Spatial Units Of Analysis Research Articles

Influence of Built Environment on Micromobility–Pedestrian Accidents

The use of micromobility (MM), a form of sustainable urban mobility which has expected effects such as reducing traffic congestion and greenhouse gases, has been rapidly increasing across the world. However, this growth has resulted in a considerable number of MM-related accidents. Most previous studies have explored MM user injuries to improve the safety of MM users, but the threat to pedestrians by MM is not yet fully understood. Therefore, this study aims to identify built environment factors which contribute to MM–pedestrian accidents by using MM–pedestrian crash data in Seoul, Korea from 2020 to 2021. Setting the spatial unit of analysis as a hexagonal grid with an apothem of 150 m, we developed the SZINB (spatial zero-inflated negative binomial) models for the accidents, controlling spatial autocorrelation, zero-inflated, and overdispersion. The model results showed that road intersections, sidewalks, and subway entrances have significant impacts on MM–pedestrian accidents. Thus, it should be suggested that safety measures for both MM and pedestrians are reducing MM speed limits in intersections, preventing MM use on sidewalks through modified sidewalk designs, and installing MM stations near subway stations.

Contribution of time-series data cubes to classify urban vegetation types by remote sensing

Mapping urban vegetation types is important for urban planning and assessing environmental justice. Nowadays, despite data cubes projects are providing Analysis Ready Data to facilitate time-series analysis, we did not found studies employing these data for improving urban vegetation mapping. By relying solely on open data and software, this work proposes and evaluates the integration of time-series data cubes in a hybrid image classification method to map the intra-urban space, differentiating Tree cover and Herb-shrub. The urban area of Goiânia, Goiás, Brazil, is the study area. The hybrid method combined object-based classification of a pan-sharpened CBERS-4A WPM image (spatial resolution of 2 m) with the pixel-based classification of Sentinel-2 MSI time-series data cubes (10 m). Both approaches used the Random Forest algorithm. Objects from the CBERS-4A segmentation composed the spatial unit of analysis and the class assignment depended on the Sentinel-2 time-series urban land cover probabilities. Based on both Maps probabilities, Shannon entropy was calculated to attribute the final urban land cover to the objects. Urban land cover probabilities presented similar spatial distribution patterns for both classification approaches. Regarding the thematic maps, the Herb-shrub cover area was 35% higher in Sentinel-2 time-series classification than in GEOBIA classification, but Tree cover was 21% lower. In general, 75% of the study area was equally classified by the initial approaches. However, for 9% of the remaining area, the hybrid classification improved vegetation classes accuracies by 35%, contributing to the vegetation covers identification. Thus, this study contributes to methodological procedures for urban land cover study and demonstrates that hybrid maps based on open data are effective to reduce classification mistakes, allowing more accurate monitoring, planning, and designing of different urban vegetation types. Future research efforts should focus on scale compatibility between data of different spatial resolutions and expand the use of data cubes to integrate time-series information into the GEOBIA classification.

The impacts of the modifiable areal unit problem (MAUP) on social equity analysis of public transit reliability

This study aims to examine the degree to which the results of social equity analysis on public transit reliability are sensitive to the choice of spatial unit of analysis (i.e., modifiable areal unit problem (MAUP)). Using the city of Winnipeg as an example, we investigate the social equity of bus on-time performance (OTP), and pass-up distribution at multiple levels (e.g., stop, route, neighborhood) and compare the results. Neighborhoods are classified as minority vs. non-minority population dominant neighborhoods and transit routes and stops are classified into minority vs. non-minority population serving ones according to the socioeconomics of residents. We conduct an equity assessment by calculating 1) the number of pass-ups, 2) the number of bus deviations (e.g., early arrivals, delays) from the predetermined schedule, and 3) the average deviations time (in seconds) that occurred on minority vs. non-minority serving routes/stops as well as in minority vs. non-minority dominant neighborhoods. We also consider different day types (e.g., weekdays, weekends) and disability status (e.g., wheelchair, regular passengers) in the equity assessment. While the route-level results depict that transit service reliability is equitable, results of the neighborhood and stop-level analysis reveal inequities in the distribution of the OTP and pass-ups in the city. Our findings demonstrate that different levels of spatial aggregation can significantly change the results of social equity analysis of public transit reliability, thereby leading to incomplete conclusions that inadequately capture the inequity due to the existence of MAUP. The results of this paper provide insights to transit authorities, planners, and policymakers for diagnosing the social equity landscape of transit reliability in a more robust manner.

