79,632 research outputs found
Demographic Inference and Representative Population Estimates from Multilingual Social Media Data
Social media provide access to behavioural data at an unprecedented scale and
granularity. However, using these data to understand phenomena in a broader
population is difficult due to their non-representativeness and the bias of
statistical inference tools towards dominant languages and groups. While
demographic attribute inference could be used to mitigate such bias, current
techniques are almost entirely monolingual and fail to work in a global
environment. We address these challenges by combining multilingual demographic
inference with post-stratification to create a more representative population
sample. To learn demographic attributes, we create a new multimodal deep neural
architecture for joint classification of age, gender, and organization-status
of social media users that operates in 32 languages. This method substantially
outperforms current state of the art while also reducing algorithmic bias. To
correct for sampling biases, we propose fully interpretable multilevel
regression methods that estimate inclusion probabilities from inferred joint
population counts and ground-truth population counts. In a large experiment
over multilingual heterogeneous European regions, we show that our demographic
inference and bias correction together allow for more accurate estimates of
populations and make a significant step towards representative social sensing
in downstream applications with multilingual social media.Comment: 12 pages, 10 figures, Proceedings of the 2019 World Wide Web
Conference (WWW '19
The value of traditional rural landscape and nature protected areas in tourism demand: A study on agritourists' preferences
This study focuses on how traditional rural landscape and proximity to a Natura 2000 Site of Community Importance (SCI) might influence consumers\u2019 choice of an agritourism farm for a weekend stay. Data were collected in Umbria region\u2019s (Italy) agritourism farms in 2014 by interviewing 160 tourists. Results from a discrete choice experiment reveal that the most important feature affecting the interviewees\u2019 propensity to pay a premium price to stay in an agritourism farm is the well-preserved traditional landscape (willingness to pay 32.32\u20ac/night for two people), followed by the availability of a swimming pool (willingness to pay 20.95\u20ac/night for two people), the proximity to a historical village (willingness to pay 18.37\u20ac/night for two people) and, the location in a Natura 2000 SCI (willingness to pay 13.57\u20ac/night for two people). Furthermore, the results underline how the preservation of the traditional landscape and protection of the surrounding environment play a strategic role in developing agritourism and provide economic benefits to local communities
Assessment of the genetic basis of rosacea by genome-wide association study.
Rosacea is a common, chronic skin disease that is currently incurable. Although environmental factors influence rosacea, the genetic basis of rosacea is not established. In this genome-wide association study, a discovery group of 22,952 individuals (2,618 rosacea cases and 20,334 controls) was analyzed, leading to identification of two significant single-nucleotide polymorphisms (SNPs) associated with rosacea, one of which replicated in a new group of 29,481 individuals (3,205 rosacea cases and 26,262 controls). The confirmed SNP, rs763035 (P=8.0 Ă— 10(-11) discovery group; P=0.00031 replication group), is intergenic between HLA-DRA and BTNL2. Exploratory immunohistochemical analysis of HLA-DRA and BTNL2 expression in papulopustular rosacea lesions from six individuals, including one with the rs763035 variant, revealed staining in the perifollicular inflammatory infiltrate of rosacea for both proteins. In addition, three HLA alleles, all MHC class II proteins, were significantly associated with rosacea in the discovery group and confirmed in the replication group: HLA-DRB1*03:01 (P=1.0 Ă— 10(-8) discovery group; P=4.4 Ă— 10(-6) replication group), HLA-DQB1*02:01 (P=1.3 Ă— 10(-8) discovery group; P=7.2 Ă— 10(-6) replication group), and HLA-DQA1*05:01 (P=1.4 Ă— 10(-8) discovery group; P=7.6 Ă— 10(-6) replication group). Collectively, the gene variants identified in this study support the concept of a genetic component for rosacea, and provide candidate targets for future studies to better understand and treat rosacea
(WP 2010-11) The Benefits of Environmental Improvement: Estimates From Space-time Analysis
This paper develops estimates of environmental improvement based on a two-stage hedonic price analysis of the single family housing market in the Puget Sound region of Washington State. The analysis — which focuses specifically on several EPA-designated environmental hazards and involves 226,918 transactions for 177,303 unique properties that took place between January 2001 and September 2009 — involves four steps: (i) ten hedonic price functions are estimated year-by-year, one for each year of the 2000s; (ii) the hedonic estimates are used to compute the marginal implicit price of distance from air release, superfund, and toxic release sites; (iii) the marginal implicit prices, which vary through time, are used to estimate a series of implicit demand functions describing the relationship between the price of distance and the quantity consumed; and, finally (iv) the demand estimates are compared to those obtained in other research and then used evaluate the potential scale of benefits associated with some basic environmental improvement scenarios. Overall, the analysis provides further evidence that it is possible to develop a structural model of implicit demand within a single housing market and suggests that the benefits of environmental improvement are substantial
