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	<title>y.feng</title>
	<link>https://fengyuning.com</link>
	<description>y.feng</description>
	<pubDate>Wed, 26 Jan 2022 16:10:04 +0000</pubDate>
	<generator>https://fengyuning.com</generator>
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		<title>Front page</title>
				
		<link>https://fengyuning.com/Front-page</link>

		<pubDate>Mon, 03 Jan 2022 20:59:29 +0000</pubDate>

		<dc:creator>y.feng</dc:creator>

		<guid isPermaLink="true">https://fengyuning.com/Front-page</guid>

		<description>The Impact of NYC’s Open Streets Program on Neighborhood Air Quality in Upper Manattan&#38;nbsp;&#60;img width="1854" height="1503" width_o="1854" height_o="1503" data-src="https://freight.cargo.site/t/original/i/0f8ca9aa42836a3d2a55bc802671fa70fb3546350ebbd206ba5d67081dbdd23a/1005.gif" data-mid="128984259" border="0"  src="https://freight.cargo.site/w/1000/i/0f8ca9aa42836a3d2a55bc802671fa70fb3546350ebbd206ba5d67081dbdd23a/1005.gif" /&#62;


Access to Healthy Food in New York City



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Identifying Community Needs in New York City in the Context of Environmental Justice




&#60;img width="3400" height="2200" width_o="3400" height_o="2200" data-src="https://freight.cargo.site/t/original/i/f339cbc76b8517e5539334df4e4a96aaa74472b335530f3537f4afee1a61bac8/GreytoGreen_FinalReport-19.png" data-mid="131427213" border="0"  src="https://freight.cargo.site/w/1000/i/f339cbc76b8517e5539334df4e4a96aaa74472b335530f3537f4afee1a61bac8/GreytoGreen_FinalReport-19.png" /&#62;
Air Pollution in Sub-Saharan African (SSA) Countries&#38;nbsp;







&#60;img width="3400" height="2200" width_o="3400" height_o="2200" data-src="https://freight.cargo.site/t/original/i/cb94d600a0020c920424ba60206fa2d460f66079ffc63fbb9f5b0d00e7de7d42/Country-Profile_Air-Quality_Spread_Page_1.png" data-mid="131427220" border="0"  src="https://freight.cargo.site/w/1000/i/cb94d600a0020c920424ba60206fa2d460f66079ffc63fbb9f5b0d00e7de7d42/Country-Profile_Air-Quality_Spread_Page_1.png" /&#62;
Modeling Open Space Accessibility in New York City





&#60;img width="3300" height="2550" width_o="3300" height_o="2550" data-src="https://freight.cargo.site/t/original/i/a753cc7e4be2034105025cc44e758443044f164580b40afa42615b42cfbc3f1e/Layout.jpg" data-mid="131427228" border="0"  src="https://freight.cargo.site/w/1000/i/a753cc7e4be2034105025cc44e758443044f164580b40afa42615b42cfbc3f1e/Layout.jpg" /&#62;
Citizen Science: Urban Sensor and Urban Data Creation

 

&#60;img width="2200" height="1700" width_o="2200" height_o="1700" data-src="https://freight.cargo.site/t/original/i/0b08717c64471f45b2f21c974aa09e7027b0816d9154db2e98aa8257b3c61b00/Final-report_Yfeng_Page_11.png" data-mid="131427254" border="0"  src="https://freight.cargo.site/w/1000/i/0b08717c64471f45b2f21c974aa09e7027b0816d9154db2e98aa8257b3c61b00/Final-report_Yfeng_Page_11.png" /&#62;



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	<item>
		<title>01. Thesis:NYC Open Streets Air Quality</title>
				
		<link>https://fengyuning.com/01-Thesis-NYC-Open-Streets-Air-Quality</link>

		<pubDate>Wed, 26 Jan 2022 16:10:04 +0000</pubDate>

		<dc:creator>y.feng</dc:creator>

		<guid isPermaLink="true">https://fengyuning.com/01-Thesis-NYC-Open-Streets-Air-Quality</guid>

		<description>01.

 Thesis: NYC’s Open Streets Program and Air Quality

	2021, Urban Planning &#38;amp; Public Heath
	Adivised by 
Boyeong Hong &#38;amp; Weiping Wu
	Tools: ArcGIS, R, Python
Numerous conversations have been generated around the Open Streets program
in New York City regarding its economic and public health benefits ever since the
program started in Summer 2020. The program created additional restaurants sittings and school spaces by closing streets to traffic. The opportunity allows a wide range of programs that have supported economic development and activated open spaces in communities. This study takes this opportunity to collect street-level data with high temporal resolution using a combination of black carbon sensor and noise sensor to study the effect of a street closure event on local black carbon concentration. A set of factors is considered as potential sources for either emitting black carbon or mitigating black carbon exposures. Moreover, this study
uses the Generalized Additive Model and penalized splines to account for unknown
effects from meteorological variables and street canyons. Results from this study suggest a measurable decrease in black carbon concentration on the streets with the Open Streets segment when compared to their parallel streets without the program during Open Streets program hours, during the study period of March to October 2021.

Research Questions The main research question -- “How does the Open Streets program impact black carbon concentration in Upper Manhattan?” is parsed into multiple quantifiable steps:&#38;nbsp;What is the baseline black carbon (BC) concentration on streets in the selected study areas in Upper Manhattan? 

What is the difference in BC concentration on streets with an active Open Streets segment compared to that of parallel streets without?

What is the difference in BC concentration on a street segment with an active Open Street compared to the same segment while the Open Street is not active? 

What is the difference in BC concentration on a street segment without Open Streets program while a parallel streets with Open Street is active compared to the same segment while the parallel Open Street is not active?

