Exploring the Spatial Correlation of Blight and Litter: A Case Analysis of Memphis, Tennessee Neighborhoods

Abstract

Urban blight and litter are twin issues that significantly affect the quality of life in city neighborhoods. This paper investigates the relationship between blight and litter, commonly overlooked in urban studies literature. We measure the prevalence of blight and litter across block groups in our mapping with a focus on socioeconomic factors, including income levels, crime rates, and land use types (industrial, commercial, and residential) for our case study, Memphis, Tennessee. Using statistical and spatial analytics, as well as data from the Memphis Data Hub and the City of Memphis, we show the prevalence of blight and litter across block groups. GIS was used to map neighborhood-specific blighted structures and their spatial connection to litter accumulation. We also explore the distribution of blight and litter across different land uses. A Pearson correlation value of 0.639 suggests a strong positive relationship between blight and litter at the block group level. Spatial clustering is assessed by Global Moran’s Iand Local Moran’s I, identifying neighborhood-level hotspots. The block group is used as the unit of analysis to capture micro-spatial variation and to enable meaningful equity-based insights at the neighborhood level. Our mapping offers practical insights into urban revitalization strategies in deference to per capita income, crime rate, and land use. The findings contribute to urban policy discussions by promoting the joint consideration of blight and litter, helping guide future community-based interventions aimed at alleviating the negative impacts of blight and litter, particularly in disadvantaged neighborhoods.

1. Introduction

Urban blight and litter are persistent urban issues with severe social, economic, and environmental consequences. Blight, commonly identified by abandoned buildings, vacant lots, vandalized structures, and deteriorated infrastructure, is both a symptom and a cause of neighborhood decline [1,2,3,4]. Litter, defined as misplaced anthropogenic waste, arises from behavioral, structural, and socioeconomic factors and often signals environmental neglect and lack of public services [5,6]. Both conditions impact quality of life, public health, and property values, and together they form a visual landscape of urban decay that reinforces negative perceptions and deters investment.

In Memphis, Tennessee, these challenges are particularly evident in areas affected by poverty, aging infrastructure, and historically uneven access to city services. Neighborhoods in inner city and south Memphis consistently face higher incidences of illegal dumping and neglected properties. Spatially concentrated blight is closely tied to patterns of disinvestment and industrial decline in cities like Memphis [2], while studies e.g., [7] highlight that litter accumulates most in areas with lower income and limited infrastructure [8]. These environmental issues are deeply entangled with historical inequalities, including redlining and systemic neglect.

Although urban blight and litter have been studied individually in the literature, limited attention has been given to their spatial correlation, especially using fine-resolution units like block groups. Most previous research has focused on either the socio-economic impacts of blight or behavioral patterns associated with littering [6,9], with fewer studies exploring the interaction of these conditions through geospatial analytics. Recent studies emphasize the need for integrating spatial and statistical approaches to capture localized dynamics and support place-based planning strategies [10,11].

This paper addresses that gap by examining the spatial and statistical correlation between blight and litter in Memphis neighborhoods, with a focus on socio-economic factors including per capita income and crime, as well as land use categories (residential, commercial, and industrial). The research employs Pearson correlation to test variable relationships, Global Moran’s I to assess spatial clustering, and Local Moran’s I to identify neighborhood-level hotspots. The block group is used as the unit of analysis to capture micro-spatial variation and to enable meaningful equity-based insights at the neighborhood level [12,13].

By combining quantitative and spatial techniques, this research contributes to the emerging field of urban analytics, with implications for environmental justice and spatial inequality. The study’s findings are expected to support more targeted revitalization strategies by identifying specific zones where blight and litter overlap and where intervention may be most urgently needed. In doing so, it aims to contribute to empirical research and planning practice for more sustainable and equitable urban environments.

2. Research Objectives and Questions

This study seeks to determine how urban blight and litter are related, both statistically and spatially, across neighborhoods in Memphis. While widely documented in urban studies, the combined presence and spatial distribution of blight and litter is rarely investigated at the neighborhood (block group) level. Identifying where urban degradation is most pronounced by the presence of both blight and litter, contextualized by socioeconomic indicators, suggests a more focused and equitable planning response targeting neighborhood areas in need of intervention. The goal is to generate practical, spatially grounded insights that contribute to policy-making and environmental justice, especially in communities facing systemic disinvestment.

2.1. Research Objective

To determine the correlation of blight and litter across block groups in Memphis and explore how the correlation is contextualized by crime, income, and land use types using statistical and spatial analysis techniques.

2.2. Research Questions and Approaches

a.

To what extent are blight and litter correlated at the neighborhood level?

–

Tested using Pearson correlation across all block groups.

b.

How does blight relate to litter in block groups characterized by crime, per capita income, and land use types?

