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Systematic Review

Urban Disparity Analytics Using GIS: A Systematic Review

by
Tanmoy Malaker
and
Qingmin Meng
*
Department of Geosciences, Mississippi State University, Mississippi State, MS 39762, USA
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(14), 5956; https://doi.org/10.3390/su16145956
Submission received: 19 June 2024 / Revised: 8 July 2024 / Accepted: 9 July 2024 / Published: 12 July 2024
(This article belongs to the Special Issue GIS Implementation in Sustainable Urban Planning)
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Abstract

:
Urban disparity has been extensively studied using geospatial technology, yet a comprehensive review of GIS applications in this field is essential to address the current research status, potential challenges, and future trends. This review combines bibliometric analysis from two databases, Web of Science (WOS) and Scopus, encompassing 145 articles from WOS and 80 from Scopus, resulting in a final list of 201 articles after excluding 24 duplicates. This approach ensures a comprehensive understanding of urban disparities and the extensive applications of GIS technology. The review highlights and characterizes research status and frontiers into research clusters, future scopes, and gaps in urban disparity analysis. The use of both WOS and Scopus ensures the review’s credibility and comprehensiveness. Findings indicate that most research has focused on accessibility analysis of urban services and facilities. However, there is a recent paradigm shift toward environmental justice, demonstrated by increasing GIS applications in analyzing pollution exposure, urban heat islands, vegetation distribution, disaster vulnerability, and health vulnerability.

1. Introduction

Urban disparity, characterized by the uneven distribution of resources, services, and opportunities among communities of an urban setting, remains a pressing challenge for contemporary urban planners and policymakers [1,2,3,4,5]. This phenomenon manifests in various forms, including economic and racial inequality, access to services, environmental justice, and spatial segregation within a city [2,3,4]. Urban disparity has been looked at through different lenses like socioeconomic (e.g., age, income, and race), health, accessibility (to service and resources), gender (disparity among males and females), geospatial location, environment, transport, and so on. Urban disparity significantly affects resident’s quality of life and manifests in economic, social, environmental, and health outcomes. Economic inequality results in varying income levels and job opportunities across neighborhoods, while social disparities are evident in education quality/level and access to services [6]. Environmental injustices often see low-income areas exposed to higher pollution levels [7], fewer green spaces [8], and placement adjacent to a dumpsite. Health disparities follow, with limited healthcare access leading to poorer health outcomes in disadvantaged communities [9]. Historical segregation, economic shifts, and policy decisions perpetuate these inequalities, necessitating comprehensive interventions like affordable housing policies, inclusive urban planning, and community engagement to address and mitigate urban disparity [10]. Above all, these interventions seek extensive research and understanding of the factors that are resulting in urban disparity for further policy intervention. While 56 percent of the world population is living in cities (about 4.4 billion), with 80 percent of global GDP contribution [11], urban disparity is grabbing the attention of the global research community. Its increasing importance, multidimensional nature, and multidisciplinary involvement make urban disparity highly demanding for different research areas.