How do other variables correlate to the BC measurement (i.e. weather, traffic, road types, and road activities)?

To answer these questions, the study used black carbon concentration and traffic noise data collected on Open Streets using microAeth® AE51 (AE51) and a noise sensor. The noise sensor was designed and assemble by Luc Dekoninck, a noise scientist who have used same equipment for his previous studies. Black carbon data and traffic noise data was collected during 30 biking trips on designated streets from June to October in 2021. Segments from five parallel streets were included in the study: Broadway Avenue, Amsterdam Avenue, Columbus Avenue, Manhattan Avenue, and the Eighth Avenue which contains partial Central Park West and Frederick Douglass Boulevard. Using this original data, instantaneous black carbon pollution models were produced. Results from the model helped compare black carbon concentration on Open Streets and non-Open Streets, and therefore allowing us understand the scale of environmental impact of the Open Street program with quantitative evidence. Its hyper-local level can also facilitate policymaker’s future emission reduction strategies.



Data Cleaning (Tools: Python and R)
 


A total of 30 trips were made from March 6th to October 30th in 2021. 27 out of 30 trips had complete black carbon data. Only trips with complete data were being used for data analysis.  9829 black carbon data points were ultimately kept in the dataframe with a mean of 1336 ug/m3 and standard deviation of 9084.8 ug/m3, ranging from -48706 ug/m3 to 626792 ug/m3. This raw data contains a number of negative BC values recorded by AE51. While the official website of AethLAB indicated that negative readings are not necessarily a concern and should be treated as noise, it is still possible that some of these values are related to the settings and operation of the instrument. According to the AethLAB website, lowering the flow rate or timebase could potentially reduce noise ratio. This method is not applicable to this research since data has to be collected at a high flow rate and timebase to ensure its high temporal resolution. 

An investigation of these negative values was conducted. Figure 10 shows the percentage of negative values produced in each trip. These values were not distributed equally -- some trips, such as those taken on 06/26, 10/06, 10/09, and 10/14 had about 30% of data that were negative compared to an average of 13.8%. Arbitrarily dropping or replacing these values would be detrimental to the dataset. To further understand why certain days had more negative values than other days, a time series plot and a map of where data was collected were compared. 




&#60;img src="https://lh5.googleusercontent.com/wOxvbi_qWmqTxCrAdpHw4evpE9patvM6yciVxhbtjyKgO47fIUdjpp6uMiLLDCRFNBqsZI9xBN7eHjyfgl0H9XQ3Fm09xMU83QBRHGxNcfd77o-sW43VVQgGleWAzNVeB7bgb5JL" width="605" height="404" style="width: 605px; height: 404px;"&#62;


Figure 10. Percentage of negative values produced in each trip.




&#60;img src="https://lh6.googleusercontent.com/J7e5MZwC6Ejh0swn92Nc_ua6biPXi5jXksqpx7DSKDaAFjFF-9NZQwE_xi2xf2W0e6XOeWjj5KE1Hd-J6jYlVFpxw3HW3NMtxwO6vR2Undx5cpRPBvmOCdatVQfsyJoTH1z82dwX" width="465" height="378" style="width: 465px; height: 378px;"&#62;







&#60;img src="https://lh4.googleusercontent.com/D_2UWtIsFZDjNuUzvGTlv_JqEc-zKMyXEHaty7WUUEF_ZH7U_pKf_mUxZY8INlsi5KJyaHMwSwQvtCe0oRUpPR8tg-sBSEfaj4-T9uFTUQP6TIy7SqHgC-F8h5_pNNz14lJZpvdO" width="465" height="404" style="width: 465px; height: 404px;"&#62;




Figure 11. Examples of two typical trips and their BC value recorded. Negative BC values (shown in blue) were often found at the beginning and evenly recorded during the trip.



Figure 11 is an examples route and black carbon data points collected from two trips. By comparing all 27 trips, the researcher found all negative BC values could be categorized into two types by their potential causes -- negative values due to device calibration, and negative values that are noise. A total of 16 extreme negative values that are smaller than -10,000 ug/ml were found in both cases. Considering their ratio to total data is small but can still have significant influence over the mean, they are dropped from the dataset. For negative values produced during device calibration, they are replaced with the lowest value recorded in that trip. Same treatment was given to negative values distributed during the trip. After dropping and replacing negative BC values, the statistical summary found the data range had changed to 1 ug/m3 to 626792 ug/m3 with a mean of 1554 ug/m3  and standard deviation of  8977.65 ug/m3. 

The standard deviation is abnormally high, indicating more investigations are needed to detect outliers. Anomaly detection was used in this step to detect data points that are deviating from the dataset's usual behavior (“Detect Anomalies in Time Series Using Anomalize Package In R,” 2020). A total of 268 points were detected using the default setting and by comparing averages among all trips. Using a different method, by comparing values within each trip, a total of 181 anomalies were detected. Since preserving each trip’s characteristics is important to the study, the later method was used and those 181 anomalies were deleted from the dataset. This treatment effectively smoothed data and allowed better interpretation of BC trend for each trip (Figure 12 and 13). 