–

Assessed using correlation and spatial patterning.

c.

Do blight and litter exhibit clustering patterns that are statistically significant in space?

–

Evaluated using Global Moran’s I to determine overall spatial autocorrelation.

d.

Where are the hotspots or spatial clusters of blight and litter located within the city?

–

Identified through Local Moran’s I (LISA) to highlight block group-level clusters and outliers.

3. Literature Review

Urban blight and litter are two long-standing challenges that shape the physical, social, and environmental fabric of cities. While often treated separately in academic and policy literature, both phenomena share underlying structural drivers, particularly those tied to spatial inequality, socio-economic deprivation, and disinvestment. This review presents the foundational research on blight and litter individually, before addressing their intersection and the role of spatial analysis in understanding their distribution and correlation.

3.1. Blight: Definitions, Causes, and Spatial Patterns

Urban blight is broadly defined as the deterioration of physical environments and the economic and social decline of neighborhoods, commonly marked by abandoned buildings, overgrown lots, structural disrepair, and visible neglect [1,2,14,15,16,17]. The literature links blight to reduced property values, increased crime perception, and diminished resident well-being [11,18,19]. Industrial blight poses a unique challenge in cities like Memphis, where post-industrial land often remains underutilized and environmentally hazardous [2].

From a spatial planning perspective, blight is not randomly distributed; instead, it forms observable clusters, particularly in historically marginalized and economically disadvantaged areas. Spatial clustering tools such as entropy analysis, Local Indicators of Spatial Association (LISA), and spatial regression have been applied in past studies to examine blight’s geospatial dimensions [13,20,21]. These tools have proven useful in identifying “hotspots” of decay, aiding planners in targeting revitalization efforts.

3.2. Litter: Behavioral Roots and Urban Impact

Litter, typically defined as human-made waste misplaced in public space, is driven by both individual behavior and systemic urban conditions. Scholars have emphasized that littering behaviors are affected by factors such as social norms, perceived neglect, peer influence, and lack of environmental stewardship [6,22]. At a broader scale, however, litter accumulation is more likely in areas with poor waste infrastructure, inadequate municipal services, and lower socio-economic status [5,8,9,23,24,25,26,27,28,29].

Studies show that litter accumulates most frequently in areas adjacent to vacant lots, blighted structures, or low-income residential zones [8,29,30]. Metrics like the Clean Environment Index and Environmental Status Index have also been used to quantify litter presence, enabling comparative studies across neighborhoods [9]. Certain land uses, such as pedestrian-heavy areas or those with limited access to waste disposal infrastructure, are associated with higher litter volumes [27,31].

3.3. Blight-Litter Overlap: Interlinked Symptoms of Spatial Inequality

Despite the vast literature on blight and litter individually, relatively few studies explicitly examine their interdependence. Those that do suggest that litter is more likely found in or near areas of blight and that both are mutually reinforcing symptoms of environmental neglect and spatial injustice [2,7]. The co-occurrence of these phenomena contributes to disinvestment cycles and deteriorating neighborhood perception, further discouraging community engagement and external investment [32,33].

Recent scholarship has called for integrated approaches that recognize the spatial co-location of litter and blight as indicators of deeper urban inequality [10]. In Memphis and similar cities, the strongest correlations are often found in areas marked by longstanding disinvestment, infrastructure failure, and institutional neglect. However, spatial methods such as Global Moran’s I and Local Moran’s I have not been widely applied to study the overlap between these conditions at finer spatial scales like block groups [2].

3.4. Gaps in Literature

Although there is growing attention to the spatial dimensions of urban decay, several gaps remain:

a.

Few studies explicitly correlate blight and litter at the neighborhood scale using spatial and statistical tools [1].

b.

Integrated modeling frameworks combining socioeconomic, land use, and environmental variables are rare, particularly in Southern U.S. cities like Memphis.

c.

There is limited work examining the directionality or causality of the blight-litter relationship [2,7].

d.

Spatial equity, the idea that some neighborhoods receive less public service and infrastructure support, remains underexplored in litter and blight analyses.

In sum, urban blight and litter are characteristically intertwined, shaped by shared socio-economic conditions and spatial structures. While each issue has been studied independently, their co-occurrence presents an opportunity for more holistic urban diagnostics. Existing research highlights the need for neighborhood-level, spatially nuanced analysis, which this study addresses by using spatial statistics to explore clustering patterns in context. The results inform targeted, equitable, and sustainable interventions across cities facing similar challenges.

4. Methodology

This study adopts statistical and spatial analysis methods to examine the correlation between urban blight and litter and to identify spatial patterns that reveal environmental inequity in Memphis. The analysis applies statistical correlation and spatial autocorrelation methods to assess the relationships among key variables at the block group level. Three main methods were employed: Pearson correlation, Global Moran’s I, and Local Moran’s I (LISA).