While most of the research on urban disparity is concerned with accessibility [12], few studies have described urban disparity from the perspective of accessibility to cultural ecosystem services of urban green space (UGS) [1] and tried to investigate the distribution and quality of UGS along with accessibility. Benati [1] focused on the distribution of UGS and their accessibility to different social groups. While Benati talked about UGS distribution and quality, others [13,14,15,16,17] looked at the social health vulnerability through the amalgamation of accessibility to healthcare and transportation. Many studies included the socioeconomic backwardness of different ethnic, racial, and minority groups, which includes mainly access to jobs, education, health, and urban service [1,17,18,19,20,21,22]. Transportation (accessibility and modal choice) seems to be one of the most closely related disciplines while studying urban disparity [23,24,25,26]; however, detailed transport network analysis is not very common for urban disparity analysis [27]. Housing, building density, spatial distribution, and location are also studied to determine urban disparity [13,14,15,16,17,28]. Meanwhile, the classic approach of health disparity related to socioeconomic conditions has evolved with the increasing inclusion of newer urban issues like pollution [7], urban heat island (UHI) impact [28,29], and accessibility to physical activities (PA) [30]. Geographic Information System (GIS) has emerged as a powerful tool in these analyses, as has the visualization of urban disparities in an urban setting [19,23,31,32,33], which was hardly possible through traditional analytical methods [34,35]. By integrating socio-economic, demographic, and environmental data, GIS provides a comprehensive framework for understanding the spatial dimensions of urban inequality [36,37,38,39]. Despite increasing evidence and calls for research into urban disparities and the use of GIS tools, there remains a lack of comprehensive reviews on the subject.
Most publications rely on conventional approaches, such as accessibility analysis to resources and services in urban settings [1]. However, there are few innovative approaches that delve deeper into understanding urban disparities using GIS, where combining new ideas with traditional accessibility analysis is uncommon but not unheard of. Although existing research has explored accessibility analyses related to urban disparity, there is a notable absence of comprehensive analyses of disparity in urban settings. While some studies have examined disparity from the perspective of public transport [23,40], countries like the USA require more studies focused on network analyses and modal choices beyond just public transport. Moreover, the outcomes of existing studies are often predictable, frequently highlighting the deprivation and vulnerability of poor, racial, and ethnic minority groups. Meanwhile, the spatial disparity within urban settings and the disparity in urban development and growth have been largely overlooked.
GIS offers advanced methodologies for analyzing urban disparities spatially and temporally [31,41,42,43], surpassing traditional approaches through enhanced spatial analysis, data integration, and visualization capabilities [44,45]. GIS facilitates the creation of detailed multi-thematic maps with diverse distance measurements that integrate diverse socio-economic indicators, revealing complex spatial patterns and correlations typically overlooked by conventional methods. By synthesizing data from various sources and incorporating spatial and temporal dynamics, GIS provides a comprehensive spatiotemporal perspective on urban disparities [18,46,47]. Advanced spatial statistical techniques and predictive modeling within GIS enable the identification and forecasting of disparity trends, while high-resolution remote sensing imagery supports the assessment of physical and environmental urban conditions [7].
This systematic review provides a comprehensive understanding of the subject, emphasizing the significance of GIS in studying urban disparity and introducing a novel approach by combining bibliometric analyses from both the Web of Science and Scopus databases. The review explores various forms of urban disparity, such as economic and racial inequality, and highlights the role of GIS in addressing these issues through the integration of diverse data within a GIS environment. Section 2 details the systematic literature review approach, utilizing VOSviewer (version 1.6.20.0). The findings represent the chronological development of research interest in urban disparity using GIS worldwide and identify five major clusters of research interest, which are summarized in Section 3. Section 4 reflects on evolving research paradigms, emphasizing the importance of green space, health vulnerability, and disaster vulnerability in urban disparity studies. Finally, the review underscores the multifaceted nature of urban disparity, the critical role of GIS, and the need for integrated approaches to effectively address urban inequalities, in addition to limitations, which are summarized in Section 5.

