Does the Connectivity of Urban Public Green Space Promote Its Use? An Empirical Study of Wuhan

Dali M, Nordin NA. Connectedness among Urban Parks from the Users' Perspective: A Systematic Literature Review

Although many benefits of urban green space networks have been consistently demonstrated, most of the discussion on space connectivity has concentrated on ecological aspects, such as patch-corridor-matrix connectivity. There are limited systematic studies that have investigated the connectedness between urban parks and people. This study aimed to explore the connectedness among urban parks from the users' perspective by using a systematic literature review. By following the PRISMA protocol and analyzing 54 studies from Scopus and Web of Science between 2017 and 2022, we proposed the concepts of physical connectedness and perceived connectedness. The "physical connectedness" contained the dimensions of road attributes and park attributes, as well as six categories including physical accessibility, street connectivity, the street environment, spatial scale, facilities and amenities, and natural elements. The "perceived connectedness" mainly referred to people's perception of the physical environment. The four categories were perceived accessibility, perceived safety, aesthetics, and Kaplan's perceptual model. Finally, in terms of individual attributes, the impact of sociodemographic factors (age, gender, income, education, and occupation) and the motivation for activity on park connectedness were also taken into account. On the basis of our findings, this study suggested that park connectedness should not only focus on physical connectedness but also perceived connectedness.

A simplified geospatial model to rank LID solutions for urban runoff management

Studies have shown the usefulness of low impact development (LID) in runoff management in urban areas; however, there is a limited number of systematic decision-making models for ranking LID solutions (i.e., the location and type of LID required). This research proposes a physics-based GIS Multi Criteria Decision Making model (GIS-MCDM), which we refer to as the LID Solution Evaluation and Ranking ApproacH (SERAH). This model integrates the hydrological and socioeconomic-environmental benefits of LID with the subcatchment-level demand of LIDs - this has been traditionally overlooked in previous research. Specifically, SERAH integrates key the contributing criteria, including LID benefits, cost, feasibility, and subcatchment demand to rank LID solutions. To demonstrate the applicability of SERAH, a highly urbanized catchment in Toronto was used as a case-study and three types of LID: rain gardens, infiltration trenches, and porous pavements were considered. The hydrological performance of the ranked solutions was estimated using the stormwater management model, PCSWMM. The resulting LID ranking from SERAH corresponded to the best hydrological performance and LID co-benefits. Runoff volumes were reduced by 8.9-11.3%, and peak runoff values were reduced by 1.3-19.9% compared to the base scenario. The infiltration trench was ranked the highest in 16 of the 19 subcatchments where the cost was identified as a key factor. For the remaining three subcatchments, the rain garden was ranked the highest due to its socioeconomic-environmental benefits outweighing the higher cost. The effect of different rainfall durations, frequencies, and temporal patterns on the performance of the highest-ranked LID solution suggested that LID provide higher performance (runoff volume reduction) in more severe events. SERAH is useful for strategic planning for sustainable infrastructure. Future research is needed to better quantify the socioeconomic and environmental benefits of LID to improve SERAH.

A Literature Review of Big Data-Based Urban Park Research in Visitor Dimension

Urban parks provide multiple benefits to human well-being and human health. Big data provide new and powerful ways to study visitors’ feelings, activities in urban parks, and the effect they themselves have on urban parks. However, the term “big data” has been defined variably, and its applications on urban parks have so far been sporadic in research. Therefore, a comprehensive review of big data-based urban park research is much needed. The review aimed to summarize the big data-based urban park research in visitor dimension by a systematic review approach in combination with bibliometric and thematic analyses. The results showed that the number of publications of related articles has been increasing exponentially in recent years. Users’ days data is used most frequently in the big data-based urban park research, and the major analytical methods are of four types: sentiment analysis, statistical analysis, and spatial analysis. The major research topics of big data-based urban park research in visitor dimension include visitors’ behavior, visitors’ perception and visitors’ effect. Big data benefits urban park research by providing low-cost, timely information, a people-oriented perspective, and fine-grained site information. However, its accuracy is insufficient because of coordinate, keyword classification and different kinds of users. To move forward, future research should integrate multiple big data sources, expand the application, such as public health and human–nature interactions, and pay more attention to the big data use for overcoming pandemic. This review can help to understand the current situation of big data-based urban park research, and provide a reference for the studies of this topic in the future.

