scholarly journals Insights from self-organizing maps for predicting accessibility demand for healthcare infrastructure

2018 ◽  
Author(s):  
Jerome Mayaud ◽  
Sam Anderson ◽  
Martino Tran ◽  
Valentina Radic
Keyword(s):  
Population Growth ◽  
Low Income ◽  
Public Transportation ◽  
Census Data ◽  
Principal Component ◽  
Healthcare Services ◽  
Computational Techniques ◽  
Clustering Methods ◽  
Urban Accessibility ◽  
The City

As urban populations grow worldwide, it becomes increasingly important to critically analyse accessibility – the ease with which residents can reach key places or opportunities. The combination of ‘big data’ and advances in computational techniques such as machine learning (ML) could be a boon for urban accessibility studies, yet their application remains limited in this field. In this study, we aim to more robustly relate socio-economic factors to healthcare accessibility across a city experiencing rapid population growth, using a novel combination of clustering methods. We applied a powerful ML clustering tool, the self-organising map (SOM), in conjunction with principal component analysis (PCA), to examine how income shifts over time (2016–2022) could affect accessibility equity to healthcare for senior populations (65+ years) in the City of Surrey, Canada. We characterised accessibility levels to hospitals and walk-in clinics using door-to-door travel times, and combined this with high-resolution census data. Higher income clusters are projected to become more prevalent across the city over the study period, in some cases incurring into previously low income areas. However, low income clusters have on average much better accessibility to healthcare facilities than high income clusters, and their accessibility levels are projected to increase between 2016 and 2022. By attributing temporal differences through cross-term analysis, we show that population growth will be the biggest accessibility challenge in neighbourhoods with existing access to healthcare, whereas income change (both positive and negative) will be most challenging in poorly connected neighbourhoods. A dual accessibility problem may therefore arise in Surrey. First, large senior populations will reside in areas with access to numerous, and close-by, clinics, putting pressure on existing facilities for specialised services. Second, lower-income seniors will increasingly reside in areas poorly connected to healthcare services; since these populations are likely to be highly reliant on public transportation, accessibility equity may suffer. To our knowledge, this study is the first to apply a combination of PCA and SOM techniques in the context of urban accessibility, and it demonstrates the value of this clustering approach for drawing planning policy recommendations from large multivariate datasets.

scholarly journals Insights from Self-Organizing Maps for Predicting Accessibility Demand for Healthcare Infrastructure

Urban Science ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 33 ◽  
Author(s):  
Jerome Mayaud ◽  
Sam Anderson ◽  
Martino Tran ◽  
Valentina Radić
Keyword(s):  
Low Income ◽  
Principal Component ◽  
Healthcare Services ◽  
Computational Techniques ◽  
Self Organizing Maps ◽  
Healthcare Facilities ◽  
Urban Accessibility ◽  
Income Data ◽  
Som Clustering ◽  
Self Organising Map

As urban populations grow worldwide, it becomes increasingly important to critically analyse accessibility—the ease with which residents can reach key places or opportunities. The combination of ‘big data’ and advances in computational techniques such as machine learning (ML) could be a boon for urban accessibility studies, yet their application in this field remains limited. In this study, we provided detailed predictions of healthcare accessibility across a rapidly growing city and related them to socio-economic factors using a combination of classical and modern data analysis methods. Using the City of Surrey (Canada) as a case study, we clustered high-resolution income data for 2016 and 2022 using principal component analysis (PCA) and a powerful ML clustering tool, the self-organising map (SOM). We then combined this with door-to-door travel times to hospitals and clinics, calculated using a simple open-source tool. Focusing our analysis on senior populations (65+ years), we found that higher income clusters are projected to become more prevalent across Surrey over our study period. Low income clusters have on average better accessibility to healthcare facilities than high income clusters in both 2016 and 2022. Population growth will be the biggest accessibility challenge in neighbourhoods with good existing access to healthcare, whereas income change (both positive and negative) will be most challenging in poorly connected neighbourhoods. A dual accessibility problem may arise in Surrey: first, large senior populations will reside in areas with access to numerous and close-by, clinics, putting pressure on existing facilities for specialised services. Second, lower-income seniors will increasingly reside in areas poorly connected to healthcare services, which may impact accessibility equity. We demonstrate that combining PCA and SOM clustering techniques results in novel insights for predicting accessibility at the neighbourhood level. This allows for robust planning policy recommendations to be drawn from large multivariate datasets.

