| 9701. |
- Li, Wenjuan, 1963-, et al.
(författare)
-
Linking spatial scale to changes in workplace earnings : an exploratory approach
- 2015
-
Ingår i: CyberGeo. - : Cybergeo. - 1278-3366.
-
Tidskriftsartikel (refereegranskat)abstract
- The paper investigates the importance of spatial scale for changes in earnings at the workplace by using spatial regression applied to workplace-level micro data in an exploratory purpose. A floating grid technique is used to define equal-sized workplaces and their daily-reach surrounding zones as divided into three spatial entities: working-square, local area and hinterland. On the basis of geo-referenced information on workplaces and places of residence along with numerous individual-level socio-economic indicators, the results of the regression models reveal that the indicators of the daily-reach area play a dominant role and that their contribution varies over spatial entities. Among the spatial entities, the working-square (km square) surrounding the workplace is more important than the workplace itself, the local area and the hinterland. Moreover, the results suggest that internal factors related to population size, diversity of trade and industry and educational level contribute to about one-third of changes in work income at the workplace level. It can be concluded that knowledge, learning and human capital are strongly associated with increased earnings.
|
|
| 9702. |
|
|
| 9703. |
- Li, Xin, et al.
(författare)
-
Towards a sustainable city : Deciphering the determinants of restorative park and spatial patterns
- 2024
-
Ingår i: Sustainable cities and society. - : Elsevier. - 2210-6707. ; 104
-
Tidskriftsartikel (refereegranskat)abstract
- Urban parks have been found to provide mental health benefits. Some empirical studies have tested natural features and perceptual measures respectively, announcing their contribution to psychological restoration. However, inconsistent findings were occasionally reported, whereas few attempts have been made to combine both observed and perceptual factors for validation. Little is known about the variation of restorative drivers and their spatial patterns. To address these problems, this study combined public participation geographic information system (PPGIS) and deep learning method to capture visual qualities of landscape features along with several important perceptual measures. A typical urban park in Wuhan, China, was selected for a pilot study, and 1560 crowdsourced on-site images were collected, with thematic and geographic information being integrated. A series of statistical models, e.g., OLS, QRM, and MGWR, were employed successively for validation. The results showed that landscape preference, place attachment, greenery and water were validated as the global explanatory factors to estimate the conditional mean of psychological restoration. The variation of influential effects of these factors were detected at different restoration levels. There exist spatial heterogeneity for these influential factors on restorative effects. Findings provided new knowledge on a deeper understanding of the subtlety of restoration drivers and their spatial patterns. The findings offered useful insights and guidance for urban planners in creating high-quality green parks with restorative values.
|
|
| 9704. |
- Li, Y., et al.
(författare)
-
Why some rural areas decline while some others not : An overview of rural evolution in the world
- 2019
-
Ingår i: Journal of Rural Studies. - : Elsevier. - 0743-0167 .- 1873-1392. ; 68, s. 135-143
-
Tidskriftsartikel (refereegranskat)abstract
- Rural decline is an inevitable process as human society transforms from the agrarian to the urban-industrial economy, and further on to the knowledge economy. Through an extensive literature review, this paper aims to interpret why some rural areas decline while some others do not. The findings show that it is by the interactions between rural areas and the external environment that rural communities either grow, decline or even vanish. The paper emphasizes the necessity to improve rural communities' resilient capacity through adjusting their internal components' function and structure to survive the external changes. In this process, rural livelihood diversification, the creation of market oriented institutions and strong social capital are considered to enhance rural resilience and build up sustaining rural communities. Finally, three conditions for sustainable rural development in the knowledge economy are discussed: 1) development of new economic activities that can respond to potential urban demand; 2) local entrepreneurship that can establish and expand these new activities; and 3) social capital that can support the entrepreneurship in new activities with access to credits, labor, human capital, external markets and external knowledge for learning and innovation.
|
|
| 9705. |
- Liao, Yuan, 1991, et al.