2. Material and Methods

This review aims to collect data from WOS and Scopus, two widely popular science article database platforms, to demonstrate how the research in urban disparity using GIS has proliferated spatially and temporally. The bibliometric data analysis emphasizes the current research trend, gaps, and future scopes. Through extensive bibliometric analysis using VOSviewer [48] (a widely used bibliometric software tool), this study tries to analyze and visualize the research findings. This review also provides a wider view of research conducted over 190 cities around the world on urban disparity using GIS. The study found that accessibility analysis is the most used approach around the world for urban disparities study. However, new approaches like greenspace (proximity, distribution, amount, and pattern), health vulnerability, disaster vulnerability, built environment and urban heat island (UHI), and urban growth are some of the factors that are taking over the accessibility analysis, eventually identifying disparity.
The literature analysis used in this study is conducted in three steps. The first step includes the systematic analysis of existing literature on WOS and Scopus using appropriate keywords and multicriteria analysis (data collection/identification). Secondly, these two different datasets are combined into a single library dataset that is compatible with further statistical analysis. Finally, VOSviewer for Scientometric network analysis [48] and visualization are used to obtain a comprehensive understanding of the current research trend as well as the gap in the exiting research approach (Figure 1).
VOSviewer is a tool developed for constructing and visualizing bibliometric networks. These networks can be created based on citation, co-citation, and bibliographic coupling relations. VOSviewer also offers text mining functionality to create and visualize the co-occurrence networks of important terms extracted from a body of scientific literature. In this study, the power of text mining and co-occurrence network visualization has been utilized to extract factors of urban disparity studied since 1996 using GIS.