&#60;img src="https://lh5.googleusercontent.com/03u0nTOe4-WYIFQ1Nq21BTkc8zuBlXIOjCz725MxieSXS3_YAlilF52VllBObGEPnldfCshBnDgSNCF8SyG5R1WdncJiKqFM85MPJS2FytIxYAS37li3M2AyJ7r_4J6MJGb0_xeP" width="655" height="301" style="width: 655px; height: 301px;"&#62;




Figure 12. Time Series of black carbon values before detecting and treating the anomalies





&#60;img src="https://lh5.googleusercontent.com/EK8g54HLaCsR40GKxZ1plNmS2m8sybvG_X1xM_MnMQvZbwOCMr9rGYxpFVGardPT9Eb9QoBryk_o-C1zOCrvxiJ03sF7AlJ6z4IrwdBeKfFNTavVkI5YU0_h7nMicl6GTThgdL9l" width="658" height="352" style="width: 658px; height: 352px;"&#62;


Figure 13. Time Series of black carbon values after detecting and treating the anomalies 






Exploratory Data Analysis





Black carbon dataset is not normally distributed, therefore, a log transformation using natural base was performed to ensure all black carbon values can be compared within the same scale. The distribution of black carbon data before and after log transformation is seen in Figure 14 and Figure 15.&#38;nbsp;
	&#60;img src="https://lh5.googleusercontent.com/nbUz1XSVkTcXJt6xH9IgYuGp-6DzUZhsZ1y4BYRQTWELICxA_K3qlZuZcK2GeJvLGMONkQm9_Ahw0cACoL1KDnUcAnbwI61sktVkAf2ojXPKIwPI8k-Cmrxi37OQUFbGGFB5Qe2m" width="298" height="180" style="width: 298px; height: 180px;"&#62;

Figure 14. The distribution of black carbon values in a histogram

	&#60;img src="https://lh3.googleusercontent.com/bz-s0yROZi-rrNNnWGd3W-7HKhXnSzRZ1E0D6ik4a82JEy34UZbsZr4HHCaP_FZY_Mj32Br6eq4YMnRYQOaCTo8hRtf3uf5tA6qHLA9409MXW23CmwPIUJdlXxgvX6uFJicb-YY6" width="298" height="181" style="width: 298px; height: 181px;"&#62;
Figure 15. The distribution of black carbon value after natural log transformations in a histogram







The daily distribution of Logarithmic transformed black carbon values was produced (Figure 16).

&#60;img src="https://lh5.googleusercontent.com/a3KLRYf6YSY_Xsuu5nERv21FCw8SW0ImYp-WJKoz5bk_YoNjvwFC7X0UTmvzPsVB-knxF1AFcfXoM8h2Ay66AKEDW8ZhTHz0usCzjd03jnrsnFmqwowoPkQG7CEiFvtFA6cRI9Pj" width="625" height="346" style="width: 625px; height: 346px;"&#62;


 











Figure 16. Distribution of black carbon value (after being log transformed) in each trip.

CollinearityTo test Multicollinearity,
the “mctext” package in R was used. An individual diagnostic checking for multicollinearity was performed, and Farrar-Glauber test (F-test) was performed on each of these variables to check if their collinearity is significant. The diagnostic outpu from the collinearity test include: Variance Inflation Factor (VIF), Tolerance (TOL) and Farrar-Glauber F-test (Wi). As expected, the “Temperature” variable had the highest VIF, followed by “Dew Point ''. To test whether the correlation between each variable is significant, a t-test was performed for each correlation coefficient. The results are aligned with the observation, the high partial correlation between temperature and other weather variables is very high, except with dew point, followed by wind direction. negative correlations between wind speed and dew point, and between wind direction and dew point were also observed. 
Variables were removed to test collinearity among variables that are left. Starting with the variable with the highest VIF, “Temperature” and “Dewpoint” were removed. After that, collinearity was no longer detectable among “Wind Speed”, “Wind Direction” and “Street Canyon”. 


&#38;nbsp;

&#60;img src="https://lh4.googleusercontent.com/SJvy3ofA7p1gsOTUEfDqXGSsolQrTDywaNz9tdFIsEDcG0gRDBxASKedCzRqbrkIvvfhAZho0DTnBtRkZ-ZfxbqV0xPJ--7GtSbE7t-PjxeODtOsQaS93b3N5d1zE48GHkC_IsYP" width="608" height="373" style="width: 608px; height: 373px;"&#62;





Figure 20. The correlation matrix of meteorological variables and Street Canyon Index.
Note: The plot shows that temperature and dewpoint are highly correlated. Some correlations are also present between temperature and wind Speed, and temperature and wind direction.&#38;nbsp; No evidence of correlation is observed between Street Canyon Index and any weather variables.





Results&#38;nbsp;
“

What is the difference in BC concentration on streets with an active Open Streets segment compared to that of parallel streets without?”
	
&#60;img width="408px;" height="529px;" src="https://lh4.googleusercontent.com/S4DS6gX2v1XjRCrAsGbbw_DfLVmfExDsE6njcVV66EIww1Z3CJw7S1ZGCpaBv5WLeh1ve2zuLx2ChNJ9lgtGJYHHgRQX4Gq9y7OdhTS1MYEcSi8KvuDIoOVK0cghmDh6Hk7UShwGTbn4" style="width: 348.24px; height: 451.518px;"&#62;
	Finding 1: When Open Streets program is active, overall Black Carbon (BC) concentration increased in the study area.
*Open Streets segment is highlighted in blue
 Finding 2: When Open Streets program was active and when compared to BC on Amsterdam Avenue, a decreased BC on Broadway Avenue, Columbus Avenue, and Manhattan Avenue was observed. At the same time, an increased BC on Frederick Douglass Blouvard and Central Park West was observed. &#38;nbsp;












“What is the difference in BC concentration on a street segment with an active Open Street compared to the same segment while the Open Street is not active?”
	

&#60;img width="417px;" height="540px;" src="https://lh4.googleusercontent.com/pisjQocs0b6PLif2GbRittr0sBH6gJd6MZKATB2nHjV5nsZu24xt_bfIn31hAAXs4BEsQtQU0XrHYRvuYLFfkmKs7OMzC9_nB48JQeocrAj35vzRK5LN8p0kAWwS6rIYw5r0giASYKJv" style="width: 348.24px; height: 450.959px;"&#62;


	

Finding 3: In a subset of data that only contained data collected on Amsterdam Avenue and Columbus Avenue, streets with Open Streets program segment, when Open Streets program was active, the overall Black Carbon (BC) concentration increased.








