How People’s Travel Destinations Change with Pandemic Phases / Geospatial Research Based on R Postgraduate Academic Work: Planning Technologies (Team Project) Instructors: Filip Biljecki 03.2021 - 05.2021
1 INTRODUCTION 1.1 BACKGROUND
1.1.1 Rail Ridership: Correlated to Urban Development Rail network is the backbone of the public transport system in Singapore. The first MRT section was opened in November 1987 (Xuan Zhu et al., 2004). According to LTA (2013), the rail network will be expanded to about 360 km by 2030 from 200 km in 2020. This means connecting eight in 10 households to within 10 minutes of a train station. Ridership, or passenger volume, is one of the most commonly used measures to capture the effect of the surrounding land use, clustered development, diversity, density, transit supply, system efficiency on transit use (Chakraborty & Mishra, 2013). Higher ridership also reveals higher popularity and better economic benefits.
2.2 LAND USE CLASSIFICATION Station locations follow urban planning principles. Land use of the station surrounding area can illustrate the urban functions it serves, its user group and people’s travel behaviors. Therefore, we try to use the proportion of different types of land sue to classify stations into several typologies, and to explore how people’s travel destinations change with pandemic phases. We create a 700 meters buffer zone for each station since its a suitable walking distance within 10 minutes. Further, we intersect Singapore 2019 land use map and stations wtih 700-meter buffer and figure out the proportion of different types of land use surrounding each station.
The difference in land use allocation has an effect on the popularity of rail stations. As Xuan Zhu et al. (2004) mentioned, the existing urban land use configuration helps to shape travel patterns. In Singapore, the urban center hierarchy and the new town development concept have led to the difference in land use characteristics of the TOD stations (Shaofei Niu et al., 2019). Many studies did the correlation analysis of ridership data and land use data. Sidek et al. (2017) summarized related papers and concluded that high public transit ridership is related to high land use density. In addition, a diversity of land uses among three major categories within the walking radius of the station can also accumulate transit passenger volume. Kim et al. (2017) used transit smart card data to identify the travel pattern and reveal the relationship between travel patterns and the surrounding environment in Seoul. Through quantifying linear functions consisting of ridership and GFA, Berawi et al. (2020) found that office development can generate more passengers while residential development can generate less.
1.1.2 The Impact of COVID-19 Pandemic also influences the ridership of public transport. Because of the breakout of COVID-19, the transport sector has experienced a drastic reduction in passenger traffic (Sarbast Moslem et al., 2020). According to LTA (2020), ridership of MRT plummeted by 75 per cent in April compared to pre-COVID levels in Singapore. Singapore exited Circuit Breaker from June 2 June 2020 and started Phase One: Safe Re-opening. Economic activities which do not pose high risk of transition were gradually reopened in this stage, while social, economic and entertainment activities with a higher risk will remain closed. Passenger flow of the railway partly recovered compared with the time during Circuit Breaker. Phase Two of reopening started from 19 June 2020. Certain places for recreation and activities gradually reopened in the late June or early July under strict restrictions. Therefore, passenger flow of many MRT stations increased significantly in July. The Land Transport Authority (LTA) reported on Feb 10, 2021 that average daily ridership for public transportation fell by 34.5 per cent to 5.04 million (11 year low), which broke the trend of consecutive rises in public transportation in the previous 15 years. Work from home induced by Covid-19 led to the reduction of MRT and LRT lines to 2.162 million a day. The reduction in public transport ridership has raised concerns regarding the financial sustainability of the MRT network, which was primarily designed with pre-pandemic usage in mind.(Christopher Tan, 2020) Singapore, with limited land, requires more careful and effective urban planning. The analysis of railway passenger volume can help to understand human mobility, and therefore further understand human daily activities.
1.2 OBJECTIVES Identify the changing trend of people’s destination across different pandemic phases. Identify how the pandemic influences the types of urban areas that people visit most.
1.3 DATA INTRODUCTION There are two main datasets used in this project. Firstly, the orgin-destination (OD) dataset is published by the Land Transport Authority at their Datamall. It contains a varaiety of information about transportation patterns at a high spatial and temporal level of detail from January 2020 to Feburary 2021. The second dataset will be using is the Master Plan 2019 Land Use downloaded from Department of Singapore, which includes the geographic location and coordinates of each MRT station. Combining these two datasets together will facilitate spatial analysis.
Figure 2.2.1 Land Use within 700-meter Buffer Zones of Rail Stations (Interactive Map) This figure illustrate different types land use in the surrounding area each rail stations. We need to further calculate the proportion of each type of land use for each station.
2 DATA PREPROCESSING 2.1 DATA WRANGLING
Figure 2.2.2 Proportions of Different Land Use in 700-meter Buffer Zones of Rail Stations
The land use distribution varies a lot among different rail stations. To standardize the land use classification, we identify 9 station typologies based on the proportion of different land use.
Figure 2.2.3 Rail Station Typologies Based on Land Use 1. Residential Land use distribution: “RESIDENTIAL” + “RESIDENTIAL / INSTITUTION” > 40%; “COMMERCIAL” + “COMMERCIAL & RESIDENTIAL” + “RESIDENTIAL WITH COMMERCIAL AT 1ST STOREY” < 8% The station is mainly surrounded by residential housing, possibly with a small amount of educational land use. Most passengers of these stations are neighborhood residents. 2. Residential with Town Center Land use distribution: “RESIDENTIAL” + “RESIDENTIAL / INSTITUTION” > 40%; “COMMERCIAL” + “COMMERCIAL & RESIDENTIAL” + “RESIDENTIAL WITH COMMERCIAL AT 1ST STOREY” >= 8% Besides large area of residential communities, there are also commercial development close to the station which form a lively town/ regional center. The representative stations are ANG MO KIO and CLEMENTI 3. Open space Land use distribution: “PARK” + “WATERBODY” + “OPEN SPACE” > 20% The station is in adjacency with park, open space or water body, which usually acts as recreation destination. The represent ative stations are BOTANIC GARDENS and HARBOURFRONT 4. Commercial Land use distribution: “COMMERCIAL” + “COMMERCIAL & RESIDENTIAL” + “RESIDENTIAL WITH COMMERCIAL AT 1ST STOREY” > 20% The station is surrounded by large commercial areas and serves as shopping destination and office spaces in the city. The representative stations are ORCHARD and BUGIS. 5. Business Land use distribution: “BUSINESS 1” + “BUSINESS 2” + “BUSINESS PARK” + “BUSINESS PARK - WHITE” + “BUSINESS 2 WHITE” + “BUSINESS 1 - WHITE” > 20% The station is surrounded by industrial & business parks, concentrating major manufacturing factories. The representative stations are JURONG EAST and ONE-NORTH. 6. Public Facilities Land use distribution: “Civic & COMMUNITY INSTITUTION” + “EDUCATIONAL INSTITUTION” + “HEALTH & MEDICAL CARE” + “LIGHT RAPID TRANSIT” + “MASS RAPID TRANSIT” + “PLACE OF WORSHIP” + “PORT / AIRPORT” + “SPORTS & RECREATION” + “TRANSPORT FACILITIES” > 40% The station is located close to public facilities such as schools, stadiums, transportation ports etc. Most of the passengers are users of these facilities. The representative stations are CHANGI AIRPORT and STADIUM. 7. White Land use distribution: “WHITE” >= 30% The station is located in the area used or intended to be used mainly for commercial, hotel, residential, recreation and other compatible uses, or a combination two or more of such uses. 8. Reserve Land use distribution: “RESERVE SITE” >= 30% The station is located in the area with specific uses yet to be determined. The representative station is TUAS LINK. 9. Others This type of stations cannot be identified as either of the typology above.