2.1. Data Collection

Web of Science (WOS) offers a diverse, comprehensive, and accessible database that indexes a wide range of academic journals across various disciplines. WOS includes peer-reviewed and reputable sources ensuring quality journals and the flexibility of bibliometric analyses like advanced search options, research metrics, h-index, citation tracking, and comprehensive citation analysis. Our study is focused on the understanding of urban disparity and the use of GIS in identifying the causes and factors of urban disparity. GIS is a widely used tool for urban issues like planning, development, management, transport, and public services for its advanced ability to analyze and represent geospatial factors in relation to socioeconomic characteristics of urban settings. Considering the aim of the research, we choose the keywords and the process of screening very carefully. We wanted all the available bibliographic data from each platform (WOS and Scopus) but only those that fit our criteria to study urban disparity. For that reason, we narrowed down the search using the keywords Urban GIS (Urban, city, and GIS) and urban disparity (urban and disparity).
The search criteria started with Urban GIS in all fields of existing articles in each platform (WOS and Scopus) and eventually narrowed down to topics (including all articles that have urban GIS in the abstract, keywords, and author keywords) and titles (Figure 2 and Figure 3). Once narrowed down to the ultimate level, we used the “and” Boolean operator to merge all the articles in this stage on urban GIS and urban disparity, which formed our initial list. For further screening, we start the Prisma method from the PRISMA statement (2020) (see Supplementary Materials) [48,49] from this stage, considering the other criteria we were skeptical about. For any studies considering disparity on a regional or rural scale, we removed them from our list as we were focused on urban disparities only. Besides regional and rural levels, any studies comparing cities or looking into the disparities between different cities were removed too. Finally, studies of a different language (language not English), irrelevant research objectives, and irrelevant research methods were removed from the list for a solid and justified database for further analyses. Following the process described, for the WOS platform and removing articles that have a language other than English (13), irrelevant Research Objective (125), and irrelevant research method (39), we obtained a list of 145 peer-reviewed and quality journals (Figure 2).
Scopus also provides a comprehensive citation database curated by Elsevier, which includes over 70 million records from more than 24,000 peer-reviewed journals. For the Scopus search, we followed a similar approach to the WOS platform database. Following a similar approach for the Scopus platform and after finally removing articles that have a language other than English (7), irrelevant research objectives (116), and irrelevant research methods (43), we obtained a list of 80 peer-reviewed quality journals (Figure 3).
After finallycombining these two datasets from two different platforms, 24 duplications were found and removed from the final list (using Zotero (6.0.36) tools). The thorough screening and removal of duplications resulted in a list of 201 (n = 201) articles for bibliometric analysis.

2.2. VOSviewer as a Bibliometric Tool

For bibliometric analysis using VOSviewer, we merged the WOS and Scopus databases using bibliometric merge tool software and converted the database into a compatible bibliometric file for further analysis. The merging resulted in the removal of 24 duplications and a final library of 201 peer-reviewed and quality journal articles. The duplicate literature was identified using bibliometric tools (Zotero) for creating a final list of 201 peer-reviewed literature. Zotero provides the flexibility to import, check duplicates from different databases, and export selected data for further processing through VOSviewer or any other bibliometric analysis tools.
VOSviewer is a prominent and widely used tool for bibliometric analysis in the research field [48] and is increasingly being used for scientific review articles. In this study, VOSviewer was utilized for bibliometric analysis, network visualization, and text mining to understand the connections between different research databases and their intrinsic meaning. VOSviewer (a Java-based program) helps to create bibliometric network mapping and insightful presentation [48]. In this study, the co-occurrence (instead of co-authorship) was focused on as the research aims to identify the application of GIS in urban disparity and the factors or metrics used to identify the disparity in an urban setting. The VOSviewer analysis helps to extract and visualize the intrinsic meaning and prominent factors of quantifying urban disparity within the peer-reviewed articles reviewed in this study.

3. Results

This review (n = 201) shows a chronological increase in research in the field of urban disparity (Figure 4) from 1996 to 2024 (May of 2024). Although from 1996 to 2006 only 9 articles were published, there was a significant increase in publication in later years. Especially since 2019 and onwards, there has been a dramatic increase in publications considering urban disparity and GIS. The findings of this chronological data represent the urges and importance of studying urban disparity in recent times.
We found that most of the studies were consecutively conducted in the USA (81), China (32), United Kingdom (8), and Canada (8). Most of the research on urban disparity was conducted in more urbanized countries (Figure 5), which is obvious. However, there are some developing countries that have been studied but most of them were larger urban agglomerations with diversified socioeconomic conditions, for instance, the city of Mumbai in India.
The reviewed articles encompass 190 cities worldwide as study areas for investigating urban disparity (Figure 6). Figure 6 illustrates the geographic distribution of these cities covered in the study. Certain cities have been the subject of multiple publications and thus resulted in about 330 mentions within the reviewed articles. Notably, New York has been extensively studied, appearing in 13 publications focusing on urban disparity globally. Other frequently studied cities in North America include Philadelphia (eight publications), Houston (eight publications), Chicago (six), Detroit (six), Boston (six), Atlanta (five), Washington DC (five), Austin (five), and Los Angeles (five). Toronto has been cited in four separate studies. Beyond North America, cities in Asia, Europe, South America, and Africa have also been examined. Tehran (Iran) and Beijing (China) stand out as the most frequently studied cities outside of North America in terms of urban disparity research in Asia.
However, Asia ranks second in terms of the number of studies on urban disparity (Figure 7), with China alone featuring 32 distinct studies. Among these, Beijing (six) and Shenzhen (five) are the most extensively studied areas, located in the southeastern province of China known for its significant urban agglomeration. Seoul, a prominent urban agglomeration in South Korea, has also been mentioned and studied in multiple publications (five) focusing on urban disparity.
Although Asia ranks second in terms of the number of studies on urban disparity analysis, there is a notable absence of research on urban disparities, particularly in South Asia. Apart from India, there are no studies focusing on urban disparities in south Asia, including south-east Asia (one study on Singapore). A similar research gap is evident in Central American countries and Russia as well.