“What is the difference in BC concentration on a street segment not part of the Open Street program while a parallel Open Street is active compared to the same segment while the parallel Open Street is not active?”
	


	

&#60;img width="414px;" height="540px;" src="https://lh3.googleusercontent.com/hjxkI2kbMWmiF0Ge8PMEN5CMCFcwbGcVEYiN6tr_cZxo7C88u2jq0b9HgFLBcrTRRNyuN75a-ZPn-jdfD1-buTtDeNa4uQollQC8N8PKsjl-ZA1r_Ahm1V4A2pO2cDddSrWrKO84l1vQ" style="width: 348.24px; height: 454.227px;"&#62;


	

Finding 4: In a subset of data that only contained data collected on Broadway, Manhattan Avenue, 8th Avenue(Central Park West &#38;amp; Frederick Douglass Avenue), streets without Open Streets program segment, when Open Streets program was active, the overall Black Carbon (BC) concentration increased.

However, when looking at each street segment, a decreased BC concentration was&#38;nbsp; found on Broadway and increase BC concentration was found on Manhattan Avenue and 8th Avenue.






“How do other variables correlate to the BC measurement (i.e. weather, traffic, road types, road activities)?”




	

&#60;img width="408px;" height="529px;" src="https://lh4.googleusercontent.com/S4DS6gX2v1XjRCrAsGbbw_DfLVmfExDsE6njcVV66EIww1Z3CJw7S1ZGCpaBv5WLeh1ve2zuLx2ChNJ9lgtGJYHHgRQX4Gq9y7OdhTS1MYEcSi8KvuDIoOVK0cghmDh6Hk7UShwGTbn4" style="width: 348.24px; height: 451.518px;"&#62;


	Finding 5: After controlling for a set of environmental variables, the final model suggested an increased BC concentration on parallel streets that without Open Streets segment during Open Streets program hours. In another word, during Open Streets program hours, BC concentration on Amsterdam Avenue and Columbus Avenue decresed but that of on Broadway, Manhattan Avenue, and 8th Avenue increased.&#38;nbsp;




Limitations and Indications for Future Studies




There are limitations in the study. First, the collinearity between variables is not fully understood and therefore, requires further studies. The selected Open Street program for this study is only on Fridays and weekends. The traveling behaviors differ on weekends then on weekdays. It is difficult to get a result that is fully clean from the weekend effect. For future studies, more data on an Open Streets program that operates during weekdays need to be collected. There are also Open Streets programs that are not Open Restaurants, such as Play Streets program—the host streets are closed to traffic to provide extra space for school activates.&#38;nbsp; Having more data from different types of Open Streets program will be important in order to have a broader sense of understanding of the impact of streets closure events on black carbon concentration regardless of the time and location of the events. 

Second, the binary way of including a variable limits the model’s ability to fully reflect a variable effect on black carbon concentration. In the data, park and restaurants variables are included as binary variables. The “binary” means, the data only reflects whether there is a park or restaurant within 50 meters search radius of a black carbon data point. The variable is limited by the search radius defined in the analysis, which does not fully reflect the more transient impact these variables may have on black carbon concentration in the neighborhood. Similarly, the restaurant variable also limited because the not all restaurants emit the same amount of black carbon. Some restaurants are more likely to have outdoor barbeques than others, but the restaurant type was not considered. Future research can improve the limitation of binary variables by using distance as the indicator. Instead of a research radius, the future research can measure the distance to the nearest restaurant and nearest park. The distance will be included as the variable—a more nuanced way of account for their effects. The restaurant types may also be included in the dataset. 

Third, the temporal effect on black carbon concentration is not fully accounted. The analysis included “before 5pm/after 5pm” and as a temporal variable; however, this binary variable cannot fully reflect and control the temporal effect. Forth, when comparing black carbon concentration on different streets, the analysis used one of the street as the constant and compared it to every other streets. It may not have been the most accurate way to present black carbon effect if the comparison is done among the streets. For future research, it’s possible to include use splines for time. Deciding the intervals that would be used for aggregation would be important. Future analysis should consider a time interval that preserves a meaningful level temporal resolution, and then, consider using the spline for the aggregated time variable, which is an easy and more nuanced way of accounting for temporal resolution and maintaining flexibility. 

Overall, while the data collected has high spatial and temporal resolutions, the analysis conducted in the scope of this research may not be enough to detect all subtle differences in black carbon concentration in the study area. Data aggregation and grouping processes may weaken some of the spatial and temporal effects. As an example, aggregating data using 5pm as the cut-off will overlook the change happening before 5pm and after 5pm.&#38;nbsp; For future analysis, the model can be benefited from introducing time variables through a more sensitive method. 

Still, the results from this study play a role for setting up future hyper local and hyper temporal air quality studies. Using a combination of sensors is an innovative method which gives the flexibility and enables high spatial and temporal resolution data to be examined at the same time. The highlighted temporal and spatial effect on black carbon concentration proves the potentiality for using low-cost innovative data collecting methods for air quality studies. 

Significance 




This study contributes to the existing body of air quality literature in a few ways. Few studies used dynamic sensing to capture temporal and spatial effects on air quality. Most air quality studies are constrained by air quality data with low time resolution and siting options. The approach used in this project proves the feasibility of using combined sensing techniques and citizen air monitoring. The hyper local and hyper temporal air quality data generated in this project allows for greater flexibility in data analysis. Hence more air quality research questions regarding locality and temporality could be asked and studied using this set of data. Its spatiality allows a number of built environment factors to be studied as well. While it is known that the built environment has a tremendous impact on air quality, their interactions and the dynamic emission patterns existing between them and traffic patterns are rarely studied. It is important for future research to investigate such relationships. 