The first method used is Pearson correlation, which tests the linear association between pairs of variables. This technique was applied to measure how closely blight is related to other factors, including litter, crime, per capita income, and the proportions of residential, commercial, and industrial land use in each block group. The Pearson coefficient (r) ranges from −1 to +1, where values close to +1 indicate a strong positive relationship, values near −1 indicate a strong negative or inverse relationship, and values near 0 suggest no linear relationship. Statistical significance was assessed by using corresponding t-values and p-values, with results considered significant at p < 0.05.

To determine whether the aforementioned variables are spatially clustered or randomly distributed, the study uses Global Moran’s I, a well-established method for measuring spatial autocorrelation across the entire study area. Moran’s I values range from −1 (perfect dispersion) to +1 (perfect clustering), with a value near 0 indicating spatial randomness. Each variable, blight, litter, crime, per capita income, and land use types, was tested for spatial clustering using this method. The results provide an overview of whether similar values (e.g., high blight or high litter) tend to occur near one another citywide.

Next, Local Moran’s I, also referred to as LISA (Local Indicators of Spatial Association), was used to detect localized clustering patterns and spatial outliers within individual block groups. While Global Moran’s I provides an overall picture of spatial autocorrelation, LISA pinpoints where high or low values are spatially grouped. In this study, Local Moran’s I maps classify each block group into one of three categories:

a.

Cluster: similar values are adjacent (e.g., high blight surrounded by high blight);

b.

Dispersed: neighboring values are dissimilar (e.g., high surrounded by low);

c.

Random: no statistically significant spatial structure.

The combination of Pearson correlation and both local and global Moran’s I techniques allows for a multi-scalar analysis of environmental conditions, linking statistical strength with spatial patterning. Together, these methods offer a more comprehensive understanding of how litter and blight interact and how they are embedded within Memphis’s urban landscape.

4.1. Study Area

Our case study is focused on the City of Memphis, located in Shelby County, Tennessee, in the Southeastern United States, a legacy city known for its rich cultural heritage. Memphis is a mid-size U.S. city with a 2020 population of 633,104 and an estimated slight decrease to 620,063 (ACS 2023a) [34]. The median population density is 3268. The low urban density is reflected in the urban form of the block groups with an average density of 4598 per square mile. The city is not transit-friendly due to the low density, making commuting to jobs and services car-dependent. African American is the dominant racial composition of the urban resident population (62.91%) (ACS2023b). [35]. The demographics reflect Memphis’ longstanding racial dynamics of being both predominantly African American and racially segregated. Like other U.S. cities, Memphis experienced redlining that resulted in segregated neighborhoods. The post-industrial suburbanization of jobs and population contributed further to the decline of the inner city, particularly public transit, resulting in blight and crime [2,35,36]. Local planning policy of decades of extending public infrastructure (road, water, sewer) beyond the city limit to the unincorporated, suburban periphery, combined with single-use zoning also contributed to the decline of the inner city. However, current planning is promoting “build up not out” and a greater emphasis on mixed-use zoning to counter low-density sprawl [37]. Noteworthy, the priority list of the former municipal government that also called for efficiency and comprehensive planning after decades of a hiatus are blight, litter, and crime—interrelated factors that we also use in our mapping.

For a discussion of the historic impact of zoning, crime, and transit as challenges to urban revitalization in the American city, see [7,13,36,38,39,40,41].

Like post-industrial cities, Memphis also faces long-standing structural challenges, including high poverty rates, racial segregation, aging infrastructure, and disinvestment. These conditions have led to a concentration of urban blight and litter, especially in economically marginalized and historically underserved neighborhoods. To analyze the detailed spatial differences of these environmental stressors, we use U.S. Census block groups as the main unit of spatial analysis. This level of geographic aggregation contains the building block of the metropolitan region—the neighborhood—with greater granularity than census tracts or ZIP codes, ideal for detecting neighborhood-level disparities in land use, socioeconomic conditions, and urban degradation [2,13].

The study area encompasses all 546 block groups within the municipal boundary of the City of Memphis (see Figure 1). Regions with insufficient data or those mainly composed of significant non-residential land uses, such as airports or mainly industrial areas, were omitted to maintain the validity of the neighborhood-level analysis. To ensure spatial consistency and precision, all datasets were synchronized to the same coordinate system and trimmed to fit within the city limits. Additionally, major roads and block group boundaries were incorporated to offer contextual and navigational clarity for understanding the spatial distributions of blight and litter throughout the urban environment.