Figure 2.2.4 Rail Station Typologies (Interactive Map) All rail stations are classified into different land use typologies according to their land use profile. Some stations have multiple typologies like CC2 (commercial, open space), CC25 (business, public facilities), PW2 (public facilities, open space), etc. We didn’t try to restrict each station to one typology, since the diverse identity can make our analysis more accurate. The typologies of rail stations are joined to od data so we can further explore how people’s travel destination change according to different station typology in different pandemic phases.
3 DATA ANALYSIS 3.1 THE TREND OF RAIL PASSENGER VOLUME DURING THE PANDEMIC According to policies from Singapore government, we identify three phases of pandemic: pre pandemic (January 2020 - March 2020), Circuit Breaker (April 2020 - June 2020), and reopening (July 2020 onwards). To get a general idea about our people’s travel behaviou change during the pandemic, we first calculate the total rail passenger volume on weekdays and weekends/holiday eadh month.
Generally, passenger volume during weekdays are higher than during weekends/holiday. Passenger volume showed a sharp decrease from March 2020 to April 2020, when Singapore started to implement Circuit Breaker. May 2020 had the lowest passenger volume. Then passenger volume started to increase with weekday growing faster than weekends. After October the passenger volume are relatively stable despite a few fluctuations.
Figure 3.1.1 The Trend of Rail Passenger Volume during the Pandemic - Daily Rail Station Passenger Volumn on Different Months
3.2 POPULARITY OF MRT STATION
3.3 POPULARITY OF DIFFERENT TYPES OF RAIL STATION Introducing land use typology to analyze what types of land use are essential to people, and what types of land use are attractive to people.
Figure 3.2.1 Popularity of Rail Stations on Weekday (Interactive Map)
Figure 3.3.1 Popularity of Different Types of Rail Station - Daily Passenger Volumn of Different Types of Rail Stations during Different Pandemic Phases The boxplot indicates the popularity of different typologies of rail stations in three phases. The median indicates the average of passenger volume. Overall, Residential with Town Center is the most popular typology in every phase while Reserve typology is the least popular one. The passenger volumes fo each typology all experience a decrease in the circuit breaker phase with Commercial decreased the most. In the reopening phase, the passenger volume of Business typology and Residential typology almost backed to the pre-pandemic level while the passenger volume of Open Space and Public Facilities still kept a relatively low level.
3.4 THE VARIATION OF PASSENGER VOLUME IN DIFFERENT TYPES OF RAIL STATION
Figure 3.2.2 Popularity of Rail Stations on Weekends / Holiday (Interacticve Map)
Zooming in to see the distribution of popularity, Figure 3.2.1 and Figure 3.2.2 shows the popularity of each rail station from January 2020 to February 2021. Overall, the central region influenced by the pandemic the most and even in February 2021, the passenger volumes still did not back to the pre-pandemic level. Similarly, Jurong East, Woodlands and Changi Airport also experienced a sharp decrease and gradual increase during the past 14 months. Therefore, the influence of the breakout of pandemic is sharp and enormous while the recovery process is slow. During the pandemic, no matter in weekdays or weekends/holidays, people’s activities were, actively or passively, minimized. But people’s commuting demand still exists. We speculate that the travel behaviors happened during the circuit breaker phase were essential while the increased number of travel behaviors happened in pre-pandemic and Reopening were relatively nonessential, which can consider as the travel happened based on ‘attractive’ factors.
Figure 3.4.1 Weekday - Weekend Variation of Passenger Volume in Different Types of Rail Stations It is noted that there is a large decrease in variation of business from weekdays to weekends indicated by the low median value compared to other land uses. The main reason could be the declining use of MRT for commuting . The interquartile range reflects the variation in popularity between weekdays and weekends. Residential and residential with town center show little variation, which may be led by the stable daily routines and activities and use of MRT near passengers’ home. Public Facilities also present distinct outlier, and the possible reason could be the increasing traveling during weekends which lead passengers to places where located far away like Changi Airport.
3.5 SPATIAL VARIATION OF PASSENGER VOLUME VARIATION IN DIFFERENT PANDEMIC PHASES The analysis of commuter pattern from the period of prepandemic to circuit breaker indicates a relatively higher variation in the residential, residential with town center, commercial and Business typologies. This variation analysis draws to a conclusion that the significant change in ridership pattern after the circuit breaker to residential MRT typologies might have been the result of increasing safety regulations and work from home implementations since the pandemic outbreak. Additionally, commercial typologies largely concentrated into the central region, which can also explain why the central region suffered the most in the pandemic. Figure 3.5.1 Spatial Variation of Passenger Volume Variation (Pre-pandemic - Circuit Breaker) Figure 3.4.2 Weekday - Weekend Variation During Different Phases of the Pandemic
This map ndicates an increased positive variation in the residential typology, especially in Punggol area. At the same time, commercial and business activities began to recover, which had positive impact on Marina Bay and the surrounding areas. There is a outlier of White typology with sharply increased passenger volume, which may also contributed to its location that near the waterfront or other unclear reason.
This plot presents that there is an increase in popularity from weekday to weekend in the use of MRT stations in residential with town center and commercial areas before the pandemic. The reason could be that people who need to work from Monday to Friday tend to spend time shopping and go to town center on weekends. During the circuit breaker, the changes in variation were shrunk for all typologies, which was due to the circuit breaker measures including closure of all schools and non-essential workplaces and restrictions on movement and gatherings Furthermore, during the reopening stage, MRT station in residential with town center experience the greatest increase in use from weekday to weekend, and residential, public facilities, commercial and business show similar amount of variation. The variation of all MRT station typologies decreases from pre pandemic to circuit breaker. The mean value of commercial is relatively low compared to other typologies, indicating the declining use of MRT close to shopping mall where used to be crowded with high flow of people prior to the pandemic. The variation of MRT station in residential area is relatively higher although it is decreasing, it was probably due to people choose to move around their residences to purchase necessities in those uncertain times.