Using GIS-based spatial analysis to determine urban greenspace accessibility for different racial groups in the backdrop of COVID-19: a case study of four US cities

As the United States leads COVID-19 cases on global charts, its spatial distribution pattern offers a unique opportunity for studying the social and ecological factors that contribute to the pandemic's scale and size. We use a GIS-data-based approach to evaluate four American cities-Anchorage (Alaska), Atlanta (Georgia), Phoenix (Arizona), and Portland (Oregon) characterized by the significant composition of different racial and ethnic group populations. Building upon previous studies that investigated urban spatial inequalities using the environmental justice framework, we examine: (1) the relative racial vulnerability of Census Block Groups (CBG) and ZIP Code Tabulation Areas (ZCTA) to COVID-19 (2) green space distribution at CBG and ZCTA scale. Using standard normalization methods, we ranked racial vulnerability against % available green space for each city. Our results highlight the legacy of past and present urban planning injustices. The project is useful from environmental justice, public health management, and urban planning perspectives.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10708-021-10538-8.

Spatial distributive effects of public green space and COVID-19 infection in London

While public green spaces (PGS) are opined to be central in the pandemic recovery, higher accessibility to PGS also mean a higher risk of infection spread from the raised possibility of people encountering each other. This study explores the distributive effects of accessibility of PGS on the COVID-19 cases distribution using a geo-spatially varying network-based risk model at the borough level in London. The coupled effect of social deprivation with accessibility of the PGS was used as an adjustment factor to identify vulnerability. Results indicate that highly connected green spaces with high choice measure were associated with high risk of infection transmission. Socially deprived areas demonstrated higher possibility of infection spread even with moderate connectivity of the PGS. The study demonstrated that only applying a uniform social distancing measure without characterising the infrastructure and social conditions may lead to higher infection transmission.

Exploring the Dynamics of Urban Greenness Space and Their Driving Factors Using Geographically Weighted Regression: A Case Study in Wuhan Metropolis, China

Urban greenness plays a vital role in supporting the ecosystem services of a city. Exploring the dynamics of urban greenness space and their driving forces can provide valuable information for making solid urban planning policies. This study aims to investigate the dynamics of urban greenness space patterns through landscape indices and to apply geographically weighted regression (GWR) to map the spatially varied impact on the indices from economic and environmental factors. Two typical landscape indices, i.e., percentage of landscape (PLAND) and aggregation index (AI), which measure the abundance and fragmentation of urban greenness coverage, respectively, were taken to map the changes in urban greenness. As a case study, the metropolis of Wuhan, China was selected, where time-series of urban greenness space were extracted at an annual step from the Landsat collections from Google Earth Engine during 2000–2018. The study shows that the urban greenness space not only decreased significantly, but also tended to be more fragmented over the years. Road network density, normalized difference built-up index (NDBI), terrain elevation and slope, and precipitation were found to significantly correlate to the landscape indices. GWR modeling successfully captures the spatially varied impact from the considered factors and the results from GWR modeling provide a critical reference for making location-specific urban planning.

Income Differentiation as a Factor of Unsustainability in Forestry

Environmental policy is a set of objectives put in place for the protection of natural resources including water, air, soil, food, and other renewable resources. In addition, it has a considerable impact on the labor market and the income of employees in the environmental and forestry sectors. Environmental policy both directly and indirectly creates new jobs, the so-called green jobs. These jobs are designed to be long-term and sustainable, working towards both environmental and socio-economic stability. The aim of the research was to determine if there was a difference in income between the forestry and the environmental policy sectors. The primary objective of this paper was to propose ideas and instruments for strengthening the income of employees in both sectors to the creators of the new State Environmental Policy. This objective was met through appropriate research methods, including the field survey technique. In terms of statistics, we used descriptive characteristics and tested the hypothesis using a T-test. The data from 70 respondents were collected from January 2019 to December 2019. Half of the respondents were from the forestry sector, and the other half worked in environmental protection. Their total income was compared by using two selective T-tests, and the results showed a strong discrepancy. The analysis indicated that the average incomes in forestry are significantly lower than incomes in the environmental protection sector (in the Czech Republic). The statistically higher income of environmental workers reflects that the State Environmental Policy is effective, which improves the position of employees in the labor market. The forestry sector deserves similar concentrated state assistance, and therefore we recommend that the new State Environmental Policy in the Czech Republic also addresses the problem of low income in forestry.