3.1. Five Main Research Clusters Using GIS

The bibliometric analysis using VOSviewer demonstrates the emergence of six main clusters, ‘gis’, ‘greenspace’, ‘access’, ‘health’, ‘socioeconomic’, and ‘city’, which show the highest co-occurrences among the articles reviewed (Figure 8). Other notable clusters include ‘open space’, ‘healthcare’, ‘spatial data’, and ‘education’. These clusters help to identify the main factors and metrics of urban disparity within an urban setting. Our research reviewed all the available databases on urban disparity and GIS in the WOS and Scopus platforms up to 31 May 2024, which fulfilled the search criteria described in the materials and methods section. The search criteria inherently increase the co-occurrences of ‘GIS’ and ‘City’, so we may overlook these two keywords as factors of urban disparity; but, the others are undoubtedly the most effective ways to identify urban disparity according to the existing literature. However, the diameter and prominent presence of gis as an occurrence cluster in the center of the diagram reflects the importance and powerful presence of gis as a tool in urban disparity research.
The articles reviewed used different words to describe similar aspects or factors; for instance, ‘urban’ and ‘city’ explain similar aspects and the plural form ‘cities’ occurred more frequently than ‘city’. To address this highly random distribution of keywords, some keywords with similar meanings were merged into a single keyword for better and clearer visualization of urban disparity studies shown in the figure above (Figure 8).
Merging these keywords of the same meanings into one gives us a more concentrated cluster than if they were treated independently. The merging process was conducted in VOSviewer using a compatible file format. As a result, even though there are five major clusters (accessibility, green, health, socioeconomic, and open space), there are five sets of factors underneath to consider (one for each) and we should always consider them when discussing major factors of urban disparity in this study.
VOSviewer also provides the flexibility to delve further into the strengths and connections between the co-occurrences. This tool allows us to visualize the connections among the co-occurrences and helps identify the closely related factors. Figure 9 represents the strongest connections with wider lines, clearly showing that the five major clusters (accessibility, green, health, socioeconomic, and open space) discussed above have the strongest connections when studying urban disparity.
On the other hand, overlay visualizations provide a quick overview of bibliometric developments over time (Figure 10). The overlay analysis indicates that healthcare (planning and policy), ethnic disparity, spatial disparity, and quality of life are some of the older aspects of identifying urban disparity. However, “Green” (including terms like green space, green spaces, greenspace, urban green space, urban green spaces, and canopy) as a factor of urban disparity is the most recent concern. This shift could be attributed to the vast use and availability of remote sensing data, which encourages these types of studies.
Among the five major clusters, urban growth, spatial equity, spatial accessibility, landscape metrics, spatial distribution, and land surface temperature have become the most recent phenomena in urban disparity studies. This trend is also an impact of the increasing access to and availability of remote sensing data and their use in academia. The integration of GIS and health is comparatively new (between 2015 and 2020), whereas socioeconomic factors and accessibility to services and facilities are relatively older (2010–2015). However, accessibility with newer perspectives like walkability, cycling, urban green space, and physical activity (PA) has made the approach classic.
In Figure 11, we delved deeper into the factors related to larger clusters. The graphical presentation (a) demonstrates how accessibility is connected to other keywords. The wider the connection line, the stronger the bibliographic connections. Green (green space) shows a strong connection with open space (b) and urban ecosystems, which highlights the importance of considering factors like open space and urban ecosystems when studying urban disparity from the perspective of green infrastructures. Similarly, health has a strong connection with green space (c) and open space highly connects green space (d) but socioeconomic disparity can be examined through the diverse lens of healthcare, education, social status, and minority group studies (e). This research is a systematic review of urban disparity, so the keyword city/urban is an obvious cluster that is not needed to describe it because the objective of this review is to systematically analyze the social, economic, health, and environmental aspects of urban disparity.
Figure 11a, showing access and its connection to other factors, represents the highest connections (total connections 33) along with socioeconomic (also 33 connections) among all the major clusters (compared to other major factors in Figure 11b–e). However, ‘GIS’ has the highest link strength (of 229) among all the clusters as it was one of the major criteria for identifying the articles on urban disparity. ‘City’ also has a higher strength (158) but we have avoided these two clusters in figure analysis because their occurrence is obvious while screening the articles (urban and GIS). The figures also help identify the specific contents and contributions of these studies by each connected node. For instance, accessibility shows a high connection with health (health care), open space, green space, and city. Excluding city (as all the studies were conducted in urban settings), accessibility studies focused highly on these factors. The studies of urban disparity with accessibility analysis are not limited to these factors alone. Gender, age group (older adults, children, and youth), safety, education, and environment (pollution exposure) are also studied from the perspective of accessibility. GIS, on the other hand, is in the center of these connections, playing a powerful tool in these analyses to an urban extent. All these factor analyses and their findings are helping to create a sustainable and accessible city for all by identifying the disparities and helping the policymakers to solve the different disparity issues.
These visual representations help identify the underlying connections of factors considered while studying urban disparity in the reviewed articles. They also suggest considering these factors and their connections in future studies as well. Figure 11 also helps to identify the most prominent factors in identifying urban disparity, where accessibility analysis dominates the existing literature. Although the socioeconomic cluster has the highest link strength, it includes 14 subcategories (Table 1) compared to accessibility (which has only 8 subcategories). However, both clusters have the same link value of 33, which makes accessibility analysis a major approach in identifying urban disparity.