Planning of areas of influence of reservoirs. Proposal from land carrying capacity

The starting point of this research is the identification of a normative and technical gap in planning areas of influence of reservoirs for power generation, understood these as water bodies in which an important part of water supply 
 resources is concentrated. In this sense, a description of the problem and the importance of considering hydroelectric megaprojects as spatial analysis units is made; then, the determination of the land carrying capacity is proposed as a methodological tool, since it is considered that it functions as an Accumulative Impact Assessment, inasmuch as it allows to understand the configuration of the territory after the start of operation of the hydroelectric project and, based on this information, become an input for decision-making with technical support, that answer the question of what to do sustainably in those territories.

Nonlinear and threshold effects of the built environment on e-scooter sharing ridership

Understanding the relationship between the built environment and e-scooter sharing (ESS) usage is important because it could help planners determine the high-demand area and design an effective investment plan to promote the use of micromobility. Previous studies usually assume that the relationship is linear, which may lead to inaccurate ridership prediction and ineffective policy. Thus, this study explores the nonlinear and threshold effects of the built environment on ESS ridership in Los Angeles using the gradient boosting decision tree. Fourteen built environment and ten demographic variables are selected as independent variables. We find that the built environment accounts for 91.66% of the total relative importance. The ten most important variables are intersection density, road density, public transit station density, restaurant density, employment density, distance to the center, population density, proportion of park area, parking density, and bike lane density. The nonlinear and threshold effects of the built environment on ESS ridership are determined. By using two spatial analysis units (census tract and census block group) and four temporal analysis units (spring, summer, autumn, and winter), the modifiable areal unit problem and the modifiable temporal unit problem are revealed.

Mapping the Risk of COVID-19 Contagion at Urban Scale

From the beginning of the COVID-19 pandemic, several methods have been developed to assess the risk of COVID-19 transmission using spatial units of analysis based on administrative limits (e.g., neighborhoods, census sections, and districts). The main objective of this study is to create a method to assess the risk of contagion within an interurban scale, considering buildings as the smallest unit of analysis. The general risk equation has been the basis to develop the method, individually assessing its components (i.e., hazard, vulnerability, and exposure). Several mapping tools that address the management of the risk of contagion have been proposed, and the main result was the detection of a pattern of contagion and the identification of areas where the risk of contagion was greater. Additionally, the comparison of the risk of a contagion pattern and the population size at an intraurban scale allowed for inferring the specific vulnerability of the population to contagion. The results also showed that there was a direct relation between the risk of contagion and population density, as well as the presence of areas especially vulnerable to contagion.

Open data on Covid-19 in the Spanish autonomous communities: reutilization in spatial epidemiology studies

The Covid-19 pandemic has highlighted the need for governments and health administrations at all levels to have an open data registry that facilitates decision-making in the planning and management of health resources and provides information to citizens on the evolution of the epidemic. The concept of “open data” includes the possibility of reutilization by third parties. Space and time are basic dimensions used to structure and interpret the data of the variables that refer to the health status of the people themselves. Hence, the main objective of this study is to evaluate whether the autonomous communities’ data files regarding Covid-19 are reusable to analyze the evolution of the disease in basic spatial and temporal analysis units at the regional and national levels. To this end, open data files containing the number of diagnosed cases of Covid-19 distributed in basic health or administrative spatial units and temporal units were selected from the portals of the Spanish autonomous communities. The presence of infection-related, demographic, and temporal variables, as well as the download format and metadata, were mainly evaluated. Whether the structure of the files was homogeneous and adequate for the application of spatial analysis techniques was also analyzed. The results reveal a lack of standardization in the collection of data in both spatial and temporal units and an absence of, or ambiguity in, the meaning of the variables owing to a lack of metadata. An inadequate structure was also found in the files of seven autonomous communities, which would require subsequent processing of the data to enable their reuse and the application of analysis and spatial modeling techniques, both when carrying out global analyses and when comparing patterns of evolution between different regions.