(författare)
-
A Mobility Model for Synthetic Travel Demand from Sparse Traces
- 2022
-
Ingår i: IEEE Open Journal of Intelligent Transportation Systems. - 2687-7813. ; 3, s. 665-678
-
Tidskriftsartikel (refereegranskat)abstract
- Knowing how much people travel is essential for transport planning. Empirical mobility traces collected from call detail records (CDRs), location-based social networks (LBSNs), and social media data have been used widely to study mobility patterns. However, these data suffer from sparsity, an issue that has largely been overlooked. In order to extend the use of these low-cost and accessible data, this study proposes a mobility model that fills the gaps in sparse mobility traces from which one can later synthesise travel demand. The proposed model extends the fundamental mechanisms of exploration and preferential return to synthesise mobility trips. The model is tested on sparse mobility traces from Twitter. We validate our model and find good agreement on origin-destination matrices and trip distance distributions for Sweden, the Netherlands, and Saõ Paulo, Brazil, compared with a benchmark model using a heuristic method, especially for the most frequent trip distance range (1-40 km). Moreover, the learned model parameters are found to be transferable from one region to another. Using the proposed model, reasonable travel demand values can be synthesised from a dataset covering a large enough population of very sparse individual geolocations (around 1.5 geolocations per day covering 100 days on average).
|
|
| 9706. |
- Liao, Yuan, 1991, et al.
(författare)
-
From individual to collective behaviours: exploring population heterogeneity of human mobility based on social media data
- 2019
-
Ingår i: EPJ Data Science. - : Springer Science and Business Media LLC. - 2193-1127. ; 8:1
-
Tidskriftsartikel (refereegranskat)abstract
- This paper examines the population heterogeneity of travel behaviours from a combined perspective of individual actors and collective behaviours. We use a social media dataset of 652,945 geotagged tweets generated by 2,933 Swedish Twitter users covering an average time span of 3.6 years. No explicit geographical boundaries, such as national borders or administrative boundaries, are applied to the data. We use spatial features, such as geographical characteristics and network properties, and apply a clustering technique to reveal the heterogeneity of geotagged activity patterns. We find four distinct groups of travellers: local explorers (78.0%), local returners (14.4%), global explorers (7.3%), and global returners (0.3%). These groups exhibit distinct mobility characteristics, such as trip distance, diffusion process, percentage of domestic trips, visiting frequency of the most-visited locations, and total number of geotagged locations. Geotagged social media data are gradually being incorporated into travel behaviour studies as user-contributed data sources. While such data have many advantages, including easy access and the flexibility to capture movements across multiple scales (individual, city, country, and globe), more attention is still needed on data validation and identifying potential biases associated with these data. We validate against the data from a household travel survey and find that despite good agreement of trip distances (one-day and long-distance trips), we also find some differences in home location and the frequency of international trips, possibly due to population bias and behaviour distortion in Twitter data. Future work includes identifying and removing additional biases so that results from geotagged activity patterns may be generalised to human mobility patterns. This study explores the heterogeneity of behavioural groups and their spatial mobility including travel and day-to-day displacement. The findings of this paper could be relevant for disease prediction, transport modelling, and the broader social sciences.
|
|
| 9707. |
- Liao, Yuan, 1991, et al.
(författare)
-
Predictability in Human Mobility based on Geographical-boundary-free and Long-time Social Media Data
- 2018
-
Ingår i: 2018 21st International Conference on Intelligent Transportation Systems (ITSC). - 2153-0017. - 9781728103211 ; 2018-November, s. 2068-2073
-
Konferensbidrag (refereegranskat)abstract
- Understanding of predictability in human mobility benefits a broad spectrum such as urban planning and traffic forecasting. In human mobility studies, geotagged social media data are being gradually accepted as a user-contributed data source. It remains unclear to what extent we can use geotagged social media data to predict individual mobility. In the present study, a dataset is collected and applied which includes 652,945 geotagged tweets generated by 2,933 Swedish users covering time spans of more than one year (3.6 years on average). Based on such a dataset, human mobility predictability has been explored from three aspects: 1) time history of mobility range indicating how people diffuse in space, 2) entropy and the corresponding predictability of mobility, and 3) the limits of predictability dependent on geographical boundaries and mobility range. This study reveals a dataset that captures Twitter users' mobility where they routinely visit a couple of regions at most of the time and occasionally explore new regions. A 70% potential predictability is obtained by measuring the entropy of each individual's geotagged activity trajectory using a half-day time interval. The predictability's dependence on mobility range is prolonged when the observation of mobility is geographical-boundary-free which also decreases predictability.