Figure 3.5.2 Spatial Variation of Passenger Volume Variation (Circuit Breaker - Reopening)
4 CONCLUSION 4.1 GENERAL CONCLUSION (1) The influence of breakout of pandemic is sharp while the recovery process is slow The passenger volume in each rail station experienced a dramatic decrease in April and the central region, where the passenger volume most concentrated, suffered the most. However, the recovery process is much slower. After the reopening in July, the passenger volume increased gently and in February 2021, the volume still did not return to the pre-pandemic level due to possible reasons such as switching to work-from-home mode. (2) Rail stations located in Residential, Residential with town center, and Open space are more popular
Figure 3.4.3 Pre Pandemic - Circuit Breaker Variation The median illustrates higher increase in the variation in popularity of commercial areas, open spaces, and others from circuit breaker to reopening phrase, while the variation in business areas is relatively low. The reason could be the reopening status of different land uses in Phase One and Phase Two after June, 2020. Business first recovered from the Circuit Breaker from 2nd June 202, while pert of commercial activities and open spaces gradually reopened after 19th June. Most of them reopened in July, 2020. On the other hand, besides others typology, the interquartile range of commercial vary to a great extent. There are some typologies showing notable outliers. For example, the outliers of open space are possibly resulted from the different reopening status and the development of each open space. Additionally, the outlier of public facilities is mainly caused by the reopening of transit systems such as flights. Figure 3.4.4 Circuit Breaker - Reopening Variation
For the first time period from pre pandemic to circuit breaker between January and June 2020, rail stations in Residential areas witnessed higher passenger volume compared to other typologies while there was a significant decline in the use of rail stations in Commercial areas. The second period between circuit breaker and reopening phases till Feb, 2021, it was noted that the passenger volume variations are relatively high in stations with Residential, Residential with town center, and Open space. People tend to travel more frequently to places near their dwellings rather than long-distance trips in daily life. In addition, they seem to value the opportunities for open spaces after they experienced restricted quarantine measures during circuit breaker. (3) The pandemic greatly influenced the place people work The variation of weekday and weekend in passenger volume changed dramatically from pre-pandemic to circuit breaker, the shrunk in variation indicates that people travel less in workdays. The shrunk in variation is most significant in Town Center, Commercial areas, as those areas gather a large number of office jobs, we can perceive people that worked there were working from home during the pandemic. Whereas the shrunk in variation of Business stations are relatively small, suggesting that manufactory, industrial jobs still need workers to work on-site. Additionally, as the Commercial and White types of stations are largely concentrated into the central region, we can conclude that there is a shifting trend of people working in concentrated CBD toward their individual homes.
4.2 LIMITATIONS The project has some scope of limitations in terms of data acquisition and data analysis. For instance, the current land use is not in Singapore’s government database therefore we adopt the Master Plan 2019 land use layer that represents the future land use, thus led to bias of some undeveloped stations. Moreover, there might be other unaccounted factors affecting the passenger volume of a station and our analysis could be improved.
Relationships Between Dietary Habit, Income and Diabetes Prevalence / Geospatial Research Based on R Postgraduate Academic Work: Planning Technologies (Individual Assignment) Instructors: Filip Biljecki 02.2021
1 BACKGROUND
Food consumption can reflect dietary habits to some extents and may influence people’s health conditions. Healthy dietary pattern helps to protect against malnutrition and noncommunicable diseases such as diabetes, heart disease, stoke and cancer[1]. For instance, in “Tesco Grocery 1.0, a large-scale dataset of grocery purchases in London (Aiello et al., 2020)”, authors illustrated the close relationships between carbohydrates and diversity/entropy of energy from nutrients with diabetes[2].
Comparison of energy intakes from different nutrients with recommended compositions Fractions of energy from fats, saturated fats, sugar, protein, carbohydrate, fibre and alcohol are compared with compositions recommended by WHO and USDA.
According to WHO and Dietary Guidelines for Americans, a healthy diet requires for less than 30% of total energy intake from total fats (with less than 10% from saturated fat), less than 10% of total energy intake from free sugars[1], 45% - 65% of energy intake from carbohydrates and 10% - 35% from protein[3]. Dietary behaviour may vary depending on various factors, including age, lifestyle, physical activities, cultural context, food availability, social economic status, etc. Previous studies have illustrated that lower income is associated with a poor quality dietary intake with less consumption of fruits and vegetables and more consumption of sugar-sweetened beverages[4].
Fats, saturated fats and sugar intakes are all far more than recommended fractions (which are 30%, 10% and 10% respectively). Protein is mostly within the recommended range, but at a relatively lower level. Energy from carbohydrate is partly below recommended level. Overall, people in London have an unhealthy diet with large fats and sugar intakes and relatively low energy intakes from protein and carbohydrate.
Based on previous indications about the relationships between healthy diet and diabetes, as well as dietary behaviour and income, this research examines the relationships between dietary habits (reflected from food purchasing), income and diabetes prevalence in London, UK.
2 DATA PREPROCESSING Dataset 1. Area-level grocery purchases in London (2015, ward): The data contains 202 columns, including the number of transactions and nutritional properties of the typical food item bought (including the average caloric intake and the composition of nutrients) in different areas of London. 2. Household Income Estimates for Small Areas (2001-2012/13, ward): The data contains mean and median average gross annual household income in different areas of London, from 2001 to 2012/13. 3. Diabetes Estimates (2016, ward): The data illustrates the number of people aged 17+ on a register for diabetes at each GP practice in different areas of London.
Clean data 1. Use “f_energy_{nutrient}”(fraction of energy from different nutrients), “h_nutrients_calories”(diversity/entropy of energy from nutrients), “f_{liquids}”(fraction of liquids purchased) and "f_{food category}_weight“(fraction of food weight purchased) as indicators for dietary habit.
Figure 2 Nutrition structure - fractions of energy intake from different nutrients
3 RELATIONSHIPS BETWEEN DIETARY HABIT, INCOME AND DIABETES PREVALENCE 3.1 SPATIAL DISTRIBUTION OF NUTRITION INTAKES, INCOME AND DIABETES PREVALENCE According to Figure 1, fractions of nutrition intakes vary in different areas. I visualize the fractions nutrients that significantly exceed the standards (fats, saturated fats, sugar) as well as carbohydrate and entropy of energy from nutrients (which are highly related to diabetes in literature[2]) on map to observe spatial variation of nutrition intakes. Spatial distributions of annual average household income and diabetes area also visualized to map to see if there are certain patterns illustrated.
2. Use”Mean 2012/13" as indicator for annual household income since it’s closet to 2015.
Higher fractions of energy intake from total fat are represented in central areas and some south-western areas. Eastern region generally has lower fraction of fat intake, though it still far exceeds the standard.
3. Use“estimated_diabetes_prevalence” as indicators for diabetes prevalence.
Food and liquids consumption composition Fractions of liquids and food purchased are illustrated below to provide a general idea of food consumption pattern in London.