scholarly journals Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques

2021 ◽  
Author(s):  
Kamil Faisal ◽  
Ahmed Shaker
Keyword(s):  
Environmental Quality ◽  
Geographically Weighted Regression ◽  
Family Income ◽  
Census Data ◽  
Principal Component ◽  
Urban Morphology ◽  
Weighted Regression ◽  
Spatial Lag Model ◽  
Economic Parameters ◽  
The City

Urban Environmental Quality (UEQ) can be treated as a generic indicator that objectively represents the physical and socio-economic condition of the urban and built environment. The value of UEQ illustrates a sense of satisfaction to its population through assessing different environmental, urban and socio-economic parameters. This paper elucidates the use of the Geographic Information System (GIS), Principal Component Analysis (PCA) and Geographically-Weighted Regression (GWR) techniques to integrate various parameters and estimate the UEQ of two major cities in Ontario, Canada. Remote sensing, GIS and census data were first obtained to derive various environmental, urban and socio-economic parameters. The aforementioned techniques were used to integrate all of these environmental, urban and socio-economic parameters. Three key indicators, including family income, higher level of education and land value, were used as a reference to validate the outcomes derived from the integration techniques. The results were evaluated by assessing the relationship between the extracted UEQ results and the reference layers. Initial findings showed that the GWR with the spatial lag model represents an improved precision and accuracy by up to 20% with respect to those derived by using GIS overlay and PCA techniques for the City of Toronto and the City of Ottawa. The findings of the research can help the authorities and decision makers to understand the empirical relationships among environmental factors, urban morphology and real estate and decide for more environmental justice.

scholarly journals Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques

2021 ◽  
Author(s):  
Kamil Faisal ◽  
Ahmed Shaker
Keyword(s):  
Environmental Quality ◽  
Geographically Weighted Regression ◽  
Family Income ◽  
Census Data ◽  
Principal Component ◽  
Urban Morphology ◽  
Weighted Regression ◽  
Spatial Lag Model ◽  
Economic Parameters ◽  
The City

Urban Environmental Quality (UEQ) can be treated as a generic indicator that objectively represents the physical and socio-economic condition of the urban and built environment. The value of UEQ illustrates a sense of satisfaction to its population through assessing different environmental, urban and socio-economic parameters. This paper elucidates the use of the Geographic Information System (GIS), Principal Component Analysis (PCA) and Geographically-Weighted Regression (GWR) techniques to integrate various parameters and estimate the UEQ of two major cities in Ontario, Canada. Remote sensing, GIS and census data were first obtained to derive various environmental, urban and socio-economic parameters. The aforementioned techniques were used to integrate all of these environmental, urban and socio-economic parameters. Three key indicators, including family income, higher level of education and land value, were used as a reference to validate the outcomes derived from the integration techniques. The results were evaluated by assessing the relationship between the extracted UEQ results and the reference layers. Initial findings showed that the GWR with the spatial lag model represents an improved precision and accuracy by up to 20% with respect to those derived by using GIS overlay and PCA techniques for the City of Toronto and the City of Ottawa. The findings of the research can help the authorities and decision makers to understand the empirical relationships among environmental factors, urban morphology and real estate and decide for more environmental justice.

scholarly journals Changes in Spatial Inequality and Residential Segregation in Metropolitan Lima

2021 ◽  
pp. 471-490
Author(s):  
Graciela Fernández-de-Córdova ◽  
Paola Moschella ◽  
Ana María Fernández-Maldonado
Keyword(s):  
Urban Space ◽  
Residential Segregation ◽  
Low Income ◽  
Census Data ◽  
Diversity Index ◽  
Transitional Zone ◽  
Spatial Inequality ◽  
Dissimilarity Index ◽  
Occupational Groups ◽  
The City

