Studying the electronic word-of-mouth (eWOM) in the foodservice industry can not only provide guidance for merchants, but also spatially optimize the urban foodservice industry, restaurants’ location selection, and customers’ purchasing decisions. In this study, taking Sanya city as the research object, using big data crawling technology to collect the directory and their attribute information of 2107 restaurants with more than 100 reviews. Kernel density analysis, grid analysis and the geographically weighted regression (GWR) model were applied to reveal the distribution characteristics and influencing factors of eWOM in the foodservice industry in Sanya, China. The main results are as follows. The foodservice industry in Sanya extends along the southern coastline and is characterized by little dispersion and agglomeration at the macro level. The overall eWOM score of the foodservice industry is low. Market popularity, restaurant rating, transportation conditions, and commercial development all have a positive impact on the eWOM of the foodservice industry. Population and price have both positive and negative effects and the public services has a nonsignificant impact on the eWOM. This study not only improves the theoretical understanding of the foodservice industry, but also provides a general reference for its development in other industries and cities.

MaxEnt Supplementary Info

The rise of e-commerce has imposed increasing pressures on urban freight distribution systems with a significant demand for dedicated delivery services to the end consumers. Last-mile delivery, which usually happens in residential areas conducted by small vans or trucks with low speeds, raises concerns for environmental and safety issues. One of the strategies to address these problems is to set up Pick-up Point (PPs) networks or Automated Parcel (APs) systems. This research will focus on the spatial dynamics and the associated potential GHG emission reductions of Amazon Lockers, one of the most popular APs, in Los Angeles County. The location data of Amazon Lockers will be obtained by Google Map API and Python. Specifically, the questions to be answered include: (1) Describing the spatial distribution of lockers using spatial pattern analysis tool (Kernel density and Moran's I statistics); (2) Analyzing the socio-economic and built environmental factors that might affect the spatial distribution of Amazon Lockers using spatial regression models (Geographically Weight Regression); and (3) Predict and estimate the potential GHG emission reduction based on the spatial regression models. The results indicate that (1) There is a "three-tier-clustering" pattern based on the level of density; (2) There is a significant positive spatial autocorrelation at 99% confidence level; (3) Geographic Weighted Regression with independent variables population/internet use, income, education, walkability, transit and parking can explain 41% of the variations in dependent variables; (4) Business cooperation and spillover effects also greatly affect the locker distribution.

Abstract: The Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), 1998-2019, V4.GL.03 consists of annual concentrations (micrograms per cubic meter) of all composition ground-level fine particulate matter (PM2.5). This data set combines AOD retrievals from multiple satellite algorithms including the NASA MODerate resolution Imaging Spectroradiometer Collection 6.1 (MODIS C6.1), Multi-angle Imaging SpectroRadiometer Version 23 (MISRv23), MODIS Multi-Angle Implementation of Atmospheric Correction Collection 6 (MAIAC C6), and the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) Deep Blue Version 4. The GEOS-Chem chemical transport model is used to relate this total column measure of aerosol to near-surface PM2.5 concentration. Geographically Weighted Regression (GWR) is used with global ground-based measurements from the World Health Organization (WHO) database to predict and adjust for the residual PM2.5 bias per grid cell in the initial satellite-derived values. These estimates are primarily intended to aid in large-scale studies. Gridded data sets are provided at a resolution of 0.01 degrees to allow users to agglomerate data as best meets their particular needs. Data sets are gridded at the finest resolution of the information sources that were incorporated, but do not fully resolve PM2.5 gradients at the gridded resolution due to influence by information sources at coarser resolution. The data are distributed as GeoTIFF files and are in WGS84 projection. Purpose: To provide an annual global surface of concentrations (micrograms per cubic meter) of all composition ground-level fine particulate matter of 2.5 micrometers or smaller (PM2.5) for large-scale health and environmental research. Legend:

  Color
  Legend Label
  Description: 



  80 - 139
  Annual concentrations (micrograms per cubic meter) of all composition ground-level fine particulate matter (PM2.5)