4.2. Data Sources

This study relies on a combination of spatial, socio-economic, and administrative datasets to examine the correlation between urban blight and litter across Memphis neighborhoods at the block group level. All data were collected for the year 2024, unless otherwise noted, and processed using ArcGIS Pro (ESRI 2025, version 3.5.2) [42] to ensure consistency in projection and spatial alignment.

Blight, litter, and crime were originally provided as point datasets. The blight data were obtained from the City of Memphis Division of Planning and Development, representing the location of structures identified as abandoned, vacant, vandalized, or otherwise physically deteriorated. Litter data, specifically illegal dumping incidents, were sourced from the Memphis Data Hub [43], consisting of citizen-reported points geocoded across the city. Crime data were collected from the Public Safety Institute at the University of Memphis, covering a range of reported incidents. All three datasets were geocoded (where needed) and then aggregated to the block group level using spatial joins. The total number of points for each variable within each block group was calculated to support correlation and clustering analysis.

Additional socio-economic and land use data were integrated into the analysis. Per capita income figures were extracted from the U.S. Census Bureau’s American Community Survey (ACS), 2022 5-year estimates [44] joined to block group boundaries using FIPS codes. Land use data were provided by the Memphis and Shelby County Division of Planning and Development and reclassified into three primary categories: residential, commercial, and industrial. For each block group, the percentage of land area covered by each use type was calculated to create comparable indicators.

The study uses Census block groups as the unit of analysis to provide neighborhood-scale granularity and enable the identification of localized spatial patterns. An advantage of using the Census block-group level is the availability of income data. All data layers were projected to the NAD 1983 StatePlane Tennessee FIPS 4100 (US Feet) coordinate system [45]. This integrated dataset forms the analytical foundation for the correlation and spatial clustering methods applied in the following sections.

4.3. Spatial Distribution of Key Variables

To better understand the geography of blight and litter across Memphis, this section presents (GIS) thematic maps showing the incidence or intensity of each variable at the block group level. These maps provide a spatial foundation for interpreting statistical and cluster analysis results in later sections. All datasets reflect conditions for the year 2024, unless otherwise noted.

4.3.1. Blight

The distribution of blighted parcels reveals clear clustering in inner city and south Memphis, with high concentrations around areas of older housing stock, industrial zones, and under-resourced neighborhoods. Many block groups in these areas contain multiple instances of damaged, vacant, or severely deteriorated structures. The western edge of the city also exhibits moderate blight, particularly near transitional land uses (Figure 2).

4.3.2. Litter (Dumping Incidents)

Litter incidents measured by the number of dumping reports within each block group follow a similar spatial trend. The highest densities of dumping are also found in central and southern neighborhoods, overlapping with areas of high blight. This overlap supports the hypothesis that litter and blight are spatially correlated, likely due to similar socio-economic and service-related conditions (Figure 3).

4.3.3. Crime

Crime data, aggregated at the block group level, show moderate clustering in central Memphis and parts of the southeast and north. However, the spatial distribution is more diffuse compared to blight or litter. Certain block groups with high crime rates do not necessarily align with the most blighted or littered areas, suggesting that crime may operate under different spatial influences (Figure 4).

4.3.4. Per Capita Income

The lowest-income block groups are located primarily in south and southwest Memphis, commonly overlapping with areas of high blight and litter. In contrast, northeast and far east Memphis contain block groups with the highest per capita income, where both blight and litter are minimal. This spatial pattern supports longstanding concerns about spatial inequity and environmental burden (Figure 5).

4.3.5. Land Use Types (Residential, Commercial, Industrial)

Land use data were grouped into three major categories to assess their influence on blight and litter (see Figure 6):

a.

Residential land use is widespread but shows higher densities in older inner-city neighborhoods, many of which also exhibit elevated blight levels.

b.

Commercial land use is concentrated along major corridors and intersections, with variable overlap with blight.

c.

Industrial zones are concentrated along riverfront, rail, and peripheral areas. Some industrial-adjacent block groups show dispersed blight patterns, suggesting potential edge effects or zoning transitions.

These spatial distributions highlight where urban degradation is most severe and establish the foundation for formal spatial statistical analysis in the following sections. The observed geographic overlap of blight, litter, and low income suggests the need for data-driven planning focused on spatial equity and neighborhood revitalization.

4.4. Spatial Analysis

This section presents the outcomes of the three primary analyses used in the study: Pearson correlation, Global Moran’s I, and Local Moran’s I (LISA). These results help quantify the relationships between urban blight, litter, and related variables and reveal the spatial structure of these patterns across Memphis block groups.

4.4.1. Pearson Correlation Results

Pearson correlation was used to test the relationships among blight and six other variables: litter, crime, per capita income, residential land use, commercial land use, and industrial land use. The correlation coefficient (r) quantifies the direction and strength of the relationship, while the p-value indicates whether the correlation is statistically significant (

Table 1. Pearson correlation between blight and other variables.