Refined Urban Functional Zone Mapping by Integrating Open-Source Data

The determination of a reasonable spatial analysis unit is an essential step in urban functional zone (UFZ) division, which significantly affects the results. However, most studies on the division of functional zones are based on excessively large spatial units, such as blocks or traffic analysis zones (TAZs), which easily overlook the detailed characteristics of urban regions and introduce bias to the research conclusion. To address this issue, a refined zone segmentation method, namely, the Voronoi diagram for the polygon method, was proposed to generate refined spatial analysis units. Afterward, the functional topics of the spatial analysis unit were classified by a multiclass support vector machine (SVM) to produce the final UFZ map, where the functional topics of each spatial unit were obtained by coupling latent Dirichlet allocation (LDA). To verify the effectiveness of the proposed method, experiments were conducted in Beijing, China. The results indicated that the proposed segmentation method can generate fine-scale spatial units and provide fine-grained and higher accuracy UFZs (overall accuracy = 84%; kappa = 0.82).

Characterizing mobility patterns of private electric vehicle users with trajectory data

Human mobility pattern analysis has received rising attention. However, little is known about the mobility patterns of private Electric Vehicle (EV) users. In response, this paper characterized mobility patterns of private EV users using a unique one-month dataset containing moving trajectories of 76,774 actual private EVs in January 2018 in Beijing. Specifically, we first explored the diversity, regularity, spatial extent, and uniqueness of EV users’ mobility patterns. The results suggested that most EV users had both regular travel and activity patterns (the mean travel and activity entropies were 2.17 and 1.83, respectively) with special preferences towards some specific activity locations relative to all the locations they visited (the mean number of activity locations visited was 13.57 in one month). Furthermore, they tended to perform activities within a small geographical area (the mean radius of gyration was 7.60 km) and have a short daily travel distance (the mean value was 37.35 km) relative to their electric driving range. Further, we associated EV users’ mobility patterns with the built environment through ordinary least squares and geographically weighted regression models, particularly considering the so-called modifiable areal unit problem (MAUP). Due to the MAUP, most of the statistically significant built environment variables varied across spatial analysis units (SAUs). Gymnasia was the only variable statistically associated with the mobility patterns for all SAUs; while the variables related to residence and workplace were not statistically associated.

Spatial distribution of stroke readmission within 30 days and the influencing factors in Hunan Province.

Stroke readmission increases financial burden on the family and the consumption of medical resources, and 30-day readmission rate is an important indicator for quality evaluation on health services. The influential factors for readmission mainly include patient-related factors, hospital factors, and society-related factors, with regional differences. This study aims to explore the spatial distribution and its main relevant factors for 30-day readmission of stroke patients in Hunan Province, and to provide the useful information for the improvement of regional prevention and control of stroke readmission. Stroke patients in Hunan Province who were hospitalized in 2018 and readmitted within 30 days were included in the study. The vector map of the county boundary in Hunan Province was used as the basic map since county was the spatial analysis unit. SPSS 26.0 and ArcGIS 10.8 were used for statistical analysis that contains descriptive analysis of the general situation and the distribution map of readmission rate within 30 days of stroke patients. Spatial autocorrelation analysis and spatial regression analysis were further used to find the spatial clusters of the 30-day readmission rate of stroke and the local relationship between the readmission rate and main influential factors. In 2018, a total of 172 800 stroke patients were hospitalized in Hunan Province, of which 6 953 patients were re-hospitalized within 30 days after discharging due to stroke. The 30-day readmission rate was 4.09% in Hunan Province. The clusters of stroke readmission rates were mainly concentrated in the northeast and western regions in Hunan Province. The geographically weighted regression revealed that proportion of patients with complications, number of hospitals per 10 000 population and number of primary medical and health care institution per 10 000 population were the main relevant factors for stroke readmission, and there were differences both in the direction and degree of the effect on readmission in different regions. The 30-day readmission rate for stroke patients in Hunan province and its main influential factors had spatial heterogeneity. The key prevention and control areas were mainly concentrated in the northeast and western regions. It is recommended that the prevention and treatment of stroke complications and the construction of medical institutions need to be strengthened to improve the quality of medical services, particularly in the western region. The importance to the treatment of stroke complications should be attached in the northern region, and the primary health care should be reinforced in the northeast region. All counties should take prevention and control measures according to local conditions, so as to effectively control the readmission rate of stroke within 30 days.