3.2. Urban Disparity and Major Bibliographic Clutters

Accessibility is the most widely used concept of urban disparity among the articles reviewed, with about 48% of them utilizing accessibility as a metric for identifying urban disparity. These studies examine how some communities have less access compared to others due to their socioeconomic status or geospatial location. Major factors of accessibility include parks and green areas (green = 25; park = 17), health services and health-related activities (20), nearby public transport stations (14), and food services (11). Accessibility to parks and green spaces is the most frequently used criterion for determining urban disparity. However, only one study used access to liquor stores from a particular community as a criterion to assess disparity within the urban setting [16]. The second most used criterion is access to healthcare services, which includes additional dimensions such as socioeconomic, racial, and minority aspects (Figure 11c). These factors provide a comprehensive view of urban disparity by highlighting the various dimensions of accessibility that impact different communities.
Where accessibility to health services and green spaces is widely used, green space and health themselves are two other major clusters of co-occurrences. Green spaces (including terms like green spaces, greenspace, urban green space, urban green spaces, and canopy) are a widely used criterion for addressing accessibility and disparity within communities. About 50 articles (approximately 25% of the articles reviewed) have used green spaces as a major factor to analyze and describe urban disparity. Green spaces are a significant and visible factor in determining disparity from physical, environmental, and socio-economic aspects. In addition to green space accessibility, the studies also investigated the size, distribution, proximity, walkability, spatiotemporal distribution, racial segregation, equity in use (potentially accessibility), quality, and their contribution to decreasing UHI (urban heat island) effects, which are related to health, the environment, and health vulnerability.
Alongside these aspects of UGS and healthcare accessibilities, many studies have taken transport facilities as a metric for determining urban disparity, especially access to public transport (mostly the distance to bus stands from a particular neighborhood). Fourteen (14) of the articles considered transportation accessibility solely to investigate urban disparity, broadly falling under accessibility. A few pieces of literature (2) have delved further to understand the availability of modal choices while moving within a city [27,30,64,84,170,171] and one of them made a connection between transportation and health accessibility by investigating community access to public transport that could take them to a healthcare center [23]. Open space, another prominent cluster found in this review, also broadly falls under accessibility, as most of the articles were trying to analyze the accessibility to open spaces as a determinant of disparity among urban communities.
Accessibility to food (markets and shops) and its distribution is also a focus in about 14 articles. While most of these articles directly investigated accessibility, some studied equality, distribution, and quality of food. One study focused on food access for schoolchildren during the pandemic [101], while others explored food availability, food environment, and socioeconomic (race/ethnicity) disparities in distribution [59,72,170,171]. In terms of food distribution disparity in urban settings, the concept of a “food desert” is a new term used to identify the distribution and geospatial location disparities of food outlets, which results in decreased access [64,71]. A food desert is a comparatively new concept in urban disparity studies (Figure 9) and is the inverse of equal accessibility, measuring how difficult it is to access food from a particular location and serving as a metric to determine urban disparity. Besides distributional disparity, studies have also examined how it may impact health, especially obesity, in a racial context [30].
Socioeconomic status, including income, socioeconomic condition, poverty, and race, has also been a vital factor in studying urban disparity. Socioeconomic factors are the most mentioned cause and reason for urban disparity even in the accessibility analysis. In accessibility analysis, most of the studies focused on the accessibility of economically deprived or socially deprived groups (minority groups, poverty, and race). However, only two of the articles reviewed have solely used socioeconomic data to identify urban disparity [169,172]. In contrast, a few others used social media data to identify urban disparity in addition to the socioeconomic factors creating the disparity [169].Other studies utilized nighttime satellite imagery to find a connection between disparity and socioeconomic conditions [173,174,175]. Job access and its distribution (though often represented as part of accessibility) [176] and resource distribution are also significant economic factors in determining urban disparity [98].
About 43 of the articles mentioned health as a factor in identifying urban disparity, with around 18 focusing on the accessibility of health services, healthy places (green, parks, and open spaces), physical activity (PA), equity, and caregiving (both for children and older adults). Others primarily discussed diseases (asthma, cancer, and hypertension) and the underlying causes of such disparities within the same environment, often correlating socioeconomic factors with health to identify any relationship between socioeconomic conditions and health status. Six articles included pollution disparity as a factor, focusing on light, air, and urban heat island (UHI) effects, which can be identified spatially using satellite imagery, indicating the growing use of remote sensing in urban disparity studies [7,177,178]. While recent publications show increased use of remotely sensed multispectral imagery, nighttime imagery or active remote sensing is rarely used. However, satellite imagery for investigating the health impacts of pollution in relation to socioeconomic conditions has become widely popular. Additionally, segregation of service delivery [56] and the availability/inequality of health services [139] were studied in about 26 articles, considering health services and health status disparity as determinants of urban disparity.
Besides the clusters and data types used, the geographical distribution of study areas on urban disparity is also critical. Most studies on urban disparity have occurred in developed countries (Figure 5), compared to developing countries. The USA alone contributes 81 studies (about 40 percent) in this field, with New York being the most cited city (13 studies). Other frequently mentioned developed countries include the United Kingdom, Canada, South Korea, Italy, Australia, and Spain, with developed countries collectively contributing 135 articles (67.5 percent) out of 200 cases (one study did not specify a study area). In contrast, China is the most cited among developing countries in urban disparity issues (32 studies), followed by Iran (7 studies) and India (4 studies). China, often considered an emerging economy, typically focuses on larger urban agglomerations such as Shanghai, Guangzhou, Shenzhen, and Beijing, known for their economic, political, cultural, and architectural importance. Tehran in Iran is the second most cited city among developing economies, being the capital city of Iran.
At a continent level, South America and Africa have the least number of studies on urban disparity (excluding Australia), despite these regions facing significant urban issues with limited resources. Besides the unequal distribution of research among countries, there is also a drastic inequality in selecting study areas within countries. Mostly larger and economically well-known cities are studied from the lens of urban disparity worldwide. However, many of the most crowded cities in developing countries with the largest populations have not yet been studied from the perspective of urban disparity. This is a knowledge gap of urban disparity in developing countries.