Compared to Blight	Pearson r	t-Value	p-Value	Interpretation
Litter	0.639	19.37	<0.0001	Strong positive correlation
Residential Land Use	0.465	12.09	<0.0001	Moderate positive correlation
Per Capita Income	−0.216	−5.12	<0.0001	Weak negative correlation
Commercial Land Use	−0.028	−0.61	0.543	No significant relationship
Industrial Land Use	0.055	0.79	0.431	No significant relationship
Crime	−0.019	−0.41	0.679	No significant relationship

). The Pearson correlations were calculated as follows:

$$r={\displaystyle \frac{{\sum }_{i=1}^n\left(x_i-\overline{x}\right)\left(y_i-\overline{y}\right)}{\sqrt{{\sum }_{i=1}^n{\left(x_i-\overline{x}\right)}^2}\sqrt{{\sum }_{i=1}^n{\left(y_i-\overline{y}\right)}^2}}}$$

where xᵢ = values of the x variable in a sample, $\overline{x}$ = mean of the values of the x variable, yᵢ = values of the y variable in a sample, and ȳ = mean of the values of the y variable [46].

Pearson Correlation—Variable-by-Variable Scatter Plot Interpretation

Blight and Litter: Pearson is a statistical test of a linear relationship. To detect non-linearity, if any exists, we performed scatter plots of pairs of variables. For elaboration on non-linearity, see Discussion section. The scatter plots of blight count and litter count across block groups use natural logarithm (ln) transformations on both variables to normalize skewed distributions and handle zero values. Specifically, each axis represents ln (Blight Count + 1) and ln (Litter Count + 1) to ensure mathematical validity and reduce the impact of outliers.

A second-order polynomial trendline is applied to the log-transformed data to reveal the nonlinear association between blight and litter. The observed pattern indicates that as blight increases, litter tends to increase also at an increasing rate. This visualization supports the hypothesis that areas with higher levels of blight are also more likely to experience elevated levels of litter, particularly when accounting for the effects of data skewness and zero counts (Figure 7).

Blight and Residential Land Use: This scatter plot displays the log-transformed relationship between blight counts and residential land use counts across block groups. Both variables were transformed using the natural logarithm (ln(x + 1)) to normalize skewed distributions and accommodate zero values.

The curved second-order polynomial trendline highlights a nonlinear association between the two variables. The general pattern suggests that as the number of residential properties increases, the number of blighted properties also tends to increase, particularly in higher-density areas. However, the wide spread in the lower range of residential counts suggests variability in blight levels in less residentially developed blocks (Figure 8).

This pattern may indicate that while dense residential areas are more likely to experience blight, there are also block groups with low residential presence but high blight due to other land uses (e.g., vacant, commercial, or industrial zones).

Blight and Per Capita Income: A negative trend is visible in the scatter plot. The correlation coefficient of −0.216 (p < 0.0001) shows that lower-income block groups tend to have more blight. However weak, this inverse relationship supports the link between socio-economic vulnerability and environmental decline (Figure 9).

Blight and Crime: This scatter plot visualizes the log-transformed relationship between blight counts and crime counts across block groups. The x-axis displays ln(Blight Count + 1), and the y-axis shows ln(Crime Count + 1), with +1 added to account for zero values and the natural logarithm applied to normalize the data.

A second-order polynomial trendline suggests a very weak and nonlinear association between blight and crime. While crime incidents occur across all levels of blight, the trendline is nearly flat, indicating no strong directional pattern. This may reflect the complexity of crime dynamics, which may be influenced by type and a broader set of social, spatial, and temporal factors beyond the presence of physical blight alone (Figure 10).

This transformation helps clarify that in this dataset, blight and crime are not strongly correlated, at least when both are aggregated by block group and measured in this form.

Blight and Commercial Land Use: This scatter plot shows the log-transformed relationship between blight counts and commercial land use counts across block groups. The x-axis represents ln(Blight Count + 1), and the y-axis represents ln(Commercial Count + 1), with the +1 adjustment accounting for any zero values and the natural logarithm used to normalize the distributions (Figure 11).

The fitted second-order polynomial trendline indicates a very weak and nearly flat nonlinear association, suggesting little to no strong correlation between the prevalence of commercial parcels and the number of blighted properties at the block group level. This reult implies that blight is not consistently associated with the presence or absence of commercial land use or that other factors such as vacancy, underutilization, or disinvestment may play a more significant role in shaping blight patterns in commercial zones.

Blight and Industrial Land Use: This scatter plot illustrates the log-transformed relationship between blight counts and industrial land use counts across block groups. The x-axis represents ln(Blight Count + 1), and the y-axis represents ln(Industrial Count + 1), using the natural logarithm to reduce skew and accommodate zero values (Figure 12).