Higher fractions of energy intake from saturated fat are represented in western London.
Fruit and vegetable and grains take up largest proportion of consumption and varies more in different areas. While the variations of other items consumption are smaller.
Lower fractions of energy intake from sugar are represented in central and northern London. Intakes in marginal areas are relatively higher. Lower fractions of energy intake from carbohydrate are represented in central London. Intakes in marginal areas are relatively higher, especially in the east. Higher diversity/entropy of energy from nutrients is represented in central and southwestern areas. Higher income is represented in central and some south-western areas. Lower diabetes prevalence is represented in central and some south-western areas. Prevalence in the northwest and north east are higher.
Figure 1 Food consumption pattern
Figure 3 Spatial variation of nutrients fractions, nutrition diversity, income and diabetes prevalence
All factors show observable spatial variation to some extent. In general, higher fraction of fat intake, nutrition diversity and income, and lower fraction of sugar and carbohydrate intake and diabetes prevalence usually show similar spatial pattern.
3.2 RELATIONSHIP BETWEEN INCOME AND DIABETE PREVALENCE
4 REGRESSION MODELS
According to Figure 2, household income shows similar pattern with diabetes prevalence. To further explore possible relationship between these two factors, the correlation of income and diabetes are calculated.
Based on previous analysis, I choose fractions of energy intake from protein, carbohydrate and fibre and diversity/entropy of energy from nutrients as indicators to predict household income. To make predicitons more accurate, fractions of consumption of dairy, fruit and vegetable and grains are also used as indicators.
4.1 NUTRITION/FOOD INTAKES AND INCOME Higher annual income areas are usually with lower prevalence of diabetes. The coefficient is around -0.7. Regions with higher income population probably have lower rates of diabetes patients. However, there are various and complicated factors which may influence diabetes prevalence, so we can not simply conclude direct relationships between household income and diabetes prevalence. The observed are positively correlated with the predicted with a coefficient of 0.83. Dietary habit can be an indicator to household income.
Figure 4: Correlation between income and diabetes prevalence
3.3 CORRELATION BETWEEN NUTRITION INTAKES, INCOME AND DIABETES PREVALENCE To explore the relationships between nutrition structure, income and diabetes prevalence, I calculated the correlation between different nutrient intakes which are shown as unhealthy above (fat, saturated fat, sugar, carbohydrate), diversity/entropy of energy from nutrients, income and diabetes prevalence. Incomes are positively correlated with nutrition diversity and negatively correlated with diabetes prevalence and fraction of energy intake from carbohydrate.
Figure 7: Correlation between the observed and the predicted of diet - income model
4.2 NUTRITION/FOOD INTAKES, INCOME AND DIABETES PREVALENCE
Diabetes prevalence is positively correlated with fraction of energy intake from carbohydrate and negatively correlated with nutrition diversity and income. Unhealthy dietary habit with over fat, saturated fat and sugar doesn’t have a strong correlation with household income and diabetes prevalence, while carbohydrate intake and nutrition diversity have a relatively strong correlation. Purely correlating nutrition structure (with unhealthy nutrient intakes) with income and diabetes may be insufficient to explore relationships diet, income and diabetes.
The observed are positively correlated with the predicted with a coefficient of 0.86. Dietary habit can be an indicator to household income.
Figure 8: Correlation between the observed and the predicted of diet - diabetes model
Figure 5: Correlation between nutrition structure, income and diabetes prevalence
3.4 CORRELATION BETWEEN NUTRITION/FOOD INTAKES, INCOME AND DIABETES PREVALENCE To further explore relationships between dietary behaviour, income and diabetes prevalence, I calculated the correlation between other nutrient intakes (other than the ones in figure 4), proportion of food and liquids consumption, income and diabetes prevalence. Incomes are positively correlated with fractions of fibre and protein intake, consumption of wine, dairy, fruits and vegetables, and negatively correlated with consumption of soft drinks and grains. Diabetes prevalence is positively correlated with consumption of grains and soft drinks, and negatively correlated with fractions of protein and fire intake, consumption of wine, dairy, fish, fruits and vegetables. Higher incomes are probably more related to healthier diet with more fibre and protein intakes through more consumtion of dairy, fruits and vegetables. Also, healthier diets are probably more related to lower diabetes prevalence. Figure 6: Correlation between nutrition & food structure, income and diabetes prevalence
5 CONCLUSION Overall, general diet habits in London are unhealthy with high intakes of fats, saturated fats and sugar. Dietary pattern has some correlations with incomes and diabetes prevalence. Areas with higher household income are likely to have low diabetes prevalence, which is probably due to people’s different dietary behaviours in certain areas. Higher incomes are more likely related to healthier dietary habits with more energy intake from protein and fibre, more consumption of dairy, fruits and vegetables and high nutrition diversity. However, the overly intake of fats, saturated fats and sugar doesn’t represent a high correlation with household incomes. Also, healthier diet with more energy intake from protein and fibre, more consumption of dairy, fish, fruits and vegetables and high nutrition diversity are related to lower prevalence of diabetes.
6 LIMITATIONS (1) The data was collected from Clubcard customers of Tesco, which may be not representative for the overall population. (2) There were food consumption other than food purchasing from Tesco, so the dietary habits may be different when we take all kinds of food consumption into consider. (3) The distribution of Tesco may influence people’s purchasing pattern. (4) Food consumption cab be influenced by many social economic factors other than income, for instance, employment, education status, etc. (5) Diabetes can be influenced by many factors like accessibility to health care facilities, daily physical activities, etc.
What types of MRT station are popular in the postpandemic? / Preparatory Analysis of the Research "How People’s Travel Destinations Change with Pandemic Phases" Based on GIS and R Postgraduate Academic Work: Spatial Bigdata & Analytics (Team Project) Instructors: Sandeep Narayan Kundu 10.2020 - 12.2020
1 OBJECTIVES
3 ANALYSIS AND RESULTS
(2) Identify the variation trend in passenger volume and how people’s travel behavior vary from weekdays to weekends during the post-pandemic period. Further, figure out how urban functions correlate with the variation.
The station locations follow the urban planning principles, by reading the surrounding urban land use we are able to idenfity the urban functions the station serves, its user groups and their travel behaviors.
(1) Determine the passenger volume of each MRT station as a destination in the post-pandemic period in Singapore.
(3) Figure out what factors correlate with the unusual popularity of several MRT stations.
