ESS 330 Final Project Report

Urbanization, Density and Access to Public Parks in the United States

Abstract

Currently, over 50% of the world’s population is living in urban areas and large cities. Urban planning has a key role in the experiences of residents living in these areas, where efficiency, utilitarianism, and profitability have dominated. With more studies focusing on a connection to nature and time outdoors with positive mental and physical health, we wanted to determine if any changes had been made in the urban planning field with regards to this aspect. We hypothesized that there is a linear relationship between urban population density and public open space availability. To determine this, we used datasets from the UN-Habitat Urban Indicators Database and the ParkServ Database. We formatted these datasets to fit together and work efficiently through using R, conducted an exploratory data analysis to define our variables, and then used a machine learning workflow to explore predicted values for different cities. For our built open space results, we used a boosted tree model, which gave us a 2.7644 RMSE and a 0.0136 R-squared, which provide an insignificant relationship. For our open space access results, we used a linear regression model which gave us an 18.5488 RMSE and a 0.6653 R-squared. Looking at these results, we did not find a significant relationship between urban population density and public open space availability but are interested to see if there is potential to develop this relationship. Urban planners should be shifting their values and goals for cities from efficiency and utilitarianism to an increase in natural open spaces instead.

Introduction

Since 2008, the majority of the world’s population has lived in urban areas, a result of urbanization in developing countries (Beall et al., 2010; Kohlhase, 2013). The United States developed earlier than many nations, with more than 50 percent of the population living in urban areas by by the 14th Census in 1920.

Definition of Urban Areas in Census History

Prior to the 2020 Census urban areas were defined as any area with greater than 2500 people. Following the 2010 census urban clusters described areas with populations greater than 2,500 and less than 50,000; urbanized areas described areas with a population greater than 50,000. For the 2020 Census the threshold was changed to 5000 people (Ratcliffe, 2022).

In the century since the 1920 census the percentage of individuals living in urban areas has increased to 80.7% (Slack & Jensen, 2020). As more people moved to urban areas, those areas expanded forming urbanized areas and large cities.

Urban planning has existed for centuries out of necessity, historically dominated by efficiency and utilitarianism, optimizing the world we live in for profitability, corporate productivity, and automobile-based mobility. This optimization came with sacrifices, which now impact an increasingly large majority of the population. In recent years, the discipline has begun to prioritize human factors over utilitarian efficiency. Thousands of years of living in rural settings makes urban living hard for most people’s biology. Connection to nature and time outdoors even in small amounts has been shown to be a vital part maintaining physical and mental health (Bratman et al. (2012)). In an effort to make urban spaces more livable, planners are turning to parks and natural areas to connect people to nature.

Equity issues aside, overturning and correcting more than a century of bad planning is a daunting task. Many cities filled in and built up over the course of the 20th century as land became a premium commodity. How does this density present significant challenges for today’s planning professionals? This research seeks to investigate the relationship between urban demographics like density, and park access. In exploring this relationship, we hypothesize that there is an intermediate/sublinear relationship between urban population density and public open space availability.

Data Overview

This report uses data from the UN-Habitat Urban Indicators Database and the ParkServe® Database maintained by the Trust for Public Land. The UN data relates to the UN-SDG 11.7.1 pertaining to access to open spaces and green areas. The January 2025 version of the UN Open Spaces and Green Areas data includes the average share of urban areas allocated to streets and open public spaces as well as the share of the urban population with convenient access to an open public space.

UN Definition

In this case, the UN defines “convenient access to an open public space” as the “urban population withing 400 meters walking distance along the street network to an open public space” (May et al., 2000).

These data collected by the UN were collected in 2020 and provided as a .xls format spreadsheet. These data were converted to .csv format with Microsoft Excel. The ParkServe® data selected for use is the 2020 data set to match the year the UN data was recorded. Specifically, this report uses elements of the City Park Facts: Acreage & Park System Highlights. The ParkServe® data is much less synthesized and was available as a .xml file. The file was structured for viewing as a spreadsheet rather than for further analysis and included multiple worksheets withing the workbook. In converting the file to a .csv file, the data spread across multiple worksheets was collated in a single worksheet and converted to a summarized dataset .csv file.