The mutual specialization of port and urban functions: The case of France

AbstractThis research examines the similarities between port traffic structure and economic structure of French port cities. Based on the combination of Automated Identification System (AIS) data and employment data, it performs complementary analyses of the mutual specialization between ports and cities. Main results show that while larger cities handle more diversified traffic, the cross‐specialization is blurred by the complexity of trade networks and supply chains. We then propose a novel methodology whereby the spatial unit of analysis is enlarged according to the type and volume of port traffic.

Does green space influence assaults? Evidence from Toronto, Canada

Criminal behavior has been traditionally linked to green space. However, studies on the relationship between green space and crime vary in their conclusions and even contradict each other. This variability is primarily attributed to the mixing-up of specific crimes and the less-explored influencing mechanisms. This study considers Toronto and focuses on a specific kind of crime, assault, for exploring whether green space influences the neighborhood assault rate. If so, then how does it affect the assault rate? By considering the neighborhood as the spatial analysis unit, the indicators of green space and its component structure (namely the composition of trees, shrubs, and grass) are included in the regression analysis model used in this study. The dual effect (both positive and negative) of walkability on crime is treated as the moderator in the regression model. Our analysis revealed that high percentages of green space, tree area, and grass area potentially inhibit assault occurrence, while the shrub area percentage has no statistically significant association with the assault rate. When walkability reaches a certain degree (84th or/and 50th percentiles in this study), it can enhance the inhibitory impact of the green space and its component structure (including trees, shrubs, and grass) on the assault rate. Comparatively, this reduction effect is more significant for green space composed of grass. Therefore, we can infer that increasing the percentage of trees and grass, while simultaneously improving walkability, can reduce residents’ risk of being assaulted at the neighborhood level.

The modifiable areal unit problem in the relationship between jobs–housing balance and commuting distance through big and traditional data

Jobs–housing balance aims to reduce commuting distance and alleviate traffic congestion as a transport policy. However, the measurement of jobs–housing balance is affected by the spatial areal unit employed in analysis and planning. The mechanism by which commuting distance is affected by jobs–housing balance remains controversial for several reasons, including inconsistency of spatial analysis units and whether socio-demographic attributes are controlled. Cellphone data exhibit superiority in identifying the journey-to-work trips for large sample sizes and entail high spatial precision that can be integrated into different analysis units. By combining cellphone data and traditional survey data with detailed socio-demographic attributes aggregated at different analysis units, this study examines the scale and zoning issues of jobs–housing relationships in Shanghai, China. When using large analysis units, the effect of jobs–housing balance was expanded and that of different socio-demographic attributes changed variously and unpredictably. Furthermore, the uniformity of the spatial analysis unit influenced the effect of jobs–housing balance on commuting distance. This study substantiates the effect of the modifiable areal unit problem (MAUP) on the relationship between jobs–housing balance and commuting distance considering socio-demographic attributes and demonstrates that the changing rules of the effects of that balance on the commuting distance are different from those of socio-demographic attributes as the scale changes. Consequently, this work enriches the literature on the MAUP in relation to the jobs–housing balance and obtains a thorough understanding of the MAUP effects through combining big and traditional data.