The main results from this project are important because they prove a traffic pattern during a street closure event. A measurable difference was observed when comparing streets with Open Streets segments and parallel streets without active Open Streets hours. Even if the difference is small, it may still reflect local experiences during a street closure event. The data was collected through biking; therefore, the results reflect what a biker’s experience is like in the study area and the subsequent black carbon exposure a biker may have had. 

The observed traffic pattern changes and their overall effect on black carbon concentration suggest that current traffic policies related to air quality should also be expanded to include more events like the Open Streets program. Further works should assess the interaction between traffic patterns and built environment, as well as their effect on black carbon. Though the air quality improvement due to the Open Streets program was not found in this study, it is important to keep recognizing the program’s value in other aspects, such as providing open spaces and economic opportunities. While improving air quality was not what this program was designed for, its influence on traffic, traffic related emissions, emission exposures and its subsequent effect on non-traffic related emission should not be overlooked.


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	<item>
		<title>02. Access to Healthy Food in New York City</title>
				
		<link>https://fengyuning.com/02-Access-to-Healthy-Food-in-New-York-City</link>

		<pubDate>Tue, 07 Sep 2021 20:34:58 +0000</pubDate>

		<dc:creator>y.feng</dc:creator>

		<guid isPermaLink="true">https://fengyuning.com/02-Access-to-Healthy-Food-in-New-York-City</guid>

		<description>02.

 Access to Healthy Food in New York City

	2019, Urban Planning &#38;nbsp; &#38;nbsp; &#38;nbsp;&#38;nbsp;
	With Elaine Hsieh and Priska Marianne&#38;nbsp;
	Tools: ArcGIS, Illustrator, Indesign
&#38;nbsp;While New York City’s economic growth continues to outpace the United States, 1.2 million of the city’s residents are food insecure, including 18 percent of all children and 10.9 percent of all seniors (Hunger Free America, 2018).&#38;nbsp; 
Food security is defined as having access to adequate food for all household members at all times for an active, healthy life (USDA, 2017). Food insecurity also increases the risk of obesity and diabetes (Gretchen Van Wye et al., 2008). More than half of adult New Yorkers are either overweight or obese. Depending on the neighborhood’s socioeconomic and geographic character, racial/ethnic make-ups, and sizes and types of immigrants, the negative health outcomes in some neighborhoods are more prevalent than in the others (Gretchen Van Wye et al., 2008).One way that the government has been addressing food insecurity issue in New York City is through identifying food deserts  or areas lacking fresh fruit, vegetables, and other options for whole foods, defined by the American Nutrition Association, are often found in low-income neighborhoods (Gordon et al., 2011).
NYC Department of City Planning initiated the creation of the Food Retail Expansion to Support Health (FRESH) program in 2009 to incentivize supermarket development through zoning requirements and financial benefits. 
The objective of this study is to understand the effectiveness of the FRESH program.&#38;nbsp;

Our investigation includes questioning the effectiveness of FRESH zone districts, identifying communities that are vulnerable to food insecurity, and assessing geographic access to supermarkets in vulnerable areas.


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&#60;img width="3400" height="2200" width_o="3400" height_o="2200" data-src="https://freight.cargo.site/t/original/i/f65d0441c4f3570b973be023338cfd2169a312efd3e5c0c41f9790e397ebac19/FENG_HSIEH_MARIANNE_FinalDeliverable-05.png" data-mid="118339059" border="0"  src="https://freight.cargo.site/w/1000/i/f65d0441c4f3570b973be023338cfd2169a312efd3e5c0c41f9790e397ebac19/FENG_HSIEH_MARIANNE_FinalDeliverable-05.png" /&#62;
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&#60;img width="3400" height="2200" width_o="3400" height_o="2200" data-src="https://freight.cargo.site/t/original/i/c48e23b60054a6c1faaffc769bae9b3645f5f654319e3b99cb58e59c7d433aef/FENG_HSIEH_MARIANNE_FinalDeliverable-07.png" data-mid="118339061" border="0"  src="https://freight.cargo.site/w/1000/i/c48e23b60054a6c1faaffc769bae9b3645f5f654319e3b99cb58e59c7d433aef/FENG_HSIEH_MARIANNE_FinalDeliverable-07.png" /&#62;
&#60;img width="3400" height="2200" width_o="3400" height_o="2200" data-src="https://freight.cargo.site/t/original/i/d0107ec321fd1e3c94ce17035ef323267d7e3cc773cd16a347d988810163d64a/FENG_HSIEH_MARIANNE_FinalDeliverable-08.png" data-mid="118339062" border="0"  src="https://freight.cargo.site/w/1000/i/d0107ec321fd1e3c94ce17035ef323267d7e3cc773cd16a347d988810163d64a/FENG_HSIEH_MARIANNE_FinalDeliverable-08.png" /&#62;
&#60;img width="3400" height="2200" width_o="3400" height_o="2200" data-src="https://freight.cargo.site/t/original/i/cff8111742956cfc63ef40e6f524e8c921dbf52adb6063b79eaa78661e0d9eae/FENG_HSIEH_MARIANNE_FinalDeliverable-09.png" data-mid="118339063" border="0"  src="https://freight.cargo.site/w/1000/i/cff8111742956cfc63ef40e6f524e8c921dbf52adb6063b79eaa78661e0d9eae/FENG_HSIEH_MARIANNE_FinalDeliverable-09.png" /&#62;
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&#60;img width="3400" height="2200" width_o="3400" height_o="2200" data-src="https://freight.cargo.site/t/original/i/c578f1bee2942d30d8e14c82953dd40296f8aba2b0986b358c26b0f5fbc7f985/FENG_HSIEH_MARIANNE_FinalDeliverable-12.png" data-mid="118339066" border="0"  src="https://freight.cargo.site/w/1000/i/c578f1bee2942d30d8e14c82953dd40296f8aba2b0986b358c26b0f5fbc7f985/FENG_HSIEH_MARIANNE_FinalDeliverable-12.png" /&#62;
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</description>
		