These data are lacking a shared numerical position data type but share a city name column formatted as “city_name, two_letter_state_abbreviation”. There is not perfect overlap between cities with data in each database however, there are 25 cities shared between the datasets. Cities present in only one data set will be culled when the data is joined.

Methods

Firstly, we cleaned the data. This is important for our project because the raw data was downloaded as Excel spreadsheets, but some reformatting in Excel was required to effectively export as a .csv file and import the new summarized file to RStudio. Any remaining data cleaning occurred in R as needed, including header changes and creation of additional columns to reflect relational data. We then conducted exploratory data analysis (EDA) to visualize the data to further understand what we were looking at. Next, we joined the datasets by “city name” to have a complete working dataset. This data was combined into a single data frame, using an inner join, because of the high number of cities in one dataset that the other lacks. The new dataset includes only cities found in both datasets, with columns from both.

Limiting Scope

The cities found in only one dataset were cut from the data to accommodate the limited scope of the project. Unfortunately, this left a very small sample size whcih made drawing definitive conclusions difficult. With a bigger scope it is possible that additional data could be used to understand these patterns with more depth.

As we continued, we prepped the data and split it into training and testing datasets. We started by using the 10-fold cross-validation on the training data, but the dataset was too small, so it was overfitting. Then we tried bootstraps before finally switching to 3-fold cross validation for more representative results. We created a recipe and set up several models in regression mode. Using these models and the recipe, we created a workflow set. We then used the map function over workflow using workflow map. We used the highest performing model to fit the data and augment. We moved on to plotting and graphing the data to visually display our test results. If time allowed and our model was more effective, we would have continued to explore using the model to predict values for cities included in only one document.

Results

Exploratory Data Analysis (EDA)

Variable	Mean	Median	Standard Deviation	1st Quartile (Q1)	3rd Quartile (Q3)
city_pop	1,113,559.56	655,061.00	1,696,534.55	377,963.00	1,006,142.00
designed_park_area	5,559.20	3,864.00	5,020.97	2,652.00	5,785.00
dn_area_ratio	2.42	1.02	4.90	0.26	1.56
land_area	178,285.40	88,800.00	224,466.29	53,723.00	201,635.00
land_per_capita	0.33	0.16	0.70	0.08	0.28
mean_percent_built_open_space	18.71	18.20	3.23	17.20	20.60
mean_percent_open_space_access	42.06	40.00	17.04	29.80	52.80
natural_park_area	48,113.72	4,538.00	180,621.84	1,429.00	22,527.00
park_units	460.80	302.00	811.06	179.00	416.00
park_units_per_10k_pop	4.55	4.54	1.72	3.28	5.42
park_units_per_area	0.00	0.00	0.00	0.00	0.00
park_units_per_capita	0.00	0.00	0.00	0.00	0.00
parkland_area	53,824.36	9,478.00	180,158.96	5,075.00	28,312.00
parkland_per_1k_pop	143.17	12.09	607.26	9.15	28.14
parkland_per_capita	0.14	0.01	0.61	0.01	0.03
parkland_percent	15.31	12.54	15.58	6.76	17.38
percent_designed_parks	44.66	50.28	27.85	20.43	60.90
percent_half_mile_walk	0.76	0.80	0.20	0.63	0.96
percent_natural_parks	53.78	49.61	28.29	32.55	79.57
pop_density	9.52	6.39	9.84	3.59	12.41
pop_near_parks	939,727.24	360,828.80	1,686,304.23	300,312.72	797,287.04
revised_area	175,389.60	87,844.00	222,631.64	52,765.00	196,098.00

