Machine learning for analysis of wealth in cities: A spatial-empirical examination of wealth in Toronto

Vaz, Eric; Bação, Fernando; Damásio, Bruno; Haynes, Malik; Penfound, Elissa

doi:10.1016/j.habitatint.2021.102319

Cited by 5 publications

(2 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In recent years, it became more and more mature in the field of management. And some scholars successfully applied the SOM method to determine the economic level of the population through socioeconomic characteristics [ 44 , 45 ]. But they only cluster income.…”

Section: Methodsmentioning

confidence: 99%

The healthcare inequality among middle-aged and older adults in China: a comparative analysis between the full samples and the homogeneous population

Fang

Dong

2022

Health Econ Rev

View full text Add to dashboard Cite

Background In the Chinese population, the middle-aged and older adults are the two main segments that utilize a large portion of healthcare. With the fast growth of the two segments, the demands of healthcare services increases significantly. The issue related to inequality in utilization of healthcare emerges with the growth and it deserves more attention. Most existing studies discuss overall inequality. Less attention is paid to inequality among subdivisions, that is, relative inequality. This study focuses on the inequality of healthcare utilization among the homogeneous population and the inequality of the full samples in China. Methods Data were obtained from four waves of the China Health and Retirement Longitudinal Study (CHARLS): 2011, 2013, 2015 and 2018. First, the Concentration Index (CI) was used to measure the inequality of outpatient, inpatient and preventive care for the samples, and regression analysis was applied to decompose the contributing factors of inequality. Then SOM is introduced to identify homogeneous population through clustering and measure the inequality in three types of healthcare utilization among homogeneous population. Based on this, the difference between absolute inequalities and relative inequalities was discussed. Results The preventive care is shown to have the highest degree of inequality inclined to the rich and has the largest increase (CI: 0.048 in 2011 ~ 0.086 in 2018); The inequality degree in outpatient care appears to be the smallest (CI: -0.028 in 2011 ~ 0.014 in 2018). The decomposition results show that age, education, income, chronic disease and self-reported health issues help explain a large portion of inequality in outpatient and inpatient care. And the contribution of socioeconomic factors and education to the inequality of preventive care is the largest. In regards to three types of healthcare among the homogeneous population, the degree of inequality seems to be higher among group with high socioeconomic status than those with lower socioeconomic status. In particular, for the people who are in the high socioeconomic group, the degree of inequality in preventive care is consistently higher than in outpatient and inpatient care. The inequality degree of preventive care in the low socioeconomic status group varies significantly with the flexibility of their response to policies. Conclusions Key policy recommendations include establishing a health examination card and continuously improving the fit of free preventive care with the needs of the middle-aged and older adults; developing CCB activities to avoid people’s excessive utilization in the high socioeconomic status group or insufficient utilization in the low socioeconomic status group; reasonable control of reimbursement and out-of-pocket payments.

show abstract

Section: Methodsmentioning

confidence: 99%

The healthcare inequality among middle-aged and older adults in China: a comparative analysis between the full samples and the homogeneous population

Fang

Dong

2022

Health Econ Rev

View full text Add to dashboard Cite

show abstract

“…The mapping process takes the input space pattern and maps it to the winning neurons, or to other close neurons in the output space. SOM is useful for examining and grouping high dimensional data into clusters, as well as visualizing the analysis through topological maps, and in this study, applying the SOM results to spatial maps [26,27]. For a more detailed description of the SOM algorithm the reader is referred to [28].…”

Section: Methodsmentioning

confidence: 99%

Analysis of Wetland Landcover Change in Great Lakes Urban Areas Using Self-Organizing Maps

Penfound

Vaz

2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Wetland loss and subsequent reduction of wetland ecosystem services in the Great Lakes region has been driven, in part, by changing landcover and increasing urbanization. With landcover change data, digital elevation models (DEM), and self-organizing maps (SOM), this study explores changing landcover and the flood mitigation attributes of wetland areas over a 15-year period in Toronto and Chicago. The results of this analysis show that (1) in the city of Toronto SOM clusters, the landcover change correlations with wetland volume and wetland area range between −0.1 to −0.5, indicating that a more intense landcover change tends to be correlated with small shallow wetlands, (2) in the city of Chicago SOM clusters, the landcover change correlations with wetland area range between −0.1 to −0.7, the landcover change correlations with wetland volume per area range between −0.1 to 0.8, and the landcover change correlations with elevation range between −0.2 to −0.6, indicating that more intense landcover change tends to be correlated with spatially small wetlands that have a relatively high water-storage capacity per area and are located at lower elevations. In both cities, the smallest SOM clusters represent wetland areas where increased landcover change is correlated with wetland areas that have high flood mitigation potential. This study aims to offer a new perspective on changing urban landscapes and urban wetland ecosystem services in Toronto and Chicago.

show abstract

Do papers (really) match journals’ “aims and scope”? A computational assessment of innovation studies

Santos

Mendonça

2022

Scientometrics

View full text Add to dashboard Cite

Machine learning for analysis of wealth in cities: A spatial-empirical examination of wealth in Toronto

Cited by 5 publications

References 41 publications

The healthcare inequality among middle-aged and older adults in China: a comparative analysis between the full samples and the homogeneous population

The healthcare inequality among middle-aged and older adults in China: a comparative analysis between the full samples and the homogeneous population

Analysis of Wetland Landcover Change in Great Lakes Urban Areas Using Self-Organizing Maps

Do papers (really) match journals’ “aims and scope”? A computational assessment of innovation studies

Contact Info

Product

Resources

About