The plotted second-order polynomial trendline reveals a very weak and slightly curvilinear relationship, indicating that the presence of industrial land use is not strongly correlated with the number of blighted properties. The data show a concentration of low industrial counts across many block groups, and the overall distribution suggests that blight is not significantly shaped by industrial activity at the neighborhood scale.

This transformation helps clarify that while industrial land use exists in blighted areas, its relationship to blight is relatively minor compared to other land use types, such as residential.

4.4.2. Global Moran’s I—Spatial Autocorrelation Summary

Global Moran’s I was used to assess whether each variable is spatially clustered, dispersed, or randomly distributed across the city (

Table 2. Global Moran’s I results.

Variable	Moran’s I	z-Score	p-Value	Interpretation
Blight	0.4875	17.716	0.0000	Strong spatial clustering
Per Capita Income	0.5104	18.453	0.0000	Strong clustering (low-income areas group)
Litter	0.2784	10.134	0.0000	Moderate clustering
Residential Land Use	0.2819	10.256	0.0000	Moderate clustering
Commercial Land Use	0.2128	7.838	0.0000	Moderate clustering
Industrial Land Use	0.0871	4.789	0.0000	Weak but significant clustering
Crime	0.0735	2.757	0.0058	Weak but significant clustering

) [12].

Blight and Per Capita Income: exhibited the strongest clustering patterns, with values significantly different from random, indicating systematic spatial structure.

Litter, Residential, and Commercial Land Use: demonstrated moderate clustering, suggesting that these variables are not randomly distributed.

Crime and Industrial Land Use: indicated weaker but still significant clustering, which warranted follow-up analysis using Local Moran’s I to explore neighborhood-level variation.

4.4.3. Local Moran’s I—Variable-by-Variable Cluster Analysis

Blight: Clusters of high blight are concentrated in central and southern Memphis, particularly in historically disinvested areas. Dispersed zones are found on the urban fringe, where land use transitions may be occurring (Figure 13).

Litter: Similar to blight, litter clusters appear in south and central neighborhoods. Overlapping hotspots with blight support the hypothesis of co-presence of environmental stressors (Figure 14).

Per Capita Income: Strong low-income clusters are found in the same areas with high blight and litter. This supports the argument that spatial inequality underlies environmental decline (Figure 15).

Crime: Clusters are visible but more scattered than blight/litter. Some high-crime zones align with blighted areas, but overall correlation is weak (Figure 16).

Residential Land Use: Residential clusters align with areas of older housing stock and elevated blight. Some dispersed patterns exist where residential neighborhoods border industrial zones (Figure 17).

Commercial Land Use: Clustering appears along major corridors (e.g., Summer Ave, Lamar Ave). These do not consistently align with blight clusters (Figure 18).

Industrial Land Use: Weak clustering and more dispersed patterns along industrial peripheries. Industrial edges sometimes border high-blight zones, suggesting a need for further study (Figure 19).

4.4.4. Histograms and Spatial Lag Scatter Plots

Histograms of key variables reveal right-skewed distributions, particularly for blight and litter, indicating that most block groups have low values, while a small subset exhibits extreme concentrations (Figure 20, Figure 21, Figure 22, Figure 23, Figure 24, Figure 25 and Figure 26).

4.5. Validation of Spatial Results Using Google Street View Imagery

To validate the spatial clustering outcomes from the Local Moran’s I analysis illustrating blighted parcels across Memphis, representative Google Street View imagery was reviewed and assessed for visual indicators regarding land use, environmental quality, and built form conditions. The imagery included four sites that were sampled from the clusters and outliers produced by the spatial analysis, representing residential, industrial, and commercial land use typologies. The use of imagery in this way qualitatively confirms the spatial patterns produced in the numerical findings: (a) depicts a residential street at the intersection of Rayner St. and Kerr Ave. classified as a clustered area of blight. The presence of illegal dumping and the deteriorated condition of the sidewalks are consistent with the descriptions of clustering, as found by Local Moran’s I. (b) is representative of the intersection of Warford St. and Chelsea Ave., also found in a residential area. Despite being flagged as random or not statistically significant in the Local Moran’s I output, visual inspection of this site revealed several blight indicators, including vacant lots and unmaintained structures. This indicates the need to continue to improve spatial observation and modeling techniques (more in Discussion section). (c) is the intersection of Ellington St. and Mount Olive Rd. and represents an industrial area with a dispersed pattern of blight. The widely spaced structures and large parcels have isolated sites of dumping that are similar to spatial analysis parameters of non-clustered structure distribution. (d), the intersection of Chelsea Ave. and McNair St., is a clustered commercial area with signs of regular illegal dumping. These images are consistent with Local Moran’s I output as a cluster for commercial land uses (Figure 27). The visualization confirms the spatial clustering outcomes and contextually informs how land uses exhibit particular patterns of presence and intensity of blight and litter (regarding sources and validation of data, see also the Discussion section below).