City Name	City Population	City Land Area (Revised) (Acres)	Population Density (People/Acre)	City Parkland Area (Acres)	Designed-Natural Park Area Ratio (Designed Park (%) / Natural Park (%)	Percent Parkland	Parkland Per (1000) Capita	Percent of Residents within 0.5 Miles of a Park	pop_near_parks
Anchorage, AK	299,100	1,086,019	0.28	914,138	0.00	84.17	3,056.30	0.75	224,597.2
New Orleans, LA	386,105	107,655	3.59	27,775	0.11	25.80	71.94	0.80	308,698.7
Washington, DC	702,321	38,955	18.03	9,478	1.09	24.33	13.50	0.98	690,170.8
New York, NY	8,627,852	187,946	45.91	40,190	1.01	21.38	4.66	0.99	8,539,502.8
San Diego, CA	1,399,844	205,918	6.80	39,385	0.29	19.13	28.14	0.81	1,127,322.4
Virginia Beach, VA	457,832	159,341	2.87	28,312	0.26	17.77	61.84	0.68	309,073.2
Boston, MA	687,725	29,175	23.57	5,072	1.02	17.38	7.38	1.00	686,280.8
Honolulu, HI	1,006,142	379,885	2.65	57,141	0.09	15.04	56.79	0.79	797,287.0
Minneapolis, MN	421,339	33,958	12.41	5,075	8.47	14.94	12.04	0.98	413,506.3
Jacksonville, FL	925,142	467,298	1.98	67,707	0.14	14.49	73.19	0.35	322,004.9

City Name	City Population	City Land Area (Revised) (Acres)	Population Density (People/Acre)	City Parkland Area (Acres)	Designed-Natural Park Area Ratio (Designed Park (%) / Natural Park (%)	Percent Parkland	Parkland Per (1000) Capita	Percent of Residents within 0.5 Miles of a Park	pop_near_parks
Durham, NC	275,758	68,678	4.02	2,665	0.11	3.88	9.66	0.51	141,094.34
Memphis, TN	655,061	196,098	3.34	9,194	1.10	4.69	9.15	0.46	300,312.72
Winston-Salem, NC	248,839	83,917	2.97	4,263	7.34	5.08	17.13	0.37	92,267.01
Detroit, MI	660,960	87,844	7.52	5,102	3.63	5.81	7.72	0.80	528,748.17
Toledo, OH	277,467	51,169	5.42	3,175	1.41	6.20	11.44	0.81	225,200.54
Atlanta, GA	498,059	84,250	5.91	5,293	2.70	6.28	10.63	0.72	360,828.80
Cleveland, OH	377,963	46,880	8.06	3,170	1.30	6.76	8.39	0.83	315,251.38
Dallas, TX	1,378,903	215,676	6.39	20,352	1.17	9.44	14.76	0.71	977,793.91
St. Louis, MO	310,144	39,090	7.93	3,749	23.66	9.59	12.09	0.98	303,354.95
Chicago, IL	2,744,859	136,796	20.07	13,609	1.94	9.95	4.74	0.98	2,696,000.51

Source: Article Notebook

EDA Summary

The majority of the city’s land areas are distributed within 100,000 and 300,000 acres of a city’s land area. This distribution ranged from 100,000 acres to 550,000 acres, with an outlying data point above 900,000 acres (Figure 1). The frequency of land area decreases as land area increases. We found little to no linear relationship between city land area and city parkland area (Figure 2). City land per capita, measured in acres, does not correlate with Parkland area (Figure 7). The majority of cities have less than one acre of land per capita, and less than 125,000 acres of parkland per capita. Population density is weakly positive correlation with population density. As population density increases, the percentage of parkland in a city also increases (Figure 3). In Figure 5, the majority of data points are below the 45-degree dashed line, indicating that for every data point, the percentage of population living near parks is consistently lower than the mean percentage of open space access. Figure 8 does not show any or a weak correlation between the variables. When examining the correlation of all features within the dataset, we found that Parkland per Capita and Parkland per 1,000 People were the two most positively correlated features to the Mean Percentage of Built Open Space (Figure 5). When applying the same correlation analysis to our second focus variable, Mean Percentage Open Space Access, Park Units Per Area, and Percent of Residents within a Half Mile Walk were the most positively correlated to the variable.