  40 - 80



  20 - 40



  10 - 20



  5 - 10



  0 - 5

 Citation: Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), 1998-2019, V4.GL.03. Hammer, M. S., A. van Donkelaar, C. Li, A. Lyapustin, A. M. Sayer, N. C. Hsu, R. C. Levy, M. J. Garay, O. V. Kalashnikova, R. A. Kahn, M. Brauer, J. S. Apte, D. K. Henze, L. Zhang, Q. Zhang, B. Ford, J. R. Pierce, and R. V. Martin. (2022). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/fx80-4n39 For inquiries about this service, please contact ciesin.info@ciesin.columbia.edu. Publication References:Documentation for the Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD), 1998-2019, V4.GL.03. Hammer, M. S., A. van Donkelaar, C. Li, A. Lyapustin, A. M. Sayer, N. C. Hsu, R. C. Levy, M. J. Garay, O. V. Kalashnikova, R. A. Kahn, M. Brauer, J. S. Apte, D. K. Henze, L. Zhang, Q. Zhang, B. Ford, J. R. Pierce, and R. V. Martin. (2022). NASA Socioeconomic Data and Applications Center (SEDAC). DOI: https://doi.org/10.7927/thr9-xe64

Studying the electronic word-of-mouth (eWOM) in the foodservice industry can not only provide guidance for merchants, but also spatially optimize the urban foodservice industry, restaurants’ location selection, and customers’ purchasing decisions. In this study, taking Sanya city as the research object, using big data crawling technology to collect the directory and their attribute information of 2107 restaurants with more than 100 reviews. Kernel density analysis, grid analysis and the geographically weighted regression (GWR) model were applied to reveal the distribution characteristics and influencing factors of eWOM in the foodservice industry in Sanya, China. The main results are as follows. The foodservice industry in Sanya extends along the southern coastline and is characterized by little dispersion and agglomeration at the macro level. The overall eWOM score of the foodservice industry is low. Market popularity, restaurant rating, transportation conditions, and commercial development all have a positive impact on the eWOM of the foodservice industry. Population and price have both positive and negative effects and the public services has a nonsignificant impact on the eWOM. This study not only improves the theoretical understanding of the foodservice industry, but also provides a general reference for its development in other industries and cities.

Abstract copyright UK Data Service and data collection copyright owner.

Standard indexes of poverty and deprivation are rarely sensitive to how the causes and consequences of deprivation have different impacts depending upon where a person lives. More geographically minded approaches are alert to spatial variations but are also difficult to compute using desktop PCs.

The aim of the ESRC sponsored project was to develop a method of spatial analysis known as ‘geographically weighted regression’ (GWR) to run in the high power computing environment offered by ‘Grid computation’ and e-social science. GWR, like many other methods of spatial analysis, is characterised by multiple repeat testing as the data are divided into geographical regions and also randomly redistributed many times to simulate the likelihood that the results obtained from the analysis are actually due to chance. Each of these tests requires computer time so, given a large dataset such as the UK Census statistics, running the analysis on a standard machine can take a long time! Fortunately, the computational grid is not standard but offers the possibility to speed up the process by running GWR’s sequences of calibration, analysis and non-parametric simulation in parallel.

An output is a model of the geographically varying correlates of car non-ownership fitted for the 165,665 Census Output Areas in England. Specifically, a geographically weighted regression of the relationship between the proportion of households without a car (or van) in 2001 (the dependent variable), and the following predictor variables: proportion of persons of working age unemployed; proportion of households in public housing; proportion of households that are lone parent households; proportion of persons 16 or above that are single; and proportion of persons that are white British.

Note - the file does not contain Census 2001 data, only National Grid references and regression coefficients.

Further information is available from the Grid Enabled Spatial Regression Models (With Application to Deprivation Indices) web page.


Main Topics:

Investigating the spatially varying correlates of car non-ownership using GWR.