4. Discussion

The comprehensive bibliometric analysis of two widely used platforms (WOS and Scopus) provides a broad overview of the current research status on urban disparity. The findings help us understand the current research trends in urban disparity, with an increase in publications in recent years. There is evidence of a dramatic increase in publications on urban disparity since 2019. However, most studies focus on a few major clusters: urban green space (including parks and open spaces), health, socioeconomic conditions, and accessibility (including transport systems) to urban services, as well as their geospatial distribution analyses to determine urban disparities. Accessibility to services (including green spaces, parks, healthcare, food, and transport) is the most common factor studied to determine disparity from the beginning of this research trend. Even after decades, accessibility remains the basic criterion of urban disparity studies.
Although accessibility analysis is a widely used approach to determine urban disparity, it potentially encompasses many other factors, such as socioeconomic conditions, environment, equity, race, and ethnicity. Urban disparity analysis in accessing urban services, resources, and facilities using GIS tools is a conventional research paradigm. However, a noticeable paradigm shift in urban disparity research has been observed since 2020.The inclusion of green space proximity [118,120], distribution [76,103,159], UHI [29,135,145,166,179,180], equity (spatial, gender, and age group) [57,93,99,125,131,133,155,181,182], urban design [183], walkability [99,143,181,184,185], and urban greenness index [102] in relation to socioeconomic disparity has been increased in recent publications. The use of multispectral imagery and the availability of data has shaped the paradigm shift from service and resource accessibility analysis to more geospatial character-focused analysis of urban disparities in recent years.
The increased use of vegetation, pollution, proximity, spatial distribution, urban growth [186], and expansion [42,113,165,166] demonstrates the growing integration of satellite imagery into GIS-based analysis. Pollution exposure, environmental and disaster vulnerability [187,188,189,190], and health vulnerability [23,144] are some of the aspects of urban disparity that still require further investigation. New York is the most studied urban agglomeration globally in terms of urban disparity, with 13 articles reviewed, but only one of them examined UHI [166] and health vulnerability. Besides New York, other major cities should also be studied from the perspective of these emerging factors, particularly in the fastest-growing cities around the world. While socioeconomic factors are one of the most obvious contributors to disparities in urban settings, other aspects (physical growth and development disparity) need to be thoroughly studied to create sustainable and livable cities.
Disaster and health vulnerability have become increasingly important phenomena when investigating urban disparity, challenging the existing research paradigm as these factors surpass traditional socioeconomic disparity analyses. However, health and disaster vulnerability do not exclude the classic approach of accessibility analysis and socioeconomic disparity, as many of these factors depend on accessibility. For instance, accessibility to green spaces and healthcare through public transport can influence health vulnerability in urban settings, where socioeconomic conditions may affect overall accessibility, thus increasing vulnerability and exacerbating conditions during disasters. These interdependent factors necessitate a more complex indexing of urban disparity analysis rather than focusing solely on single factors like accessibility, distribution, and socioeconomic status. Integrating recent approaches with classic methods may help to explore urban disparity more accurately, with GIS serving as a tool to enhance the entire analytical process efficiently.

5. Conclusions

Our review identifies five prominent clusters in urban disparity research worldwide. Besides examining spatial and temporal research trends and the use of these clusters of access, green space, health, open space, and socioeconomic dimensions as main factors in urban disparity studies, we sought to identify the future scope and potential of urban disparity research. Many authors suggested using a combination of remote sensing, socioeconomic, geospatial, transport, and social media data (SMD) rather than relying on a single data source to obtain a broader and more accurate insight [1]. Future steps for identifying urban disparity in more detail could include modern services like broadband services [20,191], the speed of online shopping delivery, electric vehicle (EV) charging station accessibility and distribution, and human mobility behavior through SMD. GeoAI and advanced geospatial analytics for data integration and disparity assessment will be more effective and efficient, playing a crucial role in big data and fine-tuned analysis.
Apart from data, combining the spatial indicators of Urban Green Space (UGS) distribution with socioeconomic variables (population, age, income, and education) at the neighborhood scale will help reveal the factors that contribute to greening and ungreening across neighborhoods in a city [46]. UGS is mostly studied from the accessibility perspective but quality, service capability, and safety have been overlooked while studying disparity [95]. Investigating why a neighborhood is less safe than others and identifying the socioeconomic or geospatial factors responsible for such unsafe conditions is crucial to understanding and addressing urban disparity [95]. Urban heat island (UHI) vulnerability in developing countries needs more attention, especially as climate change exacerbates the situation with decreasing green spaces. Comprehensive and precise thermal analyses at finer scales of green infrastructure are needed as high variability can be observed within different local climate zones in a city [87]. Investigating the combination of natural and built environments may help resolve the current crisis and contribute to addressing the research gap in UHI and health impacts [87].
An apparent limitation is that most approaches primarily focus on accessibility analysis rather than comprehensive geospatial analysis. Even within accessibility analysis, Euclidean distance is widely used, which shows obvious drawbacks in urban studies, but block distance or time distance can be more meaningful and accurate than Euclidean distance. Furthermore, leveraging GIS for more complex analyses of urban disparities, such as hazard threats, vulnerability, health, and service disparity over space and time, remains a challenge for current and future studies. Future studies may develop composite indices capturing the interdependencies among factors to provide a holistic view of urban inequality. Addressing urban disparity requires multifaceted approaches, incorporating spatial and temporal analysis, socioeconomic data, and physical and environmental considerations. GIS remains invaluable in guiding policy interventions and promoting equitable urban development, with researchers and policymakers needing to stay attentive to emerging trends and AI methodologies to effectively mitigate urban disparities.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/su16145956/s1, PRISMA Checklist.