2 METHODOLOGY 2.1 DATA SOURCES
3.1 RAIL STATIONS TYPOLOGY CLASSIFICATION
Based on this we hope to use the proportion of land use to classify stations into different typologies. In this case, we decided to use the Buffer tool in ArcGIS pro to create a suitable area for station servers. The distance of Buffer analysis was set to 700 meters, as this distance was considered to be a suitable walking distance. Furthermore, we used Sum Statistics tool to sum the land use within the 700m buffer around the station. Figures below show our buffer areas and proportion of different land uses around each station. To s e t t h e s t a n d a r d f o r classification, we established the following 7 station typologies based on the background knowledge of urban planning. All 158 stations were classified under different typologies, resulting in 53 Residential Stations, 21 Residential with Town Center Stations, 26 Open Space Stations, 15 Commercial Stations, 23 Business Stations, 13 Public Facilities Stations and 7 Others Stations.
For monthly passenger volume by origin-destination train stations, the data source is the API from Land Transport Data Mall (https:// www.mytransport.sg/content/mytransport/home/dataMall.html). For land use, the data source is the Master Plan 2019 Land Use layer from Department of Statistics Singapore (https://www.singstat.gov.sg). For the station's geographical location of stations, the data source is the Master Plan 2019 Rail Station layer from Department of Statistics Singapore (https://www.singstat.gov.sg).
2.2 FRAMEWORK Step 1: Preliminary Data Analysis and Data Preparation
Figure 3.3.1 Buffer Analysis Step 2: Further Analysis and Visualization
3.2 STATIONS POPULARITY ANALYSIS 3.2.1 Popularity Hotspot Map
Figure 3.2.1.1 Weekdays Hotspot Analysis
Figure 3.2.1.2 Weekends Hotspot Analysis
In the weekday hotspot map, there are mainly four areas colored red: Jurong Industrial Estate, Yishun, CBD and Changi. In these areas, 6 MRT stations show high popularity: Boon Lay MRT station, Jurong East MRT station, Ang Mo Kio MRT station, Admirality MRT station, Yishun MRT station and Tampines MRT station.
2.3 ALGORITHMS
On comparing the weekend hotspot map with the weekday hotspot map,two primary differences emerge. Firstly, Tanjong Pagar MRT station, Tampines MRT station, Rochor MRT station, and Jalan Besar MRT station exhibit higher popularity on weekends, probably due to their commercial function or proximity to supermarkets. Secondly, Yio Chu Kang MRT station, which is located in the center of the map, exhibits significantly lower popularity on weekends.
3.2.2 Popularity Boxplots Boxplot is used here to illustrate the size, concentration and abnormal value of PV according to different types of MRT stations in weekdays and weekends, as well as the variation in popularity of different types of MRT stations from weekdays to weekends. PV is transformed into boxplots using RStudio ggplot2. Weekday Mean PV According To MRT Station Typologies The median indicates the overall popularity of MRT stations in different types of land use. Residential with town center and commercial area are more popular on weekdays. The low median value for business land use is possibly affected by some undeveloped industrial areas (which were defined as business land use under the 2019 master plan) with quite low passenger volumes. The interquartile range reflects the variation in popularity of different MRT stations belonging to the same typology. Passenger volumes in residential with town center vary most, followed by commercial areas and public facilities.
3.3 MONTHLY POPULARITY VARIATION ANALYSIS 3.3.1 Monthly Charts Symbol Map
Figure 3.2.2.1 Weekday Mean PV According To MRT Station Typologies (Algorithm: 2.3.1)
In general, the weekday passenger volume of most MRT stations are growing at a constant rate, pie charts are almost three equal. In detail, the pie chart map shows significant differences at 3 MRT stations. One pie chart shows that there has no passenger flow on June, little passenger flow volume in July and a significant increase in August. The other two have a more stable increase than the first, but still show a rapid increase compared to the others.
Figure 3.2.2.2 Weekend Mean PV According To MRT Station Typologies (Algorithms: 2.3.2)
Business land use has two distinct outliers, which may be due to the recovery of working activities in some industrial areas such as Jurong Industrial Estate. Open space also has outliers that are relatively distinct. A possible reason for this could be the different design and development level of different open spaces. For instance, well-developed open spaces like botanic garden are more popular with the public, while some simple green spaces without any facilities may lack in attractiveness and present low passenger volumes. Variations in open spaces may also be related to their different reopening status during the post-pandemic period. Residential have the largest number of outliers with limited variation, which may be related to different types of housing.
Figure 3.3.1.1 Weekdays Pie Chart Map
Weekend Mean PV According To MRT Station Typologies The median indicates a significant increase in the popularity of commercial areas from weekdays to weekends. Residential with town center and commercial areas continue to be more popular at weekends. The variation in the popularity of commercial areas shows an increase on weekends, while business land use and public facilities show a distinct decrease. Possible reasons fot this are the decrease in the working population and the increase in commercial and leisure activities such as shopping at weekends.
The pattern of variation in weekend passenger flows is slightly different from that of weekdays, with some MRT stations having the highest variation in July, but rapidly decreasing in August.
3.2.3 Weekday-Weekend Variation Boxplots The high median value indicates a sharp increase in the popularity of commercial areas and significant decrease in the popularity of business areas from weekdays to weekends. The main reason for this could be the decreasing use of MRT for commuting and the increasing commercial activities at weekends. The interquartile range reflects the variation in popularity between weekdays and weekends. Residential and other land uses show little variation, which may be due to the stable daily activities and the use of MRT close to passengers’ homes. Open space varies the most with two observable outliers. The main reason could be the increase of leisure activities based on the reopening status of various open spaces. Different design and development levels also influence the variation and outliers of open space. In addition to open space, public facilities also present distinct outliers. A possible reason could be the increase in traveling during weekends, which brings passengers to places such as Changi Airport.
Figure 3.3.1.2 Weekends Pie Chart Map
3.3.2 Monthly Variation Boxplots
Figure 3.2.3.1 Weekday-Weekend Mean Variation According To MRT Station Typologies (Algorithm: 2.3.3)
Figure 3.3.2.1 June-August Weekday Variation According To MRT Station Typologies (Algorithm: 2.3.4) One distinct outlier of open spaces is deleted.
Figure 3.3.2.2 June-August Weekend Variation According To MRT Station Typologie (Algorithm: 2.3.5) One distinct outlier of open spaces is deleted.
June-August Weekday Variation According To MRT Station Typologies
3.4.2 Classifying And Sifting
The median illustrates a higher increase in the variation in popularity of commercial areas and open spaces during weekdays from June to August, while the variation in business areas is relatively small. The main reason could be the reopening status of different land uses in Phase 1 and Phase 2. Business first recovered from the Circuit Breaker from 2nd June 2020, while some commercial activities and open spaces gradually reopened after 19th June (most of them reopened in July).