Affordable housing plays a significant role for the wellbeing of people all over the world. However, against the background of housing commodification and market reforms since 1978 in China, housingprice in many cities especially mega cities such as Beijing, Shanghai, Shenzhen and Guanghzou in China hasundergone rapidly increasing. The fact negatively affects housing accessibility of many residents and leadsto socio-spatial polarization of many cities. Driven by this concern, this research explores the spatial distribution pattern of housing prices and the influencing factors of Shenyang, a typical old industrial city in China.Based on POI data and the Kriging method, we firstly simulated the spatial distribution pattern of housingprices in Shenyang. Then, 11 independent variables were selected (consisting of community characteristics,public facilities and public transportations) to investigate mechanisms underlying the spatial differential pattern of housing prices of Shenyang, based on the Geographically Weighted Regression model (GWR). Theresults are as following. First, the housing price of different communities in Shenyang spatially forms amulti-center structure. Changbai region has replaced Shenhe and Heping districts as the new peak price area.Second, the independent variables show significant spatial heterogeneity. Variables related to communitycharacteristics, such as ratio of green space, parking lot ratio and neighbourhoods management fees, have significant positive effects on housing price in general. Third, we found that urban housing market developmentof old industrial cities such as Shenyang has long been featured by the "strong government, weak market" development strategies.

Description: This dataset consists of field data (arthropods, nematodes and NDVI) collected over the course of 6 field excursions in 2015 and 2016 near TyTy, GA, in a field used for growing Miscanthus x giganteus. It also includes interpolated values of soil measurements collected in 2015 and meteorological data collected on an adjacent farm. Point-in-time measurements include all meteorological, NDVI, arthropod and nematode measurements and their derivatives. Fixed values were measurements that were held constant across all sampling dates, including location, terrain and soils measurements and their derivatives. Dawn Olson and Jason Schmidt collected and processed arthropod count data. Jason Schmidt collected and processed spider count data and computed spider diversity. Richard Davis collected and processed nematode count data. Alisa Coffin collected and processed NDVI data and positional locations. Tim Strickland collected and processed soils data and Alisa Coffin interpolated soils values using kriging to derive values at arthropod sample locations. David Bosch collected and processed meteorological data. Lynne Seymour provided statistical expertise in deriving any estimated values (phloem feeders, parasitoids, spiders, and natural enemies). Alisa Coffin derived terrain data (elevation, slope, aspect, and distances) from publicly available datasets, transformed values (SI, WI, etc), carried out the geographically weighted regression analysis and calculated C:SE values, harmonized the full dataset, and compiled it using Esri's ArcGIS Pro 2.5. Methods for most data are published in the accompanying paper and associated supplements. Questions about dataset development and management should be directed to Alisa Coffin (alisa.coffin@usda.gov). This work was accomplished as a joint USDA and University of Georgia project funded by a cooperative agreement (#6048-13000-026-21S). This research was a contribution from the Long-Term Agroecosystem Research (LTAR) network. LTAR is supported by the United States Department of Agriculture. At request of the author, the data resources are under embargo. The embargo will expire on Fri, Jan 01, 2021. Resources in this dataset:Resource Title: Spreadsheet of data. File Name: GibbsMisFarm_Arthrop_Env_DepVar_201516_final.xlsxResource Description: This workbook contains all of the data used in this analysis. The first worksheet contains data dictionary information.Resource Software Recommended: Microsoft Excel, Office 365,url: https://www.microsoft.com/en-us/microsoft-365/excel Resource Title: GeoJSON. File Name: MiscanthusXGiganteusGeoJSON.json

Anthropogenic climate change has increased the frequency and intensity of fires. Despite their widespread consequences, current research has largely overlooked urban fires and their associated vulnerability. This study seeks to identify patterns of fire vulnerability, map out areas with high fire vulnerability and limited access to fire stations and hospitals, and ultimately determine the factors contributing to increased fire incidents. Principal Component Analysis was used to develop a fire vulnerability index comprising variables capturing health status and socio-environmental factors. Enhanced 2-step floating catchment area (E2SFCA) analysis was conducted to determine relative accessibility to resources such as hospitals and fire stations. Ordinary least squares (OLS) regression and geographically weighted regression (GWR) were utilized to determine factors associated with higher fire incident counts. The results of the fire vulnerability analysis highlight areas of high fire vulnerability in the eastern periphery and the north-central parts of Austin. Moreover, the eastern periphery experiences decreased accessibility to fire stations and hospitals. Finally, the results of the GWR analysis highlight a varied negative relationship between health vulnerability and fire incidents and a positive relationship with socio-environmental vulnerability. The GWR model (R2: 0.332) was able to predict a greater extent of the variance compared to OLS (R2: 0.056). Results of this study underscore that areas with socio-environmental vulnerabilities are likely to face a higher number of fire incidents and have reduced access to hospitals and fire stations. These findings can inform public health officials, city planners, and emergency services departments in developing targeted strategies to mitigate the harm caused by fire incidents.