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	<item>
		<title>03. Identifying Community Needs in New York City</title>
				
		<link>https://fengyuning.com/03-Identifying-Community-Needs-in-New-York-City</link>

		<pubDate>Tue, 07 Sep 2021 20:34:58 +0000</pubDate>

		<dc:creator>y.feng</dc:creator>

		<guid isPermaLink="true">https://fengyuning.com/03-Identifying-Community-Needs-in-New-York-City</guid>

		<description>03.
Identifying Community Needs in New York City

	2020, Urban Planning &#38;nbsp; &#38;nbsp; &#38;nbsp; 
	With Riley Burchell and Geon Woo Lee
	Tools: QGIS, Tableau, InDesigh
In July 2019, New York State passed the Climate Leadership and Community Protection Act (CLCPA), a legally binding commitment to reduce greenhouse gas emissions and transition to renewable sources of electricity. In addition to these ambitious targets, the CLCPA highlights the protection of historically disadvantaged communities, defined as those that bear the burdens of environmental pollution and socioeconomic conditions or those that have experienced discrimination on the basis of race or ethnicity.



As part of the 
 Grey-to-Green Energy Transition Spring 2020 Studio, we looked for ways to connect with local residents and community based organizations. Unfortunately, as cicumstances in the city began to develop pertaining to the COVID-19 response, it became extremly difficult to collborate with organizations and individuals. Yet, we still looked for innovative ways to capture community needs in the context of the studio outcomes.&#38;nbsp;

Asthma Related Emergency Department Visits and Air Quality Complaints
In mapping the count of 311 air quality complaints per community district against asthma hospitalization and emergency department data, we saw that the areas with the highest numbers of hospitals and emergency department visits due to asthma had some of the fewest air quality complaints, while areas with relatively low hospitalization and emergency department visit rates and the largest number of air quality complaints.&#38;nbsp;
&#60;img width="4939" height="2021" width_o="4939" height_o="2021" data-src="https://freight.cargo.site/t/original/i/8148762af2411ba2d55f10017257c2f4f088050db3edf8edf9bebcee69b438a4/GreytoGreen_FinalReport_Page_19-01.png" data-mid="118386682" border="0"  src="https://freight.cargo.site/w/1000/i/8148762af2411ba2d55f10017257c2f4f088050db3edf8edf9bebcee69b438a4/GreytoGreen_FinalReport_Page_19-01.png" /&#62;
311 Data Limitation: Without being able to conduct interviews with residents and local organizations, it is difficult to infer meaning from these data.  However, in looking at available information about how people file complaints (online, by phone, using the 311 app) and length of complaint ticket life (average amount of time it takes each city agency to register and resolve a 311 complaint), we speculate that these trends could be indicative of (1) mistrust of/reticence to engage with systems (2) not viewing 311 as a pathway to change in local conditions (3) inaccessibility of the 311 platform in terms of access and ease of use.



Ultimately, while 311 data may not be able to capture the entire story of air quality in the five boroughs, it does serve to corroborate resident and stakeholder accounts of some human impacts of on the ground conditions
Demographics&#38;nbsp;&#60;img width="5090" height="2895" width_o="5090" height_o="2895" data-src="https://freight.cargo.site/t/original/i/a97465d4cfdeccf95668de0738c0f671daab3b38476bc52a8ddb01f1bed46ede/GreytoGreen_FinalReport_Page_17-2.png" data-mid="118393811" border="0"  src="https://freight.cargo.site/w/1000/i/a97465d4cfdeccf95668de0738c0f671daab3b38476bc52a8ddb01f1bed46ede/GreytoGreen_FinalReport_Page_17-2.png" /&#62;

District Needs Statement&#38;nbsp;
&#60;img width="5090" height="2667" width_o="5090" height_o="2667" data-src="https://freight.cargo.site/t/original/i/5333d8a8955b344adbbd09f6e3f5fc89705ef8928af83d4525568b8cf8c2f722/GreytoGreen_FinalReport_Page_18-2.png" data-mid="118393870" border="0"  src="https://freight.cargo.site/w/1000/i/5333d8a8955b344adbbd09f6e3f5fc89705ef8928af83d4525568b8cf8c2f722/GreytoGreen_FinalReport_Page_18-2.png" /&#62;</description>
		
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	<item>
		<title>04. Air Pollution in Sub-Saharan African Countries</title>
				
		<link>https://fengyuning.com/04-Air-Pollution-in-Sub-Saharan-African-Countries</link>

		<pubDate>Tue, 07 Sep 2021 21:37:09 +0000</pubDate>

		<dc:creator>y.feng</dc:creator>

		<guid isPermaLink="true">https://fengyuning.com/04-Air-Pollution-in-Sub-Saharan-African-Countries</guid>

		<description>04.Air Pollution in Sub-Saharan African (SSA) Countries&#38;nbsp;


	2021, Public Health&#38;nbsp; &#38;nbsp; &#38;nbsp;&#38;nbsp;
	With Saoimanu Sope
	Tools: QGIS, Tableau, InDesigh