AbstractSince the 2000s, Lima city shows important changes in its socio-spatial structure, decreasing the long-established opposition between the centre and the periphery, developing a more complex arrangement. Sustained national economic growth has allowed better socio-economic conditions in different areas of the city. However, high inequality still remains in the ways of production of urban space, which affects residential segregation. To identify possible changes in the segregation patterns of Metropolitan Lima, this study focuses on the spatial patterns of occupational groups, examining their causes and relation with income inequality. The analysis is based on the 1993 and 2007 census data, measuring residential segregation by the Dissimilarity Index, comparing with the Diversity Index. The results confirm trends towards increased segregation between occupational groups. Top occupational groups are concentrated in central areas, expanding into adjacent districts. Bottom occupational groups are over-represented in distant neighbourhoods. In-between, a new, more mixed, transitional zone has emerged in upgraded formerly low-income neighbourhoods. Areas of lower occupational diversity coincide with extreme income values, forming spaces of greater segregation. In the metropolitan centre–periphery pattern, the centre has expanded, while the periphery has been shifted to outer peripheral rings.

scholarly journals Urban Change Detection and Population Prediction Modeling Using Multitemporal Landsat TM Images

2021 ◽  
Author(s):  
Hongmei Zhao
Keyword(s):  
Land Cover ◽  
Population Growth ◽  
City Planning ◽  
Census Data ◽  
Urban Expansion ◽  
Population Census ◽  
Prediction Modeling ◽  
Average Growth ◽  
The City ◽  
Population Prediction

Urban environments belong to the most dynamic system on the earth's surface. Urban areas contain nearly half of the world's population. Understanding the growth and change brought on by urbanization is critical for urban planning, environmental studies, and resource management. This study is an attempt to present a satellite-based approach to modelling urban population growth from multitemporal and multispectral Landsat image data. The focus is placed on two aspects: detection of urban land cover changes and population prediction modeling associated with the urban expansion. The study consists of an experimental set-up to generate the land cover maps and to recognize the vegetation-impervious surface-soil (V-I-S) patterns followed by integrating population census data and remote sensing data at the city planning district level. This is done in conjunction with geographic information systems (GIS) in order to model population growth from 1996 to 2001 in the City of Mississauga, Ontario. The main findings of this research show that a total of 81.6 km² of built-up areas have been added with Mississauga's boundaries between 1985 and 2002. This accounts for 25.5% of the total area of Mississauga at the expense of non-built and water covered areas. The results show an increase of 6.5% in built-up areas in the last three years (1999-2002), which results in an average growth rate of 7 km²/year. The previous 14 years (1985-1999) have shown an increase of 19.0% in development, which equals 4.3 km²/year. The investigation also shows that a linear equation adequately describes the relationship between the population counts and the built-up area, or "C-442" area, of V-I-S components.

Analyzing the relationship between major public investments and neighborhood change in Atlanta

2021 ◽  
Author(s):  
Josh Humphries ◽  
Kendra Taylor
Keyword(s):  
Population Growth ◽  
Low Income ◽  
Public Investment ◽  
Neighborhood Change ◽  
Public Investments ◽  
The Relationship ◽  
The City

As many of our residents can attest to personally, Atlanta’s population growth, from about 420,000 residents in 2010 to over 500,000 residents today, has been accompanied by demographic neighborhood change. In the Neighborhood Change Report released by the City in February 2021, we explore how major public investment and design goals are related to changes in where low-income and non-low-income residents live in the city.

scholarly journals Precarious Employment and Difficult Daily Commutes

2017 ◽  
Vol 72 (1) ◽  
pp. 77-98 ◽  
Author(s):  
Stéphanie Premji
Keyword(s):  
Low Income ◽  
Public Transportation ◽  
Precarious Employment ◽  
Geographical Mobility ◽  
Employment Conditions ◽  
And Migration ◽  
The Relationship ◽  
The City ◽  
Market Strategies ◽  
Immigrant Men