“The grass is greener on the other side”: The relationship between the Brexit referendum results and spatial inequalities at the local level

AbstractDespite seven decades of development of the European Union project, on 23 June 2016, the United Kingdom, Europe and the rest of the world were surprised when the Leave campaign won the Brexit referendum, offering an extraordinary case study for researchers. We spatially disaggregate the vote share data, which allows us to explore where anti‐European sentiment took root in the UK and why. In this paper, a spatial dependence model is applied to clarify and quantify the relevance of the different dimensions—demographic, cultural/educational and economic—that play a role in explaining the rise of support for the Leave campaign. The analysis is conducted at the local level, using local authorities (LAs) as the spatial unit of analysis due to the combination of official datasets with newly generated data in the context of an EU H2020 project. A new indicator capturing the affluence of each local area relative to its close neighbours is proposed and included in the model. In general, we observe that most of the main conclusions obtained by large regions or at the national level also hold at the local scale. However, it is particularly interesting that inequalities by LAs are clearly significant, indicating a marked influence on voters' decisions that have thus far been unaccounted for. This result provides further support for the existence of, to use Andrés Rodriguez‐Pose's terminology, an even more intense “revenge of the places that do not matter” at the local scale.

Effects of Coverage Area Treatment, Spatial Analysis Unit, and Regression Model on the Results of Station-Level Demand Modeling of Urban Rail Transit

Direct ridership models can predict station-level urban rail transit ridership. Previous research indicates that the direct modeling of urban rail transit ridership uses different coverage overlapping area processing methods (such as naive method or Thiessen polygons), area analysis units (such as census block group and census tract), and various regression models (such as linear regression and negative binomial regression). However, the selection of these methods and models seems arbitrary. The objective of this research is to suggest methods of station-level urban rail transit ridership model selection and evaluate the impact of this selection on ridership model results and prediction accuracy. Urban rail transit ridership data in 2010 were collected from five cities: New York, San Francisco, Chicago, Philadelphia, and Boston. Using the built environment characteristics as the independent variables and station-level ridership as the dependent variable, an analysis was conducted to examine the differences in the model performance in ridership prediction. Our results show that a large overlap of circular coverage areas will greatly affect the accuracy of models. The equal division method increases model accuracy significantly. Most models show that the generalized additive models have lower mean absolute percentage errors (MAPE) and higher adjusted R 2 values. By comparison, the Akaike information criterion (AIC) values of the negative binomial models are lower. The influence of different basic spatial analysis unit on the model results is marginal. Therefore, the selection of basic area unit can use existing data. In terms of model selection, advanced models seem to perform better than the linear regression models.

Clustering of childhood cancer in Colombia: a nationwide study

BACKGROUND AND AIM: Childhood cancer is considered as one the most important causes of death in children and adolescents, despite having a low incidence in this population. Spatial analysis has been previously used for the study of childhood cancer to study the geographical distribution of leukemias. This study aimed to identify the presence of space-time clusters of childhood of cancer excluding leukemia in Colombia between 2014 and 2017. METHODS: All incident cancer cases excluding leukemia in children under the age of 15 years that have confirmed by the National Surveillance System of Childhood Cancer between 2014 and 2017 were included. Kulldorf’s circular scan test was used to identify clusters using the municipality of residence as the spatial unit of analysis and the year of diagnosis as the temporal unit of analysis. A sensitivity analysis was conducted with different upper limit parameters for the at-risk population. RESULTS:A total of 2006 cases of non-leukemia childhood cancer were analyzed, distributed in 432 municipalities with a mean annual incidence rate of 44 cases per million children under the age of 15. Central nervous system (CNS) tumors were the most frequent type. Four spatial clusters and two space-time clusters were identified in the central and southwest regions of the country. In the analysis for CNS tumors, a spatial cluster was identified in the central region of the country. CONCLUSIONS:The distribution of non-leukemia childhood cancer seems to have a clustered distribution in some Colombian regions that may suggest infectious or environmental factors associated with its incidence. KEYWORDS: Outcomes, Children's environmental health, Spatial statistics