Based on the WEEKDAY MEAN PV and WEEKEND MEAN PV from June to August, the popular MRT stations are further classified: Sifting out the third type of MRT stations that are less popular on weekends, the following part will discuss what makes the first two types of MRT stations popular in the post-pandemic period. (1) WEEKEND MEAN PV grew rapidly from June to July, and became stabled from July to August: Changi Airport, Habour Front and Botanic Gardens;
In terms of the increase in popularity of MRT stations with different typologies from June to August, as indicated by interquartile range, open spaces show the most variation, followed by public facilities. Both two typologies show distinct outliers. The variation and outlier of open space are possibly caused by the different reopening status and the development of each open space. The outlier of public facility (Changi Airport) is mainly caused by the reopening of transit and commercial activities in July. June-August Weekend Variation According To MRT Station Typologies Overall, the increase in popularity of each type of land use is more obvious on weekends than on weekdays. The main reason could be the recovery of human outdoor activities after Phase 2 reopening. The median illustrates higher increase in the variation of popularity of commercial areas and open spaces during weekends from June to August. The interquartile range shows that the increase in popularity of open spaces varies most according to different MRT stations. Its outlier is also distinct compared to other typologies. A possible reason could be the increasing outdoor activities of the working class on weekends. The reopening of some well-designed open spaces attracts more visitors for refreshment and recreation.
(2) WEEKEND MEAN PV kept the rapid growth from June to August: Bay Front and Mirna South Pier; (3) WEEKEND MEAN PV l o w e r t h a n W E E K D AY MEAN PV: Kranji, Marina Bay and Labrador Park.
3.4 OUTLIER STATIONS ANALYSIS 3.4.1 Picking Out The Popular Station Rank the MRT stations based on Figure 3.4.1.1. The spikes in Figure 3.4.1.2 indicate that the stations have higher passenger volume variation on weekend than the predicted value by weekday data. The spikes in Figure 3.4.1.3 indicate that there is more unnecessary travel happened at the weekend than the predicted value by weekday data. The spikes picked from the two figures are considered to be the popular MRT stations. Based on the figures, eight stations are idenfitied as the most popular: Bayfront, Labrador Park, Marina Bay, Marina South Pier, Harbour front, Botanic Gardens, Changi Airport and Kranji.
3.4.3 Discussion: Popular MRT Stations In The Post-Pandemic (1) MRT stations with open space are more popular Except for Changi Airport, the other four popular stations all belong to the Open Space typology, suggesting that open space is attractive to people in the post-pandemic period. In addition, Harbour front, Bayfront and Marina South Pier are located on the waterfront, which is associated with commercial activities. The rapid growth from June to August may also indicate that the stations affected by the pandemic the most. The two stations, Bayfront and Marina South Pier, have no residential land use within the 700m buffer. According to our previous analysis, residential land use is related to the relatively stable MRT passenger volumes. With white land, which mainly supports commercial and business activities, these two stations have a lower risk resistance capacity. However, the growing passenger volume indicates the strong residence brought by open space and attractive activities.
Figure 3.4.1.1 JuneAugust Weekday Variation
(2) Reopening policy influence the passenger volume at the early stage
Figure 3.4.1.2 JuneAugust Weekend Variation
Figure 3.4.1.3 The D i f f e r e n t Va l u e O f Weekday-Weekend Va r i a t i o n I n J u n e A n d August
All the stations responded to the reopening policy in June. Especially the phase two of re-reopening from 19 June. Recreation and activities gradually reopened, and park facilities including play grounds, beaches, carparks etc. also reopened to the public. Therefore, those MRT stations with open space became popular. For Changi Airport, the close of two terminals have a great impact on its passenger volume, the reopening of Jewel Changi Airport as well as the reinstatement of flights attracted more people come to this station. Different from other stations, Bayfront MRT station had continuous re-opening steps from June to August, stimulating the commercial and leisure activities. It is also the possible reason for its continuous growth of passenger volume. The other station with rapid growth from June to August, Marina South Pier, has one of the ferry terminals in Singapore. Citizens had increasingly willingness to enjoy the leisure activity at southern islands after the re-opening, facilitating the revitalization of Marina South Pier. Table 3.4.3.1 Reopening Policies Related To Each MRT Station
Towards a Healthy City: A Study on Built Environment Determinants of Public Health in New York City / Geospatial Research Based on STATA and R Postgraduate Academic Work: Quantitative Methods for Urban Planning (Team Project) Instructors: Liao Wen-Chi 02.2021 - 04.2021 This study examines the extent to which the built environment correlates with public health outcomes in New York City. Based on recent GIS and survey-based datasets, OLS and MESS regression methods were used to examine differences in each model’s outcomes and to determine the appropriate model. Our results demonstrate statistical significance at the city level, but mixed results at the borough level. Overall, we determined that air quality, tree coverage, fresh food access, health service, and active travel infrastructures contributed significantly to health as compared to the other environmental factors.
1 INTRODUCTION
2.2 RESEARCH DESIGN
1.1 RESEARCH BACKGROUND Cities provide some of the worst as well as some of the best environments for health and well-being (Burdett, et al., 2005): The challenges posed by rapid urbanization in relation to environmental stressors and socio-economic disparities are associated with urban health and well-being. As one of the largest cities in the world, New York City (NYC) has always benn plagued by public health concerns. Studies by Thorpe et al. and Rummo et al. (2018) have found marked increases in diabetes and obesity rates among New Yorkers between 2004 and 2014. Last year, NYC emerged as the epicentre of the COVID-19 crisis in the US.
1.2 LITERATURE REVIEW There is no dearth in studies attempting to identify factors that influence health and well-being in cities. Most of them entail social surveys and self-rated reports (Ballas, 2013; Carter & Horwitz, 2014; Von Szombathely et al., 2017). These analyses focus either on investigating how public health is affected by population demographics in terms of size, density, and composition (Galea et al., 2005; Rydin et al., 2012), or association between environment determinants and well-being (Krefis et al., 2018; Salgado et al., 2020).
To test the hypothesis, the research conducts a health-environment related study based on linear regression model and spatial autocorrelation regression and the relevant testing processes.
2.2.1 Research Framework Based on the research objective and the hypothesis, the research framework first follows a conventional OLS regression model to explore the relationship between health and the built environment, with statistical tests to check the parameters. In particular, if the model shows with spatial autocorrelation in the residuals, the spatial autocorrelation regression (SAR) models would be considered. The comparative studies between different boroughs (Bronx, Brooklyn, Manhattan, Queens and Staten Island) are also employed. The diagram is shown in Figure 2.2.1.
Green space provision: Schipperijn et al (2017) found that individuals with greater proximity of urban parks have more healthy habits. Pereira et al (2012) mentioned that the prevalence of trees a lined streets or footpaths is associated with lower risk of coronary heart disease. The study conducted by Adhikari et al (2020) stated that green spaces can lead to improvement in healthrelated quality of life due to the ecological benefits. Accessibility of amenities: Zhang et al (2008) mentioned that people with low accessibility to health care services tend to be more severely ill when diagnosed. Scheffler et al (2015)'s study highlighted that inadequate availability of clinic and pharmacy impacts on adverse health outcomes. It is found by Kelly et al (2014) that frequent fresh food market shopping is associated with increased vegetable intake, leading to lower risk of chronic diseases. Environmental Quality: Wong et al (2001) and Dockery (2001) found that high air pollutant levels coupled with increasing vehicular traffic volume is linked to daily mortality in cities. However, there remains a gap in a holistic correlation study between built environment factors and physical health in NYC.