Precarious employment is on the rise in Canada, increasing by nearly 50% in the last two decades. However, little is known about the mechanisms by which it can impact upon geographical mobility. Employment-related geographical mobility refers to mobility to, from and between workplaces, as well as mobility as part of work. We report on a qualitative study conducted among 27 immigrant men and women in Toronto that investigates the relationship between precarious employment and daily commutes while exploring the ways in which gender, class and migration structure this relationship. Interview data reveal that participants were largely unable to work where they lived or live where they worked. Their precarious jobs were characterized by conditions that resulted in long, complex, unfamiliar, unsafe and expensive commutes. These commuting difficulties, in turn, resulted in participants having to refuse or quit jobs, including desirable jobs, or being unable to engage in labour market strategies that could improve their employment conditions (e.g. taking courses, volunteering, etc.). Participants’ commuting difficulties were amplified by the delays, infrequency, unavailability and high cost of public transportation. These dynamics disproportionately and/or differentially impacted certain groups of workers. Precarious work has led to workers having to absorb an ever-growing share of the costs associated with their employment, underscored in our study as time, effort and money spent travelling to and from work. We discuss the forces that underlie the spatial patterning of work and workers in Toronto, namely the growing income gap and the increased polarization among neighbourhoods that has resulted in low-income immigrants increasingly moving from the centre to the edges of the city. We propose policy recommendations for public transportation, employment, housing and child care that can help alleviate some of the difficulties described.

scholarly journals Future access to essential services in a growing smart city: The case of Surrey, British Columbia

2018 ◽  
Author(s):  
Jerome Mayaud ◽  
Martino Tran ◽  
Rafael Henrique Moreas Pereira ◽  
Rohan Nuttall
Keyword(s):  
Rural Communities ◽  
Low Income ◽  
Public Transport ◽  
Census Data ◽  
Population Change ◽  
Investment Strategies ◽  
Healthcare Facilities ◽  
Depth Analysis ◽  
Essential Services ◽  
The City

The concept of accessibility – the ease with which people can reach places or opportunities –lies at the heart of what makes cities livable, workable and sustainable. As urban populations shift over time, predicting the changes to accessibility demand for certain services becomes crucial for responsible and ‘smart’ urban planning and infrastructure investment. In this study, we investigate how projected population change could affect accessibility to essential services in the City of Surrey, one of the fastest growing cities in Canada. Our objectives are two-fold: first, to quantify the additional pressure that Surrey’s growing population will have on existing facilities; second, to investigate how changes in the spatial distribution of different age and income groups will impact accessibility equity across the city. We evaluated accessibility levels to healthcare facilities and schools across Surrey’s multimodal transport network using origin-destination matrices, and combined this information with high-resolution longitudinal census data. Paying close attention to two vulnerable population groups – children and youth (0–19 years of age) and seniors (65+ years of age) – we analyzed shifts in accessibility demand from 2016 to 2022. The results show that population growth both within and outside the catchments of existing facilities will have varying implications for future accessibility demand in different areas of the city. By 2022, the city’s hospitals and walk-in clinics will be accessible to ~9,000 and ~124,000 more people (respectively) within a predefined threshold of 30 minutes by public transport. Schools will also face increased demand, as ~8,000 additional children/youth in 2022 will move to areas with access to at least half of the city’s schools. Conversely, over 27,000 more people – almost half of them seniors – will not be able to access a hospital in under 30 minutes by 2022. Since low-income and senior residents moving into poorly connected areas tend to be more reliant on public transport, accessibility equity may decline in some rural communities. Our study highlights how open-source data and code can be leveraged to conduct in-depth analysis of accessibility demand across a city, which is key for ensuring inclusive and ‘smart’ urban investment strategies.

Analyzing the relationship between major public investments and neighborhood change in Atlanta

2021 ◽  
Author(s):  
Josh Humphries ◽  
Kendra Taylor
Keyword(s):  
Population Growth ◽  
Low Income ◽  
Public Investment ◽  
Neighborhood Change ◽  
Public Investments ◽  
The Relationship ◽  
The City

As many of our residents can attest to personally, Atlanta’s population growth, from about 420,000 residents in 2010 to over 500,000 residents today, has been accompanied by demographic neighborhood change. In the Neighborhood Change Report released by the City in February 2021, we explore how major public investment and design goals are related to changes in where low-income and non-low-income residents live in the city.

