A Data-Driven Framework for Exploring the Spatial Distribution of Industries

LISS 2020 ◽  
2021 ◽  
pp. 995-1007
Author(s):  
Huifeng Sun
Keyword(s):  
Spatial Distribution ◽  
Data Driven

Airbnb and the Hotel Industry in Warsaw: An Example of the Sharing Economy?

2018 ◽  
Vol 2 (49) ◽  
pp. 23-34 ◽  
Author(s):  
Kristóf Gyódi
Keyword(s):  
Spatial Distribution ◽  
Policy Making ◽  
Hotel Industry ◽  
Sharing Economy ◽  
District Level ◽  
Data Driven ◽  
Spatial Concentration ◽  
Living Space ◽  
The City ◽  
Unique Dataset

Abstract The aim of this analysis is to examine the characteristics of the Airbnb network, to verify the share of Airbnb offers that belong to the sharing economy and to identify the differences between the spatial distribution of the Airbnb network and the traditional hotel industry. The article is based on a unique dataset of web-scraped data on Airbnb listings in Warsaw (Poland), combined with district-level official statistics on the hotel industry. The analysis shows that only approximately 11% of offers belong to the sharing economy (“individuals granting each other temporary access to their under-utilised assets”), while at least one third of offers are provided by professional firms. The Airbnb network shows a strong centre-periphery pattern, with 75% of offers located within a range of 4.3 kilometres from the centre. The spatial concentration of Airbnb offers is strongly driven by their distance from metro lines, while it is weakly related to the amount of living space. On the district-level, the spatial distribution of Airbnb listings is correlated with that of the hotel industry, although Airbnb contributes to a more even spread of tourism in the city. The major contribution of this analysis is its presentation of the size and characteristics of the platform, which is essential for data-driven policy making.

scholarly journals Developing a data-driven spatial approach to assessment of neighbourhood influences on the spatial distribution of myocardial infarction

2017 ◽  
Vol 16 (1) ◽  
Author(s):  
Wahida Kihal-Talantikite ◽  
Christiane Weber ◽  
Gaelle Pedrono ◽  
Claire Segala ◽  
Dominique Arveiler ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Spatial Distribution ◽  
Data Driven ◽  
Spatial Approach

scholarly journals A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation

2021 ◽  
Vol 13 (23) ◽  
pp. 13384
Author(s):  
Majid Mirzaei ◽  
Haoxuan Yu ◽  
Adnan Dehghani ◽  
Hadi Galavi ◽  
Vahid Shokri ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Short Term Memory ◽  
Data Driven ◽  
Rainfall Runoff ◽  
Short Term ◽  
Runoff Simulation ◽  
Physically Based ◽  
Long Short Term Memory ◽  
Using Data ◽  
Modelling Approach

Rainfall-Runoff simulation is the backbone of all hydrological and climate change studies. This study proposes a novel stochastic model for daily rainfall-runoff simulation called Stacked Long Short-Term Memory (SLSTM) relying on machine learning technology. The SLSTM model utilizes only the rainfall-runoff data in its modelling approach and the hydrology system is deemed a blackbox. Conversely, the distributed and physically-based hydrological models, e.g., SWAT (Soil and Water Assessment Tool) preserve the physical aspect of hydrological variables and their inter-relations while taking a wide range of data. The two model types provide specific applications that interest modelers, who can apply them according to their project specification and objectives. However, sparse distribution of point-data may hinder physical models’ performance, which may not be the case in data-driven models. This study proposes a specific SLSTM model and investigates the SLSTM and SWAT models’ data dependency in terms of their spatial distribution. The study was conducted in the two distinct river basins of Samarahan and Trusan, Malaysia, with over 20 years of hydro-climate data. The Trusan basin’s rain gauges are scattered downstream of the basin outlet and Samarahan’s are located around the basin, with one station within each basin’s limits. The SWAT was developed and calibrated following its general modelling approach, however, the SLSTM performance was also tested using data preprocessing with principal component analysis (PCA). Results showed that the SWAT performance for daily streamflow simulation at Samarahan has been superior to that of Trusan. Both the SLSTM and PCA-SLSTM models, however, showed better performance at Trusan with PCA-SLSTM outperforming the SLSTM. This demonstrates that the SWAT model is greatly affected by the spatial distribution of its input data, while data-driven models, irrespective of the spatial distribution of their entry data, can perform well if the data adequacy condition is met. However, considering the structural difference between the two models, each has its specific application in a water resources context. The study of catchments’ response to changes in the hydrology cycle requires a physically-based model like SWAT with proper spatial and temporal distribution of its entry data. However, the study of a specific phenomenon without considering the underlying processes can be done using data-driven models like SLSTM, where improper spatial distribution of data cannot be a restricting factor.

scholarly journals A data-driven method for the determination of water-flow velocity in watershed modelling

2018 ◽  
Author(s):  
Qiming Zhou ◽  
Fangli Zhang ◽  
Liang Cheng
Keyword(s):  
Spatial Distribution ◽  
Flow Velocity ◽  
Water Flow ◽  
Hydrological Modeling ◽  
Overland Flow ◽  
Data Driven ◽  
Terrain Analysis ◽  
Hydrological Models ◽  
Water Flow Velocity ◽  
Water Transportation

Physically-based distributed hydrological models have always played an important role in watershed hydrology. Existing hydrological modeling applications focused more on the estimation of water balance and less on the simulation of water transportation in a catchment. Different from the prediction of flow production, the dynamic simulation of flow concentration depends largely on the field distribution of water-flow velocity. However, it is still difficult to determine the water-flow velocity with terrain analysis techniques, which had always hampered the application of hydrological models in surface water transportation simulation. This study, therefore, proposes a data-driven method for creating a field map of overland flow velocity based on the Manning’s equation. Case study on a gauged watershed is undertaken to validate the spatial distribution of flow velocity. The preliminary results indicate that the proposed empirical method can reasonably determine the spatial distribution of water-flow velocity. Further efforts are still required to support the space-time change of flow velocity under the control of microtopography and instantaneous water depth.

scholarly journals A data-driven method for the determination of water-flow velocity in watershed modelling

2018 ◽  
Author(s):  
Qiming Zhou ◽  
Fangli Zhang ◽  
Liang Cheng
Keyword(s):  
Spatial Distribution ◽  
Flow Velocity ◽  
Water Flow ◽  
Hydrological Modeling ◽  
Overland Flow ◽  
Data Driven ◽  
Terrain Analysis ◽  
Hydrological Models ◽  
Water Flow Velocity ◽  
Water Transportation

Physically-based distributed hydrological models have always played an important role in watershed hydrology. Existing hydrological modeling applications focused more on the estimation of water balance and less on the simulation of water transportation in a catchment. Different from the prediction of flow production, the dynamic simulation of flow concentration depends largely on the field distribution of water-flow velocity. However, it is still difficult to determine the water-flow velocity with terrain analysis techniques, which had always hampered the application of hydrological models in surface water transportation simulation. This study, therefore, proposes a data-driven method for creating a field map of overland flow velocity based on the Manning’s equation. Case study on a gauged watershed is undertaken to validate the spatial distribution of flow velocity. The preliminary results indicate that the proposed empirical method can reasonably determine the spatial distribution of water-flow velocity. Further efforts are still required to support the space-time change of flow velocity under the control of microtopography and instantaneous water depth.

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