Sub-Saharan Africa (SSA) can be characterized by its rapid population growth, wealth in natural resources, or the many health challenges that the region faces including that of environmental health risks. Among the major causes of death attributable to environmental health risks,the total number of deaths due to air pollution increased by 60 percent betweenthe years 1990 and 2017.In 2019 alone, 400,000 deaths were due to household air pollution and 250,000 deaths were due to ambient air pollution. This
reality persists today and for many SSA countries, healthcare infrastructure to support such risksis either lacking, inaccessible, or non-existent.
Achieving these objectives will allow the
research team to determine a country’s need for immediate intervention addressing the burden of
disease. Accordingly, the framework used and the recommendations made will be considered by
the global public health organization, Vital Strategies, to stimulate future projects addressing air pollution in the SSA region.
A Data-Driven Approach&#38;nbsp;
A number of indicators were studied using Tableau.&#38;nbsp;
Health Outcome Indicators:&#38;nbsp;
- Cardiovascular Disease- Chronic Obstructive Pulmonary Disease (COPD),-Lower Respiratory
Infection.
Operational Feasibility and Contextual Indicators

- External Health Expenditure - Domestic general government health expenditure per capita&#38;nbsp; in United States (U.S.) dollars ($).
- Population size - Percent of the population with access to clean energy and technology for cooking- Internal and external conflict.&#38;nbsp;
Deliverables - Country Profiles
Using the approach discussed above, three countries: Ghana, Ethiopia, and Madagascar were selected. As part of deliverables to Vital Strategies, profiles of these countries were created to provide a snapshot of airquality, health, and air quality management situation at the national level. The profiles are designed communicate with local policymakers, with whom Vital Strategies will engage to direct atention to air quality management.&#38;nbsp;

&#60;img width="3400" height="2200" width_o="3400" height_o="2200" data-src="https://freight.cargo.site/t/original/i/ced4876668717687261ca45e13d962f8e0d574007397f4f28a1cd645b4155de9/Country-Profile_Air-Quality_Spread_Page_2.png" data-mid="118399951" border="0"  src="https://freight.cargo.site/w/1000/i/ced4876668717687261ca45e13d962f8e0d574007397f4f28a1cd645b4155de9/Country-Profile_Air-Quality_Spread_Page_2.png" /&#62;
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</description>
		
	</item>
		
		
	<item>
		<title>05. Access to Greenspace in New York City</title>
				
		<link>https://fengyuning.com/05-Access-to-Greenspace-in-New-York-City</link>

		<pubDate>Tue, 07 Sep 2021 21:38:38 +0000</pubDate>

		<dc:creator>y.feng</dc:creator>

		<guid isPermaLink="true">https://fengyuning.com/05-Access-to-Greenspace-in-New-York-City</guid>

		<description>05.

















Modeling



Open Space Accessibility in New York City






	2021, Public Health&#38;nbsp; &#38;nbsp; &#38;nbsp; 
	


	

Tools: QGIS, R




	






















“Access” by definition refers to a
means of approaching or entering a place; to obtain, examine, or retrieve. In
urban planning and public health, the power to define access is also the power
to control access. The intepretation of accessibility and the operationalizationalization of
that definition also impact people’s experience. 

Results from previous studies are inconsistent at demonstrating the association between&#38;nbsp; park distribution and demographic and socioeconomic status variables. 

This project attempts to use a spatial regression model to
capture disparities in park accessibility in New York City. Through the
comparison between spatial and nonspatial accessibility measurements, the
project aims to capture the discrepancy and challenges in the existing methods
for measuring accessibility in public health and urban planning.

Method


Two main steps were taken study new yorker’s accessibility to parks in relationship to people’s socioeconomic status. In the first step, the physical accessibility defined as “the shortest distance” travelled needs to be measured. In the second step,&#38;nbsp; GWR model and spatial regression model were used to access the relationship between shortest distance and SES. Initially, the centroid of residential unit (in building footprint) generated from PLUTO was the preferred smallest analysis unit. However, there are over 764,372 units that qualified for residential. The computational power required to calculate the distance between each of these points to other 4,086 park access points is beyond the capability of computational power available. Census block would be another option. Currently, there are 38,800 census blocks in New York City.&#38;nbsp; Still, the computer available was not able to process this amount of data. Therefore, using the census tract as the smallest residential unit became the next option. To calculate the closest distance to the nearest park for each of the 2,165 census tracts in New York City, a closest facility analysis was performed using QNEAT 3, a QGIS plug-in (Raffler, 2018). To perform the closest facility analysis, start points and end points were provided. The start points were centroids of census tracts. To create end points, simply using the centroid of each park would be misrepresenting since some parks are much larger than the others.&#38;nbsp; For 2031 parks selected, the minimal size is 218.7 square feet and the maximum size is 86,055,448.7 square feet. Moving on, the park access points were defined and created as the intersection between LION Street line and Park outline. As a result, 4,086 access points were created (Figure 2). 



&#60;img src="https://lh4.googleusercontent.com/wtRCk1hDLAAAj33NUspS5HRs-h4yB-P6Bw-EnyCKsKEI2fSw85MuQ-ds7xwNOz5WwIdoFgJtn_8uxeqLRcTfmWX1vbYdnkqGPmItTVTuGO8KhrncbzmkuSgXT3V0JPEXz5SqKpMv" width="523" height="241" style="width: 523px; height: 241px;"&#62;




Figure 1. Methodology for generating shortest distance 



ResultThe closest facility analysis generated 2,175 shortest routes from each census tract to the closest park access point (Figure 2). These routes were matched with their corresponding census tract. The full data frame including shortest distance and selected SES indicators were imported into R to perform the spatial regression analysis. A global model was generated using linear regression. Selected SES indicators from ACS 5 years estimates for the 2019 period were included in the model (Table 1). Each indicator, except individual median income and family median income, was normalized -- number per 1,000 people. Then a geographically weighted regression (GWR) was applied to the same model. To access capital dependency, R2  and standard errors from the GWR model were plotted (Figure 3 and Figure 4). For the spatial regression model, minimum and maximum distances, and distance ranges were calculated. Moren’s I test for each distance weight was calculated and compared. The one with higher Moren’s I was used for the rest of the analysis followed by calculating local Moren’s I using selected spatial weight. Then “lm.LMtests” function was used to access 5 models ( Error dependence, Robust form of Error dependence, lag dependence, robust form of lag dependence and SARMA models) and understand the type of spatial dependence in the model. 




