model. After accuracy assessments by comparing with WorldPop, the proposed method was validated as a qualified method to generate fine-resolution population spatial maps. In the discussion, this paper suggested that without help of auxiliary data, NTL cannot be directly employed as a population indicator at small scale. The Variable Importance Measure of the RF model confirmed the correlation between features and population and further demonstrated that urban functions performed better than LULC (Land Use and Land Cover) in small-scale population mapping. Urban height was also shown to improve the performance of population disaggregation due to its compensation of building volume. To sum up, this proposed method showed great potential to disaggregate fine-resolution population and other urban socio-economic attributes.

scholarly journals Comparing the Use of Spatially Explicit Indicators and Conventional Indicators in the Evaluation of Healthy Cities: A Case Study in Shenzhen, China

2020 ◽  
Vol 17 (20) ◽  
pp. 7409
Author(s):  
Jun Yang ◽  
Xiangyu Luo ◽  
Yixiong Xiao ◽  
Shaoqing Shen ◽  
Mo Su ◽  
...  
Keyword(s):  
Social Media ◽  
Population Distribution ◽  
Spatial Accessibility ◽  
Health Determinants ◽  
Spatially Explicit ◽  
Healthy Cities ◽  
Healthy City ◽  
Social Media Data ◽  
Indicator Systems ◽  
Media Data

Various indicator systems have been developed to monitor and assess healthy cities. However, few of them contain spatially explicit indicators. In this study, we assessed four health determinants in Shenzhen, China, using both indicators commonly included in healthy city indicator systems and spatially explicit indicators. The spatially explicit indicators were developed using detailed building information or social media data. Our results showed that the evaluation results of districts and sub-districts in Shenzhen based on spatially explicit indicators could be positively, negatively, or not associated with the evaluation results based on conventional indicators. The discrepancy may be caused by the different information contained in the two types of indicators. The spatially explicit indicators measure the quantity of the determinants and the spatial accessibility of these determinants, while the conventional indicators only measure the quantity. Our results also showed that social media data have great potential to represent the high-resolution population distribution required to estimate spatially explicit indicators. Based on our findings, we recommend that spatially explicit indicators should be included in healthy city indicator systems to allow for a more comprehensive assessment of healthy cities.

Smart De-Identification of Social Media Data

2014 ◽  
Author(s):  
Kathleen M. Carley ◽  
L. R. Carley ◽  
Jonathan Storrick
Keyword(s):  
Social Media ◽  
Social Media Data ◽  
Media Data

Mining Social Media Data to Study the Consequences of Dementia Diagnosis on Caregivers and Relatives (Preprint)

2018 ◽  
Author(s):  
Anika Oellrich ◽  
George Gkotsis ◽  
Richard James Butler Dobson ◽  
Tim JP Hubbard ◽  
Rina Dutta
Keyword(s):  
Social Media ◽  
Family Relationships ◽  
Text Processing ◽  
Automated Analysis ◽  
Health Concern ◽  
Dementia Diagnosis ◽  
Data Set ◽  
Social Media Data ◽  
Real Time Processing ◽  
Media Data

BACKGROUND Dementia is a growing public health concern with approximately 50 million people affected worldwide in 2017 and this number is expected to reach more than 131 million by 2050. The toll on caregivers and relatives cannot be underestimated as dementia changes family relationships, leaves people socially isolated, and affects the finances of all those involved. OBJECTIVE The aim of this study was to explore using automated analysis (i) the age and gender of people who post to the social media forum Reddit about dementia diagnoses, (ii) the affected person and their diagnosis, (iii) relevant subreddits authors are posting to, (iv) the types of messages posted and (v) the content of these posts. METHODS We analysed Reddit posts concerning dementia diagnoses. We used a previously developed text analysis pipeline to determine attributes of the posts as well as their authors to characterise online communications about dementia diagnoses. The posts were also examined by manual curation for the diagnosis provided and the person affected. Furthermore, we investigated the communities these people engage in and assessed the contents of the posts with an automated topic gathering technique. RESULTS Our results indicate that the majority of posters in our data set are women, and it is mostly close relatives such as parents and grandparents that are mentioned. Both the communities frequented and topics gathered reflect not only the sufferer's diagnosis but also potential outcomes, e.g. hardships experienced by the caregiver. The trends observed from this dataset are consistent with findings based on qualitative review, validating the robustness of social media automated text processing. CONCLUSIONS This work demonstrates the value of social media data sources as a resource for in-depth studies of those affected by a dementia diagnosis and the potential to develop novel support systems based on their real time processing in line with the increasing digitalisation of medical care.

Citizens, Elites, and Social Media Methodological Challenges and Opportunities in the Study of Persuasion and Mobilization

2019 ◽  
pp. 1036-1058
Author(s):  
Philip Habel ◽  
Yannis Theocharis
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply And Demand ◽  
Political Process ◽  
The Political ◽  
The Novel ◽  
Complete Understanding ◽  
Social Media Data ◽  
Challenges And Opportunities ◽  
Media Data

In the last decade, big data, and social media in particular, have seen increased popularity among citizens, organizations, politicians, and other elites—which in turn has created new and promising avenues for scholars studying long-standing questions of communication flows and influence. Studies of social media play a prominent role in our evolving understanding of the supply and demand sides of the political process, including the novel strategies adopted by elites to persuade and mobilize publics, as well as the ways in which citizens react, interact with elites and others, and utilize platforms to persuade audiences. While recognizing some challenges, this chapter speaks to the myriad of opportunities that social media data afford for evaluating questions of mobilization and persuasion, ultimately bringing us closer to a more complete understanding Lasswell’s (1948) famous maxim: “who, says what, in which channel, to whom, [and] with what effect.”

Sign in / Sign up

Export Citation Format

Share Document