1.3 RESEARCH OBJECTIVES & HYPOTHESIS The research therefore aims to investigate the relationship between physical health and built environment factors, to identify the most influential determinants for health promotion and to propose spatial planning strategies for NYC. The specific research questions are: (1) What are the potential built environment determinants of public health? (2) Are they beneficial or detrimental to public health? Hypothesis 1: Greater environmental quality as defined by less air pollution, greater green coverage, and greater access to parks is correlated with improved physical health for adult New Yorkers. Hypothesis 2: Greater access to healthier food choices, as reflected by proximity to fresh food markets, and areas covered by the FRESH policy is correlated to improved physical health for adult New Yorkers. Hypothesis 3: Proximity to health care facilities such as pharmacies and clinics is correlated with improved physical health for adult New Yorkers. Hypothesis 4: Greater levels of infrastructure coverage promoting healthy lifestyles as defined by proximity to sports facilities, walkability index, bicycle lane density, is correlated with improved physical health for adult New Yorkers. Hypothesis 5: Of the factors listed above, their impacts on adult physical health have spatial disparity across NYC.
Figure 2.2.1 Research Framework
2.2.2 Ordinary Least Square (OLS) Regression Model The OLS method is a type of linear least squares method for estimating the unknown parameters in a linear regression model. The samples are used to estimate the linear regression model. xi represents independent variables, β represents the coefficient of variables, and ε represents the error term.
2.2.3 Matrix Exponential Spatial Specification Model (MESS) The MESS model is for modelling spatially dependent data. It can produce estimates and inferences like those of conventional SAR models, but has analytical, computational and interpretive advantages (Zaefarian et al., 2017). The model considers spatial disparity and retains P-value and coefficients of independent variables.
3 REGRESSIONRESULTS
3.1 COMPARISONS BETWEEN MODELS
2 METHODOLOGY
The methodology employed in this research is basically regression statistical approaches to explore the relationship between the impact of built environment on general health in NYC. The number of samples is 2,096 according to the collected valid data, which refers to the 2,096 census tracts in NYC as the studied objects. The study year is 2019. The statistical softwares used include STATA and RStudio.
2.1 DATA SOURCE
The data collected are categorized as numerical data and spatial data. The numerical data include health, demographic, and some descriptive environmental information such as air pollution, which are collected from “500 Cities” Health Project in the US, NYC Open Data, NYC EDC, Social Explorer and U.S. Environmental Protection Agency. Spatial data refers to geo-referenced environmental information, all based on GIS shapefiles and collected from Open Street Map. The study assumes that the built environment in NYC has not changed significantly from 2019 until now (as Open Street Map provides the latest spatial information). There are 10 independent variables and 3 control variables. The dependent variable represents “general poor health”, which is the index describing the percentage of adults reporting at least 14 unhealthy days in a month. The spatial data are processed with buffer zones indicating their service range. The buffer radii are defined according to existing literature and empirical practice in urban planning and design practice. The independent variable data are normalized to the interval [0,10] for coefficient comparison. The detailed metadata are shown in Table 2.1.
(left) Table 3.1 Empirical Results Of Environmental Factors On Poor Physical Health Rate
Table 2.1 Information Of The Collected Data
(right) Appendix 3.1.1 OLS Test Results
Table 3.1 summarizes the results of the OLS and the MESS models. The two models yielded marginally different results; the statistically significant built environment variables varied in the two models. The R2 value of MESS model (0.85) is higher than the OLS model counterpart (0.82), indicating that MESS model has a better explanatory capacity. According to the heteroscedasticity and the multicollinearity test results (Appendix 3.1.1), the OLS model is inefficient and could lead to biased inferences regarding individual factors. Moreover, the spatial autocorrelation test (Appendix 3.1.2) suggests that there is spatial autocorrelation in the residuals of the traditional OLS model applied to geospatial data. As the MESS model is able to reduce the impact of autocorrelation in residuals, we prioritise our focus on the MESS model.
4 DISCUSSION
4.1 KEY VARIABLES
Table 4.1 The Mean And Standard Deviation Of Key Variables In Each Borough Appendix 3.1.2 Spatial Autocorrelation Test Of OLS Residuals
3.2 EMPIRICAL RESULTS OF MESS MODEL The shaded cells in Table 3.1 indicate environmental factors that are vital determinants of physical health, including Air Pollution, Tree Coverage, Proximity to Fresh Food Markets, Clinic, Pharmacy, and Infrastructure Coverage of Active Mobility especially the Bicycle Lane Density. The results partially support their respective hypotheses 1, 2, 3 and 4. From the coefficients, Hypothesis 4 is well supported as both Walkability and Bicycle Lane Density have significant negative effects on poor health, denoting the promotion of active travel leads to better physical health in NYC. Interestingly, factors coefficients of Hypothesis 3 contradict each other, with Proximity to Clinic showcasing significant positive effect whilst Pharmacy has a significant negative effect on physical health. The explanation could be derived from a previous study that: clinic practitioners minimize total traveling distance between patients and the facility to reduce cost and improve efficiency (Peng, 2014). Therefore, the positive coefficient of Clinic is the result of location choice, and comparatively, expanding Coverage of Pharmacy could be a more efficient and operable way for planners to improve urban health services.
4.1.1 Environment (Hypothesis 1) Air Pollution shows a positive association with poor health level in Manhattan and Bronx while Tree Coverage is negatively correlated with poor health level in all boroughs. It is observed that the lowest level of air pollution in Queens illustrates a significant negative association. In Manhattan where a high mean of air pollution with high variation is presented, there is a significant positive effect on poor health. The reason could be the diseases caused by the exposure to particulate matters emitted by vehicles in different areas. On the other hand, for Tree Coverage, the negative correlation is more evident in boroughs with lower numbers of planted trees and less variation, such as Queens and Brooklyn. There is greater prevalence of trees in lined streets or footpaths in Manhattan and Bronx, as their mean and standard deviation are higher than in other boroughs, which may lead to better health condition. Air Pollution
Trees
Hypotheses 1 and 2 are difficult to ascertain as only partial factors exhibit statistical significance, suggesting the varying effects of environmental enhancement and fresh food accessibility improvement actions on shaping a healthier city. However, as the boroughbased dummy variables significantly influence the poor health rate which strongly support Hypothesis 5, and the fact that local variances are eroded in NYC global model, the two hypotheses could be further tested in comparative studies in different boroughs.