&#60;img src="https://lh6.googleusercontent.com/G1u1M4E7ANOdWg8tSI7VDooRV3JRfF8Y30zfi6n7PoD5gqrtJj7ol9jSmrWhmjFi459GQMGQzhLd023sUk568m-b5P532yovuRdCNN58cwKoRHYeuLebGRu-oa72AhMBNMC1wIIh" width="624.2062615101289" height="441" style="width: 523.01px; height: 369.627px;"&#62;






Figure 2. Shortest distance from census tract centroid to the nearest park
Results
Results from the global model show that out of 11 indicators, excluding total population which was used for normalizing other variables, suggest mostly negative correlations between each variable and the shortest distance. Percent of units lived by owers and percent of family living under the federal poverty line were both positively correlated to the shortest distance. The estimated coefficient for household income was close to zero, indicating zero correlation. 

After performing geographically weighted regression analysis, a spatial dependency of each outcome and each predictor variable was clearly shown on the R2 map (Figure 3). For the spatial regression model, minimum and maximum distance was selected as spatial lag because it yielded a higher Moren’s I ( min-max: 0.198 vs. range: 0.056). The results from local Moren’s I suggest that 17.95% of total census tracts with long distance to the nearest park was surrounded by other high-value census tracts, 24.67% of total census tracts with short distance to the nearest park was surrounded by other low-value census tracts. The rest 57.38% did not have significant spatial weight. 

The results from “lm.LMtests” demonstrated statistical significance for all 5 models, indicating that both lag and error dependencies exist in the model. Therefore, a Durbin model was used to control both dependencies. The result of Durbin model is shown in Table 3. Predicators were statistically significant in the global mode ( percent of the black population, percent of unit lived by its owner, median individual income, and median family income) became statistically insignificant in the Durbin model. Percent of households living under the federal poverty line was insignificant in the global model, but it became statistically significant in the Durbin model. Percent of individuals living under the federal poverty line was negatively correlated to the shortest distance to the park, but it became positively correlated in the Durbin Model.

Overall, some spatial dependencies were found among selected SES; however, most correlations that existed before became insignificant after controlling for spatial dependence. This was true except for one variable -- the owner-occupied housing unit, which demonstrated a positive correlation to the distance to the nearest park in the Durbin model. Figure 5 shows the spatial nonstationarity of this variable. Especially in Staten Island, the percentage of owner-occupied housing units was positively correlated to the distance to the nearest park. This relationship aligned with the current residential type compositions in the city. Staten Island, which has large green space coverage, has more owner-occupied units than the other four boroughs which are mostly occupied by renters. For other SES variables, especially communities of color, the results contradicted the hypothesis. Increased percentage of Black and Hispanic populations were negatively correlated to the distance to the nearest park and the percentage of unemployment and household poverty rate.


 



Shortest distance
from census tract centroid to the nearest park

	&#60;img 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" alt=""&#62;


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from GWR 
	



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" alt=""&#62;

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errors from GWR







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" alt=""&#62;















GWR
result (shortest distance vs. owner-occupied unit)






	

LimitationsA limitation of this study is the consideration of various park sizes and park amenities. The number of parks is not a synonym for the total square footage of parks. Looking at Figure 1, South Bronx has more parks, but their sizes are much smaller. Compared to the sporadic small parks in South Bronx, Upper West Manhattan may have fewer parks, but the size, especially Central Park, is much more significant. In reality, park size is often correlated to park usage. With larger parks having more amenities and hosting more usage, more people would prefer going to larger parks than smaller parks. The other limitation of this study is that the residential unit used for analysis was derived from census tracts. The aggregation of the neighborhood reduced spatial resolution, which made calculated distance to the nearest parkless representative of the actual neighborhood condition.
 






</description>
		
	</item>
		
		
	<item>
		<title>06. Urban Sensor and Urban Data Creation</title>
				
		<link>https://fengyuning.com/06-Urban-Sensor-and-Urban-Data-Creation</link>

		<pubDate>Tue, 07 Sep 2021 21:29:35 +0000</pubDate>

		<dc:creator>y.feng</dc:creator>

		<guid isPermaLink="true">https://fengyuning.com/06-Urban-Sensor-and-Urban-Data-Creation</guid>

		<description>05.&#38;nbsp;

Urban Sensor and Urban Data Creation&#38;nbsp;
	

2020, Urban Planning


	
	

Tools: Arduino, Arduino IDE, R


“Public Life”--people’s daily interactions within the built environment (Gehl 2011) has been transformed by the urban spaces. Commonly-accepted theories in environmental psychology and planning&#38;nbsp; became increasingly limited -- limited by time and space. My project, Playground Pulse, adivised by Dr. Anthony Vanky, uses motion sensors to explore creative ways where sensing technologies facilitate public space and social interactions in the context of a pandemic. In responding to the Covid-19 Pandemic, New York City shut down many public facilities, including public playgrounds in April 2020. Playground Pulse was developed in the mist of the city shut down, aiming to explore issues related to spaital and environemntal justice.&#38;nbsp;

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