5. Discussion

The data we used are from various Census publications in the public domain, as well as from local government, enabling citizen engagement by reporting neighborhood issues, including blight and litter. Arguably, the means for citizen participation do not ensure equity in accessibility depending on the availability and effectiveness of the technology of communication, telephone, or a public website portal, or even guarantee resolution. Recall also Arnstein’s (1969) “Ladder of Citizen Participation” [47]. The rungs of the ladder signified various forms of participation, from lowest to highest, from “therapy” to “citizen control.” However, the community is not a monolithic entity. Socio-economic, demographic, and cultural characteristics influence participation and “taking stake” in the neighborhood. Neighborhood-specific, walking, “windshield,” or aerial surveys complement, update, or validate data on neighborhood conditions for accuracy and completeness. For a discussion of the limitations of citizen reporting and procedures for retrieving, compiling, and mapping data for analysis e.g., see [16,21,22,23,27,48,49,50,51,52].

The findings of this study reveal a clear and statistically significant link between urban blight and litter, through both correlation and spatial distribution. The strong positive Pearson correlation (r = 0.639) shows that environmental problems identified by blight and litter occur jointly across Memphis block groups. This supports the main hypothesis that litter and blight are not isolated issues but are mutually reinforcing conditions that appear in neighborhoods suffering from ongoing disinvestment and neglect.

The results also reveal that blight is moderately correlated with residential land use and inversely related to per capita income, suggesting that neighborhoods with more residential land, especially aging or under-maintained housing and lower income levels, are more susceptible to blight. These findings are consistent with previous studies that emphasize the role of poverty, weak enforcement, and aging infrastructure in shaping patterns of physical decline [1,2]. The correlation between blight and income highlights the socioeconomic vulnerability of certain areas, particularly those in central and southern Memphis, where residents often face reduced access to services and limited capacity to maintain property conditions.

In contrast, crime, commercial land use, and industrial land use showed no statistically significant linear relationship with blight. Although spatial clustering was evident in some parts of the city for these variables, their Pearson correlation values were weak and not significant. This suggests that these factors may influence blight in more non-linear or localized ways or that other intervening conditions (such as infrastructure quality, housing tenure, or municipal services) may mediate their impact.

The Global Moran’s I results confirmed that blight, litter, income, and land use types are not randomly distributed but instead form spatial clusters, particularly in areas of historical disinvestment. These patterns were further confirmed by the Local Moran’s I (LISA) results, which identified specific hotspots of high blight and litter concentration, especially in central and southern Memphis. These clusters strongly align with low-income zones, reinforcing the idea that environmental burdens are not only correlated with poverty but are also spatially concentrated. This is a core indicator of spatial injustice, where certain communities bear a disproportionate share of environmental and infrastructural decline.

Furthermore, the visual and statistical overlap between high-blight and high-litter zones suggests that public perceptions of neglect may reinforce behavioral responses like illegal dumping, which in turn further stigmatize and degrade neighborhoods. If this cycle is not addressed, it may perpetuate disinvestment and negatively impact resident morale, economic opportunities, and neighborhood identity.

Taking together, these results have important implications for urban planning and policy. First, they highlight the need for place-based interventions that treat blight and litter not as isolated issues but as interconnected symptoms of broader systemic challenges. Second, the block-group scale of analysis enables a more targeted and equitable allocation of resources, allowing planners and policymakers to prioritize cleanup, enforcement, and investment in areas most affected by overlapping burdens. Ultimately, this analysis highlights the significance of spatial data and geostatistical methods in identifying and addressing patterns of urban inequality that are frequently overlooked in traditional planning approaches.

6. Future Research

While this study provides statistical and spatial evidence of the relationship between blight and litter in Memphis neighborhoods, it is exploratory in nature and leaves open opportunities for future work. One of the most promising directions for continued analysis involves the development of a predictive statistical model to estimate litter accumulation as a function of multiple explanatory variables. Such a model would allow for the quantitative estimation of how much each independent variable (e.g., blight, income, crime, land use) contributes to the presence of litter, while controlling for the influence of the others. Whereas Pearson correlation in this study assessed the relationship between variables individually, a multiple regression framework would reveal the combined effect of socio-economic and spatial factors on litter distribution (for path analysis example, which accounts for the influence of confounding factors, see [2,53], in dealing with multicollinearity by using principal component analysis). Future research could thus provide the underpinning of a policy-relevant understanding of the conditions that lead to illegal dumping and littering. For example, planners could determine whether blight remains a strong predictor of litter after accounting for income and land use or whether crime becomes more influential when modeled jointly with other variables. This approach would also support the development of evidence-based strategies for intervention, allowing city officials to prioritize neighborhoods not only based on current litter levels but also on underlying predictors of future accumulation, toward advancing the goals of spatial equity and sustainable neighborhood planning.