Author Contributions

Conceptualization, Q.M. and T.M.; methodology, Q.M. and T.M.; software, T.M. and Q.M.; validation, Q.M. and T.M.; formal analysis, T.M. and Q.M.; supervision, Q.M.; resources, Q.M. and T.M.; data curation, Q.M. and T.M.; writing—original draft preparation, T.M. and Q.M.; writing—review and editing, Q.M. and T.M.; visualization, T.M. and Q.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The bibliometric data analyzed in this study will be available on request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. A methodological diagram of the literature review.
Figure 1. A methodological diagram of the literature review.
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Figure 2. A prisma flow diagram for paper screening in WOS.
Figure 2. A prisma flow diagram for paper screening in WOS.
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Figure 3. A prisma flow diagram for paper screening in Scopus.
Figure 3. A prisma flow diagram for paper screening in Scopus.
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Figure 4. Chronological distribution of 29 years of articles.
Figure 4. Chronological distribution of 29 years of articles.
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Figure 5. Country-level distribution of articles reviewed (some articles include multiple countries).
Figure 5. Country-level distribution of articles reviewed (some articles include multiple countries).
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Figure 6. Global distribution of study areas (cities) within the reviewed articles.
Figure 6. Global distribution of study areas (cities) within the reviewed articles.
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Figure 7. Continents covered by the reviewed articles.
Figure 7. Continents covered by the reviewed articles.
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Figure 8. Network visualization of the reviewed articles.
Figure 8. Network visualization of the reviewed articles.
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Figure 9. Network visualization of the highest connection strength of co-occurrences.
Figure 9. Network visualization of the highest connection strength of co-occurrences.
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Figure 10. Co-occurrences over the time.
Figure 10. Co-occurrences over the time.
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Figure 11. (a) Access and its connections to other keywords with a link strength of 142. (b) Green space and its connections to other keywords with a link strength of 92. (c) Health and its connections to other key words with a link strength of 162. (d) Open space and its connections to other keywords with a link strength of 60. (e) Socioeconomic and its connections to other keywords with a link strength of 189.
Figure 11. (a) Access and its connections to other keywords with a link strength of 142. (b) Green space and its connections to other keywords with a link strength of 92. (c) Health and its connections to other key words with a link strength of 162. (d) Open space and its connections to other keywords with a link strength of 60. (e) Socioeconomic and its connections to other keywords with a link strength of 189.
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Table 1. List of merged keywords to understand urban disparity.
Table 1. List of merged keywords to understand urban disparity.
KeywordsMerged intoReferenced Studies
accessibility
health services accessibility
network analysis
public transport
public access
Health care access
spatial accessibility		Access[1,16,17,20,21,22,24,26,32,41,42,46,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95]
cities
city
metropolitan area
New York City
urban area
Urban areas		CityAll the reviewed article
education
School
Schools		Education[19,61,62,96,97,98,99,100,101]
geographic information system
geographic information systems
gis
spatial analysis
geographic information systems (gis)		GISAll the reviewed article used GIS
green
green space
green spaces
greenspace
urban green space
urban green spaces
canopy
urban ecosystem services		Green space[1,13,15,31,42,43,46,50,58,60,67,70,74,75,81,86,87,93,96,99,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128]
Health
health services
COVID-19
epidemiology
health care delivery
health care disparity
health disparities
health disparity
physical activity
public health
urban health
obesity		Health[20,29,35,40,41,44,53,54,63,66,77,83,85,86,89,100,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155]
Health care
health care planning
health care policy		Health care[23,63,66,83,131,132,148]
Open Space
public space
urban parks
park
parks		Open space[21,32,55,57,65,76,80,82,88,90,156,157,158,159,160,161,162,163,164,165]
economics
equity
income
poverty
racial disparity
residence characteristics
socioeconomic factors
socioeconomic status
socioeconomics
socioeconomic
socioeconomic conditions
social status
minority group
ethnic minority		SocioEco (Socioeconomic)[7,18,22,31,42,50,51,60,72,74,77,82,86,92,102,107,124,129,147,149,151,152,166,167,168,169,170]
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Malaker, T.; Meng, Q. Urban Disparity Analytics Using GIS: A Systematic Review. Sustainability 2024, 16, 5956. https://doi.org/10.3390/su16145956

AMA Style

Malaker T, Meng Q. Urban Disparity Analytics Using GIS: A Systematic Review. Sustainability. 2024; 16(14):5956. https://doi.org/10.3390/su16145956

Chicago/Turabian Style

Malaker, Tanmoy, and Qingmin Meng. 2024. "Urban Disparity Analytics Using GIS: A Systematic Review" Sustainability 16, no. 14: 5956. https://doi.org/10.3390/su16145956

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