Modeling

Vizualizing and Ranking Model Performance

wflow_id	.config	.metric	mean	std_err	n	preprocessor	model	rank
bos_2_rf	Preprocessor1_Model1	rmse	3.7411029	0.59891367	3	recipe	rand_forest	1
bos_2_rf	Preprocessor1_Model1	rsq	0.3443191	0.28968211	3	recipe	rand_forest	1
bos_1_rf	Preprocessor1_Model1	rmse	3.7018159	0.56685291	3	recipe	rand_forest	2
bos_1_rf	Preprocessor1_Model1	rsq	0.3241036	0.27364624	3	recipe	rand_forest	2
bos_2_xg	Preprocessor1_Model1	rmse	4.8878321	0.94125107	3	recipe	boost_tree	3
bos_2_xg	Preprocessor1_Model1	rsq	0.2500242	0.14210625	3	recipe	boost_tree	3
bos_1_xg	Preprocessor1_Model1	rmse	4.4965564	0.65984413	3	recipe	boost_tree	4
bos_1_xg	Preprocessor1_Model1	rsq	0.1743254	0.08380129	3	recipe	boost_tree	4
bos_1_lm	Preprocessor1_Model1	rmse	21.7443046	17.60228115	3	recipe	linear_reg	5
bos_1_lm	Preprocessor1_Model1	rsq	0.1616320	0.05566050	3	recipe	linear_reg	5
bos_2_lm	Preprocessor1_Model1	rmse	13.7494375	9.82289704	3	recipe	linear_reg	6
bos_2_lm	Preprocessor1_Model1	rsq	0.1116782	0.07409244	3	recipe	linear_reg	6

wflow_id	.config	.metric	mean	std_err	n	preprocessor	model	rank
osa_1_lm	Preprocessor1_Model1	rmse	275.33964393	256.33604253	3	recipe	linear_reg	1
osa_1_lm	Preprocessor1_Model1	rsq	0.51580872	0.26424787	3	recipe	linear_reg	1
osa_2_lm	Preprocessor1_Model1	rmse	13.46668060	1.70629264	3	recipe	linear_reg	2
osa_2_lm	Preprocessor1_Model1	rsq	0.50547990	0.16955872	3	recipe	linear_reg	2
osa_2_rf	Preprocessor1_Model1	rmse	14.04976508	2.45439883	3	recipe	rand_forest	3
osa_2_rf	Preprocessor1_Model1	rsq	0.30246876	0.12897699	3	recipe	rand_forest	3
osa_1_rf	Preprocessor1_Model1	rmse	13.84363220	2.41203279	3	recipe	rand_forest	4
osa_1_rf	Preprocessor1_Model1	rsq	0.23429515	0.10482788	3	recipe	rand_forest	4
osa_1_xg	Preprocessor1_Model1	rmse	14.59851000	2.15861590	3	recipe	boost_tree	5
osa_1_xg	Preprocessor1_Model1	rsq	0.12253699	0.03106196	3	recipe	boost_tree	5
osa_2_xg	Preprocessor1_Model1	rmse	17.03286366	0.91154682	3	recipe	boost_tree	6
osa_2_xg	Preprocessor1_Model1	rsq	0.06247707	0.04200654	3	recipe	boost_tree	6