Ticks and tick-borne diseases (TBDs) form a significant and growing threat to human health and well-being in Europe, with increasing numbers of tick-borne encephalitis (TBE) and Lyme borreliosis cases being reported during the past few decades. Increasing knowledge of tick risk areas and seasonal activity remains the primary method for preventing TBDs. Crowdsourcing provides the best alternative for rapidly obtaining data on tick occurrence on a national level.

In order to produce and share up-to-date data about tick risk areas in Finland, an online platform, Punkkilive (www.punkkilive.fi/en), was launched in April 2021. On the website, users can submit and browse tick observations, report tick numbers and hosts, and upload pictures of ticks.

Here, we looked at trends in the crowdsourced data from 2021, assessed the effect of local tick species on seasonality of observations, and examined sampling bias in the data.

The high number of tick observations (n=78 837) highlights that there was demand for such a service. Approximately 97% of 5573 uploaded pictures represented ticks. Seasonal patterns of tick observations varied across Finland, highlighting variability in the risk associated with the two human-biting tick species Ixodes ricinus and I. persulcatus, the latter having a shorter, unimodal activity peak in late spring–early summer. Tick numbers were low and the proportion of new sightings was high in northern Finland, as may be expected near the latitudinal distribution limits of both species. While the number of inhabitants generally explained the number of tick observations well, geographically weighted regression models also identified areas that deviated from this general pattern.

This study offers a prime example of how crowdsourcing can be applied to track vectors of zoonotic diseases, to the benefit of both researchers and the public. Areas with more or fewer observations than predicted based on number of inhabitants were revealed, wherein more specific analyses may reveal factors contributing to lower or higher risk levels that may be used in increasing awareness. We hope that the success of Punkkilive serves to highlight the usefulness of citizen science in the prevention of vector-borne diseases.

With the implementation of sustainable development objectives in developing countries, urban planning, land use regulation, and urban mobility policies are expected to help reduce inequalities in access to urban facilities. Urban transport policies are also expected to encourage travel by non-motorised modes and public transport. These are considered to be the sustainable modes of urban transport. In this paper, we investigate how inequality of urban facilities impacts trips made by sustainable modes in the city of Santiago de Chile. We use a Poisson regression model and its geographical extension, the geographically weighted Poisson regression model (GWPR). The results suggest that the inequality of urban facilities impacts trips made by sustainable modes. The variables with the highest relevance are the spatial distribution of mixed land use, the spatial distribution of urban services related to transport infrastructure, primary and secondary education, as well as the spatial distribution of demographic variables related to people’s life cycle.

The COVID-19 pandemic extensively impacted maternal, neonatal, and child health (MNCH) in Bangladesh. Misconceptions arising from a lack of knowledge related to the virus contributed to reduced uptake of MNCH services, which eventually helped increase maternal and neonatal mortality rates during the pandemic. In this study, we assessed the knowledge and practices related to COVID-19 prevention among the mothers of under-2 children in Bangladesh. The study was conducted in May 2021 as part of a broader research project related to COVID-19 response on MNCH service utilization. We collected data from 2207 mothers in six districts of Bangladesh using a multi-stage cluster sampling technique. We constructed weighted and unweighted composite knowledge and practice scores and identified different socio-demographic characteristics associated with the scores using multilevel generalized mixed-effect linear regression models. In general, the mothers revealed poor knowledge and practices related to COVID-19. On a weighted scale of 100, the mean composite knowledge and practice scores were 32.6 (SD = 16.4) and 53.1 (SD = 13.9), respectively. The mothers presented inadequate knowledge about COVID-19 transmission, symptoms, and the recommended preventive measures. At the same time, maintaining a safe physical distance was the least practiced preventative measure (10.3%). Level of education, access to television, and the internet were significantly positively associated with their knowledge and practices related to COVID-19. Knowledge score was also positively associated with the practice score (OR = 1.26; p-value