scholarly journals KAWASAN BANTARAN SUNGAI CIKAPUNDUNG SEBAGAI PERMUKIMAN MASYARAKAT BERPENGHASILAN RENDAH (MBR) DI KOTA BANDUNG

2017 ◽  
Vol 1 (2) ◽  
pp. 57
Author(s):  
Karto Wijaya ◽  
Asep Yudi Permana ◽  
Noor Swanto
Keyword(s):  
Growth Rate ◽  
Population Density ◽  
Population Growth ◽  
Low Income ◽  
Average Growth Rate ◽  
Average Growth ◽  
Local Residents ◽  
Housing Needs ◽  
The City

Abstract: The city of Bandung has always been a tourist attraction with various activities every year. Bandung population growth rate in the last 5 years reached 0.89% per year and in the expansion area reached 6.79% per year. With an area of only about 17,000 ha, Bandung is now inhabited by ± 2.481.901 inhabitants. The rate of population growth above the average growth rate of the population of West Java province. No wonder the average population density is 145 people / ha. Though ideally the population density of Bandung is 50-60 people / Ha. There are 657 districts and 57,687 homes that experience environmental degradation and 67 areas identified as urban slums. The implication of the high urbanization of Bandung City in Metropolitan scale to the scale of the region emerged the problem of integration of settlements with surrounding functions. The problem of settlement of Bandung City also includes segmentation of residential objects such as Low Income Community (MBR), non MBR, immigrants, local residents, students and workers of various Sectors. Thus the problems of the settlement of Bandung City include low level of fulfillment of adequate housing needs, limited access of Low Income Community to housing resources, unfinished system of financing and housing market, decreasing the quality of housing and settlement environment and not yet integrated development of area Housing and settlements with the construction of housing and settlement infrastructure, facilities and utilities. This research method to find out how far the level of slum settlement contained in Cihampelas Bandung Settlement and recommendations that can be done for the improvement of the settlement of the kampong. Keyword:Urbanization, Integration, Human settlement, Metropolitan Abstrak: Kota Bandung selalu menjadi daya tarik pendatang dengan berbagai aktivitas setiap tahunnya. Laju pertumbuhan penduduk Kota Bandung dalam 5 tahun terakhir mencapai 0,89% per tahun dan di wilayah perluasan mencapai 6,79% per tahun. Dengan luas wilayah hanya sekitar 17.000 Ha, Bandung kini dihuni oleh ± 2.481.901 jiwa. Laju pertambahan penduduknya diatas laju pertumbuhan rata-rata penduduk provinsi Jawa Barat. Tidak heran jika tingkat kepadatan penduduk rata-rata 145 jiwa/Ha. Padahal idealnya tingkat kepadatan penduduk Kota Bandung adalah 50-60 jiwa/Ha. Terdapat 657 kawasan dan 57.687 rumah yang mengalami penurunan kualitas lingkungan dan 67 kawasan diidentifikasi sebagai kawasan kumuh perkotaan. Impilikasi dari tingginya urbanisasi Kota Bandung dalam skala Metropolitan hingga skala kawasan muncul masalah integrasi permukiman dengan fungsi sekitarnya. Permasalahan permukiman Kota Bandung juga meliputi segmentasi objek hunian seperti masyarakat berpenghasilan rendah (MBR), non MBR, pendatang, penduduk lokal, mahasiswa dan pekerja berbagai sektor. Dengan demikian masalah-masalah yang permukiman Kota Bandung meliputi rendahnya tingkat pemenuhan kebutuhan perumahan yang layak, terbatasnya akses Masyarakat Berpenghasilan Rendah (MBR) terhadap sumber daya perumahan, belum mantapnya sistem pembiayaan dan pasar perumahan, menurunnya kualitas lingkungan perumahan dan permukiman dan belum terintegrasinya pengembangan kawasan perumahan dan permukiman dengan pembangunan prasarana, sarana, dan utilitas perumahan dan permukiman. Metode penelitian ini untuk mengetahui sejauh mana tingkat kekumuhan pemukiman yang terdapat di Permukiman Cihampelas Bandung dan rekomendasi yang dapat dilakukan demi perbaikan pemukiman kampung tersebut. Kata kunci: Urbanisasi, Integrasi, Pemukiman, Metropolitan

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