|
|
| 9708. |
- Liao, Yuan, 1991
(författare)
-
Understanding Mobility and Transport Modal Disparities Using Emerging Data Sources: Modelling Potentials and Limitations
- 2021
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Transportation presents a major challenge to curb climate change due in part to its ever-increasing travel demand. Better informed policy-making requires up-to-date empirical mobility data to model viable mitigation options for reducing emissions from the transport sector. On the one hand, the prevalence of digital technologies enables a large-scale collection of human mobility traces, providing big potentials for improving the understanding of mobility patterns and transport modal disparities. On the other hand, the advancement in data science has allowed us to continue pushing the boundary of the potentials and limitations, for new uses of big data in transport. This thesis uses emerging data sources, including Twitter data, traffic data, OpenStreetMap (OSM), and trip data from new transport modes, to enhance the understanding of mobility and transport modal disparities, e.g., how car and public transit support mobility differently. Specifically, this thesis aims to answer two research questions: (1) What are the potentials and limitations of using these emerging data sources for modelling mobility? (2) How can these new data sources be properly modelled for characterising transport modal disparities? Papers I-III model mobility mainly using geotagged social media data, and reveal the potentials and limitations of this data source by validating against established sources (Q1). Papers IV-V combine multiple data sources to characterise transport modal disparities (Q2) which further demonstrate the modelling potentials of the emerging data sources (Q1). Despite a biased population representation and low and irregular sampling of the actual mobility, the geolocations of Twitter data can be used in models to produce good agreements with the other data sources on the fundamental characteristics of individual and population mobility. However, its feasibility for estimating travel demand depends on spatial scale, sparsity, sampling method, and sample size. To extend the use of social media data, this thesis develops two novel approaches to address the sparsity issue: (1) An individual-based mobility model that fills the gaps in the sparse mobility traces for synthetic travel demand; (2) A population-based model that uses Twitter geolocations as attractions instead of trips for estimating the flows of people between regions. This thesis also presents two reproducible data fusion frameworks for characterising transport modal disparities. They demonstrate the power of combining different data sources to gain new insights into the spatiotemporal patterns of travel time disparities between car and public transit, and the competition between ride-sourcing and public transport.
|
|
| 9709. |
- Lidestav, Gun, et al.
(författare)
-
Forest social values : the case of Dalasjo, Sweden
- 2020
-
Ingår i: Scandinavian Journal of Forest Research. - : Taylor & Francis Group. - 0282-7581 .- 1651-1891. ; 35:3-4, s. 177-185
-
Tidskriftsartikel (refereegranskat)abstract
- Forest social values has been put forward as an umbrella term for a new and less material relationship between people and the forested landscape, a relationship that more recently has become a policy issue. In this case study we explore how forest-related values are conceptualised in the case of Dalasjo, in Vilhelmina, Sweden, where a recent process involving determining protection for social values took place in relation to, and simultaneous with, governmental considerations regarding the application of a social values concept. By means of focus group and key informant interviews, the study demonstrates that forest social values are not only about forest per se, or even the physical and user values per se. Further, the diverse but still general understanding of forest social values on a policy level is demonstrated. This stands in contrast to the specific and place-based understanding of the local community, emanating from both individual and collective experiences. Thus, it is concluded that a policy use of social values terms, which may be difficult to identify from the outside, makes the definition of values in specific land use conflicts more complex rather than offering a possibility to immediately provide clear basis for planning tools.
|
|
| 9710. |
- Lidestav, Gun, et al.
(författare)
-
Forests in common and their contribution to local development
- 2017
-
Ingår i: Globalisation and change in forest ownership and forest use. - London : Palgrave Macmillan. - 9781137571168 - 9781137571151 ; , s. 261-302
-
Bokkapitel (refereegranskat)abstract
- In this chapter, we look at the role that a forest held in common can play in supporting local development and promoting the livelihood of the local community. Four dissimilar cases in Italy, Slovenia, Sweden and UK are described and analysed by applying the Sustainable Livelihood Framework. Despite very different pre-requisites and local conditions, our results show that all cases contain rules to maintain the extent and function of natural assets, and they contributed to the mobilisation of different types of capital. In each case there is evidence of interaction with higher governance levels, which protects the group’s room for action. The provision of access to natural and physical resources for rural people in a broader sense illustrates the cases’ orientation towards public good.
|
|