3.3 COMPARATIVE STUDY IN DIFFERENT BOROUGHS Table 3.3 indicates the empirical results of MESS Model in different boroughs. Overall, Hypothesis 5 can be further supported that for different boroughs, the significant variables are different, and the health condition can be well predicted in Manhattan with R-squared 0.93. The results in lower income boroughs, Bronx and Brooklyn, support Hypothesis 1, 2, 4, indicating that some basic factors like environmental quality, accessibility to healthy food and infrastructures promoting physical exercise are lacking in poor areas. The results in Queens support Hypotheses 1, 3, 4, showing that the food insecurity problem is less serious while, health care facilities become more important. The results in Manhattan can only support Hypothesis 4, which could be because the built environment in Manhattan is more complet, and people with higher income has a greater ability to acquire diverse services.
Table 3.3 Empirical Results Of Environmental Factors On Poor Physical Health Rate (Borough Level By Household Income Order)
Figure 4.1 Environment Factors Distribution
4.1.2 Food Access (Hypothesis 2) Access to Fresh Food Markets has a significant negative effect on poor health level in Bronx and Brooklyn, and an insignificant effect in Manhattan. Compared other boroughs, Manhattan has more access to fresh food, which may lead to less variation of food access in different tracts and less obvious correlation. Queens has an insignificant positive correlation, which may result from its smaller numbers of fresh food market and the poor heath level may be influenced by other factors. Fresh Food Policy has a negative effect on poor health level in Brooklyn and Bronx. The more significant correlation in Brooklyn may be due to its less fresh food incentives and larger variation among tracts. Queens has a significant positive correlation which may result from less fresh food policies in this borough and poor health level can be largely affected by other factors. Manhattan has an insignificant positive correlation which may be influenced by its higher population income. People with higher income may be less effected by fresh food policies when purchasing food. Fresh Food
Figure 4.2 Food Access Infrastructures/Policy Distribution
FRESH Policy
5 CONCLUSION
4.1.3 Health Services (Hypothesis 3) Notably, Clinic has a significant positive correlation with poor health level in all boroughs. The high mean and high variation of clinic in Brooklyn and Manhattan lead to the most obvious positive association compared to Bronx and Queens. Besides the reason for the positive effect related to location choice explained in the previous section (3.2), another reason could be that people in these areas have adequate access to other forms of health service providers. Moreover, Pharmacy shows a significant negative correlation with poor health level in Manhattan and Queen because people have available access and choices in seeking medical support. The negative association is insignificant and less pronounced in Brooklyn, where the mean of existing pharmacy is relatively low. In Manhattan, the percentage of adults without insurance is the lowest while the median income is the highest, indicating people are more willing to access to pharmacy when there are minor health concerns. Clinic
Pharmacy
This study aims to identify the environmental determinants of public health. An OLS and a spatial regression model are successfully set up to model the health situation in NYC. It is evident that air pollution, tree coverage, fresh food access, daily health service such as clinics and pharmacies, and infrastructure for pedestrians contribute significantly to public health. Surprisingly, the walk to park index makes no statistically significant contribution in this study. The research suggests that the natural environment within cities, including air quality and tree cover, should be taken into consideration. Strategies to promote green infrastructure would help alleviate the environmental pressures associated with urban density. Additionally, as health-related factor involves various urban aspects, promoting hybrid service infrastructures that integrate healthcare and fresh food, as well as pedestrian path systems would ensure comprehensive access to services and enhance urban vitality with urban multifunction.
5.1 LIMITATIONS The most apparent limitation is the lack of time series data for the built environment for panel data analysis. In addition, the spatial data reflect real-time conditions rather than those in 2019. Furthermore, there is limited research on the radii of buffer zones. The robustness of our results is compromised if the radii assumptions are altered. Finally, the results for park and green space contradict the literature and intuition. This may be due to the different qualities of urban green space and therefore their capacity to benefit public health.
5.2 FUTURE WORK Figure 4.3 Health Services Infrastructures Distribution
4.1.4 Physical Exercise Related infrastructures (Hypothesis 4) Walkability has a negative effect on poor health level in all boroughs. Negative correlations in are significant Bronx and Brooklyn, possibly due to their less pedestrian-friendly built environment and larger populations of young people who may prefer to walk to transit. Bicycle Lane Density has a negative effect on poor health level in Bronx (insignificant), Brooklyn and Queens, and an insignificant positive effect in Manhattan. Manhattan has a much higher bicycle lane density but people living there are usually with higher income and may have more transport choices. Other reasons like population age and road safety may also influence people’s choice of transition mode. Walkability
Bicycle Lane Density
Future studies should address the above research limitations. Other aspects of urban factors need further study, such as people’s daily behavior or habits caused by the physical environment. All the environmental factors involved should be processed more rigorously. The spatial regression model of this study still has the potential to be optimized. A time series model with more samples should be incorporated, which will contribute to a more comprehensive understanding of public health affected by the urban environment.
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Figure 4.4 Physical Exercise Related Infrastructures Distribution
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4.2 PLANNING IMPLICATIONS (1) Improve accessibility to fresh and healthy food in low-income communities: Food accessibility plays an important role in the health of people living in low-income boroughs. Compared to high-income communities, low-income communities are more vulnerable to food insecurity. Therefore, promoting zoning and financial incentives to attract and retain grocery stores in underserved communities should be targeted. Food Retail Expansion to Support Health (FRESH) program provides and maintains grocery stores in underserved areas across NYC (NYCEDC), helping to reduce disparities in access to healthy, fresh and affordable food. (2) Provide sufficient healthcare facilities: The current distribution of healthcare facilities is uneven, and due to some other more fundamental environmental issues, the influence of the distribution of healthcare facilities does not show a significant correlation with health conditions in low-income communities. However, referring to high-income communities, healthcare facilities are necessary to meet the higher demands of residents. Therefore, appropriate distributions of healthcare facilities to meet residents' needs are necessary and help to reduce the existing health inequality gap in New York. (3) Encourage physical exercise by improving facilities of active mobility: First, authorities should expand bicycle lanes, especially for communities located in the periphery. According to DOT (2016), since the expansion and improvement of the onstreet bike network in 2010, there has been a 75% increase in commuting by bicycle in Brooklyn. Therefore, the construction of bicycle lanes can facilitate people yo commute by cycling, further promoting physical health. Second, building a pedestrian-friendly environment in lower-income areas is important for promoting health equity. Walkability is significantly lower in lower-income areas, indicating a diminished quality of life. Pedestrianization is one of the foundations of urban mobility, underpinning connectivity between people and parks, food markets and health services. (4) Promote green infrastructure for better urban environmental quality: Strategies to promote green infrastructure would contribute to both physical environmental improvements and physiological enhancement for local people. Green infrastructure with more tree canopy would contribute to better spatial quality for people’s daily recreation and mitigate the negative impacts of rapid urban constructions.
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