Valuing Environmental Quality: A Space-Based Strategy
This paper develops and applies a space-based strategy for overcoming the general problem of getting at the demand for non-market goods. It focuses specifically on evaluating one form of environmental quality, distance from EPA designated environmental hazards, via the single-family housing market in the Puget Sound region of Washington State. A spatial two stage hedonic price analysis is used to: (1) estimate the marginal implicit price of distance from air release sites, hazardous waste generators, hazardous waste handlers, superfund sites, and toxic release sites; and (2) estimate a series of demand functions describing the relationship between the price of distance and the quantity consumed. The analysis, which represents a major step forward in the valuation of environmental quality, reveals that the information needed to identify second-stage demand functions is hidden right in plain site — hanging in the aether of the regional housing market.Environmental Quality, Hedonic Price Analysis
Fine-Grained Car Detection for Visual Census Estimation
Targeted socioeconomic policies require an accurate understanding of a
country's demographic makeup. To that end, the United States spends more than 1
billion dollars a year gathering census data such as race, gender, education,
occupation and unemployment rates. Compared to the traditional method of
collecting surveys across many years which is costly and labor intensive,
data-driven, machine learning driven approaches are cheaper and faster--with
the potential ability to detect trends in close to real time. In this work, we
leverage the ubiquity of Google Street View images and develop a computer
vision pipeline to predict income, per capita carbon emission, crime rates and
other city attributes from a single source of publicly available visual data.
We first detect cars in 50 million images across 200 of the largest US cities
and train a model to predict demographic attributes using the detected cars. To
facilitate our work, we have collected the largest and most challenging
fine-grained dataset reported to date consisting of over 2600 classes of cars
comprised of images from Google Street View and other web sources, classified
by car experts to account for even the most subtle of visual differences. We
use this data to construct the largest scale fine-grained detection system
reported to date. Our prediction results correlate well with ground truth
income data (r=0.82), Massachusetts department of vehicle registration, and
sources investigating crime rates, income segregation, per capita carbon
emission, and other market research. Finally, we learn interesting
relationships between cars and neighborhoods allowing us to perform the first
large scale sociological analysis of cities using computer vision techniques.Comment: AAAI 201
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
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Assessment of the Employment Accessibility Benefits of Shared Autonomous Mobility Services
The goal of this study is to assess and quantify the potential employment accessibility benefits of Shared Autonomous Mobility Service (SAMS) commute modes across a large diverse metropolitan region considering heterogeneity in the working population. To meet this goal, this study employs a welfare-based (i.e. logsum-based) measure of accessibility, obtained via estimating a hierarchical work destination-commute mode choice model. The employment accessibility logsum measure incorporates the spatial distribution of worker residences and employment opportunities, the attributes of the available commute modes, and the characteristics of individual workers. This research further captures heterogeneity of workers using latent class analysis (LCA). The LCA model inputs include the socio-demographic characteristics of workers to subsequently account for different worker clusters valuing different types of employment opportunities differently. The accessibility analysis results indicate: (i) the accessibility benefit differences across latent classes are modest but young workers and low-income workers do see higher benefits than high- and middle-income workers; (ii) there are substantial spatial differences in accessibility benefits with workers living in lower density areas benefiting more than workers living in high-density areas; (iii) nearly all the accessibility benefits come from the SAMS-only mode as opposed to the SAMS+Transit mode; and (iv) the SAMS cost per mile assumption significantly impacts the magnitude of the overall employment accessibility benefits
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