Model Tuning

Model Tuning Results

Modeling Summary

Initial modeling results for Built Open Space did not perform well. Boosttree, with the engine xgboost, has the lowest Root Mean Squared Error (RMSE), and the highest R-squared value. The RMSE value was ~5, and the R-squared was 0.25, meaning 25% of the variance can be explained by this regression model (Figure 11). Like Built Open Space, the initial models for Open Space Access did not have a clear winner that stood out. Linear Regression was chosen as the best model to display Open Space Access results because it had a relatively low RMSE and an R-squared value of 0.50, meaning 50% of the variance in this model can be explained (Figure 12). After tuning for RMSE, the Boosted Tree model had an RMSE of 2.76 and an R-squared value of 0.01%. The Built Open Space Predicted vs. Actual values appeared to have a linear relationship; however, the squared residuals show little to no correlation between the predicted values and the residuals. For Open Space Access, using Linear Regression resulted in an RMSE of 18.55 and an R-squared value of 0.66. Like Built Open Space, the predicted and actual values for Open Space Access appear to have a linear relationship, but the residuals do not. Open Space Access results have an outlier in the Open Space Access Predicted vs. Residual values, where the predicted value is very low (>20), and the residual is extremely high, with a value of over 150.

Discussion

Our research found no significant correlation between urban population density and public open space access. The small sample size of 25 cities led to some analysis issues when looking at the relationship between urban demographics and park access. Additionally, Anchorage, Alaska, was an outlying point throughout the analysis, consistently being at the extreme ends of our graphs. Yet, due to our small sample size, it was decided not to remove Alaska and make our sample size even smaller. Anchorage tended to skew the data because there is much higher public open space availability with much less population density due to the nature of the state. The small sample size also created issues in our modeling, such as having to perform a 3-fold cross-validation instead of a standard 10-fold cross-validation (see Modeling section). Built Open Space Access results do not show a relationship between Built Open Space predicted vs. actual residuals, meaning the model failed to accurately predict the amount of built open space in the cities. We did not find any correlation between a city’s population density and the amount of open space the city has; therefore, we reject the hypothesis that there is an intermediate/sublinear relationship between urban population density and public open space availability. This means that an increase in population or population density does not lead to city planners creating more green spaces to accommodate a growing urban population. The mental and physical health effects of a large, urban population without sufficient access to outdoor spaces are unknown and are grounds for further study. This report could be improve with further statistical assessment of these results including ANOVA to better determine significance. That said, the small sample size gives these results extremely high uncertainty. If scope had allowed, statistically testing predicted outputs against the original UN dataset where n > 100 would have helped quantify the uncertainty and variability to better draw conclusions with the small dataset.

Conclusion

In conclusion, we found no correlation between urban population density and public open space access, meaning that there has not been a noticeable change in the urban planning sector to accommodate a continuously growing urban population. Additionally, it is unknown what the mental health effects for this population will be. Looking forward, it would be best to find a different dataset with more cities for our sample size so that we could remove any outliers that may skew our data, allowing us to come to a stronger conclusion. It is important for urban planners to start shifting towards more public open space access to make substantial improvements to society’s overall mental and physical health. This change could be difficult due to the recent and historical lack of public open space for city residents, but it is a change that should be recognized and attempted to move towards.

References

Beall, J., Guha-Khasnobis, B., & Kanbur, R. (2010). Urbanization and development: Multidisciplinary perspectives. Oxford University Press.

Bratman, G. N., Hamilton, J. P., & Daily, G. C. (2012). The impacts of nature experience on human cognitive function and mental health. Annals of the New York Academy of Sciences, 1249(1), 118–136. https://doi.org/https://doi.org/10.1111/j.1749-6632.2011.06400.x

Kohlhase, J. E. (2013). The new urban world 2050: Perspectives, prospects and problems. Regional Science Policy & Practice, 5(2), 153–166.

May, R., Rex, K., Bellini, L., Sadullah, S., Nishi, E., James, F., & Mathangani, A. (2000). UN habitat indicators database: Evaluation as a source of the status of urban development problems and programs. Cities, 17(3), 237–244.

Ratcliffe, M. (2022). Redefining Urban Areas following the 2020 Census. In Census.gov. https://www.census.gov/newsroom/blogs/random-samplings/2022/12/redefining-urban-areas-following-2020-census.html

Slack, T., & Jensen, L. (2020). The changing demography of rural and small-town america. Population Research and Policy Review, 39(5), 775–783.