7. Planning and Policy Implications

The findings of this study carry important implications for urban policy, neighborhood planning, and environmental management in Memphis and other cities facing similar challenges. The strong spatial and statistical correlation between blight and litter suggests that these issues should not be addressed in isolation. Instead, they should be treated as interdependent conditions that co-occur and reinforce one another, particularly in historically disadvantaged neighborhoods.

One of the most immediate implications is the need for targeted intervention strategies focused on areas identified as hotspots of both blight and litter. The Local Moran’s I analysis revealed that these clusters are concentrated in central and southern Memphis, where low-income populations are more likely to face compounded environmental burdens. These neighborhoods would benefit from a place-based approach that combines code enforcement, waste management, and community engagement initiatives. Coordinating efforts across departments such as sanitation, planning, housing, and public safety can help maximize efficiency and ensure that interventions are not fragmented or duplicative. Philadelphia is an example of converting vacant land into productive gardens that feed the neighborhood. The city’s public works division transformed abandoned lots from barren to fertile land and thereby also reduced blight, increased green space, and improved drainage (see also [54,55]. New Haven, CT, provides an example of converting an 80-acre blighted industrial site (brownfield) near the university campus into a district with mixed income housing and green space (for listed benefits, see [7,52,56,57,58]. For commercial properties, cases, strategies, and tools, including zoning, see [59]; for blighted commercial (also called grayfield) sites redevelopment, see [60].

In terms of planning strategy, this research supports the integration of spatial equity indicators into city planning frameworks. By using data at the block group level, policymakers can more precisely identify which areas are disproportionately affected and allocate resources based on need. This is especially relevant for cities like Memphis, where capacity and funding are often limited, and prioritization is necessary. Investment in these areas can include revitalization grants, infrastructure upgrades, and land reuse programs, as well as partnerships with local community development corporations (CDCs) to support long-term neighborhood renewal.

These findings also align with broader goals articulated in the Memphis 3.0 Comprehensive Plan (2019) [37], particularly its emphasis on reinforcing anchors, improving neighborhood livability, and promoting sustainable land use. Addressing blight and litter together through a spatial lens can help implement these goals in a more focused and effective manner. For example, cleanup and enforcement efforts could be concentrated near anchor areas with the potential for economic reinvestment, while blighted parcels near residential cores could be repurposed for public use or affordable housing.

Finally, this study underscores the need for systemic solutions rooted in environmental justice. The overlap between blight, litter, and low income reflects more than poor physical conditions—it highlights the uneven distribution of public services and civic attention. Planning responses should therefore be grounded in a commitment to correcting spatial inequities, ensuring that all communities have the opportunity to thrive in clean, safe, and dignified environments.

8. Conclusions

This paper sets out to examine the relationship between urban blight and litter in Memphis, Tennessee, through a combination of statistical correlation analysis and spatial clustering techniques. Drawing on data from the City of Memphis, Memphis Data Hub, and U.S. Census Bureau, the study analyzed block-group-level indicators of blight, litter, per capita income, crime, and land use characteristics. By using methods such as Pearson correlation, Global Moran’s I, and Local Moran’s I, it was possible to identify not only the strength of these relationships but also their spatial patterns and concentrations across neighborhoods.

The findings confirmed that blight and litter are strongly and positively correlated, with the highest concentrations located in central and southern Memphis. These areas also tend to have lower per capita income and higher proportions of residential land use, reinforcing the idea that environmental degradation and socio-economic vulnerability are spatially linked. In contrast, variables such as crime and industrial land use were not significantly correlated with blight, though they did show moderate clustering in some neighborhoods.

From a spatial planning perspective, this research offers a data-driven framework for identifying priority areas for intervention. The use of spatial statistics at the block group level allowed for the detection of localized environmental burdens, enabling planners and policymakers to allocate resources more equitably and efficiently. By treating blight and litter as interconnected issues rather than separate concerns, cities can develop more holistic and effective neighborhood revitalization strategies.

This research contributes to the literature on urban environmental justice, spatial equity, and data-informed planning, with specific relevance for Memphis and similar post-industrial cities. It also provides a foundation for future research and modeling, particularly multivariate statistical analysis that can further inform targeted interventions and policy development. Ultimately, the study underscores the importance of using spatial tools not just to measure urban problems but to help solve them in more equitable and sustainable ways.