Spatial patterns of fetal mortality: scenario before and after the implementation of surveillance

Abstract Objectives: to analyze the spatial distribution of fetal deaths before and after implementation of surveillance for this event in the city of Recife, in the Northeast Region of Brazil. Methods: an ecological study whose spatial analysis unit was the 94 neighborhoods. The gross fetal mortality rates were calculated and the local empirical Bayesian estimator was adopted to smooth out random fluctuations of such rates. To analyze the spatial autocorrelation, the Global Moran’s Index was used, and spatial clusters were located by the Local Moran’s Index. Results: during the period before implementation of death surveillance, 1,356 fetal deaths were reported, a coefficient of 9.9 deaths per thousand births. During the second period, 1,325 fetal deaths occurred, a coefficient of 9.6 deaths per thousand births. The Global Moran’s Indexes (I) were I=0.6 and I=0.4 for the first and second periods, respectively, with statistical significance (p<0.05). For both periods analyzed, spatial clusters of high-risk neighborhoods were identified in the northern and eastern regions of the city. Conclusion: the spatial analysis indicated areas that persist as priorities for planning surveillance and health assistance actions to reduce fetal mortality.

Clustering of non-leukemia childhood cancer in Colombia: a nationwide study

Background: Childhood cancer is considered one the most important causes of death in children and adolescents, despite having a low incidence in this population. Spatial analysis has been previously used for the study of childhood cancer to study the geographical distribution of leukemias. This study aimed to identify the presence of space-time clusters of childhood of cancer excluding leukemia in Colombia between 2014 and 2017. Methods: All incident cancer cases (excluding leukemia) in children under the age of 15 years that had been confirmed by the National Surveillance System of Childhood Cancer between 2014 and 2017 were included. Kulldorf’s circular scan test was used to identify clusters using the municipality of residence as the spatial unit of analysis and the year of diagnosis as the temporal unit of analysis. A sensitivity analysis was conducted with different upper limit parameters for the at-risk population in the clusters. Results: A total of 2006 cases of non-leukemia childhood cancer were analyzed, distributed in 432 out of 1,122 municipalities with a mean annual incidence rate of 44 cases per million children under the age of 15. Central nervous system (CNS) tumors were the most frequent type. Two space-time clusters were identified in the central and southwest regions of the country. In the analysis for CNS tumors, a spatial cluster was identified in the central region of the country. Conclusions: The distribution of non-leukemia childhood cancer seems to have a clustered distribution in some Colombian regions that may suggest infectious or environmental factors associated with its incidence although heterogeneity in access to diagnosis cannot be discarded.

PAISAGENS FLUVIAIS E PEIXES DE RIACHO: UMA INTRODUÇÃO AOS DADOS, UNIDADES ESPACIAIS DE ANÁLISE E CLASSIFICAÇÃO

Research on riverscapes and stream fish ecology has undergone a wide progress since the 1990s. Several conditions have been pointed out as essential in this progress, including the following four: (a) availability of geo-technology and spatial data, (b) setting the regional context of study areas (biomes, large river basins, ecoregions); (c) defining hierarchical spatial units of analysis (watersheds, segments, reaches) and their attributes (e.g., area, slope, order, perennial/intermittent), and (d) classification of spatial units according to their attributes. Here we present an introduction to these topics, using examples from studies in Brazilian streams, where research progress on riverscapes and stream fish has occurred only more recently. We identify a few challenges in the Brazilian context, including the standardization and consolidation of regional and national spatial databases that support riverscape analyses, training fish ecologists in Geographic Information Systems (GIS) and spatial data, expanding the use of classification systems in different spatial coverages and resolutions, research on structural, spatial and temporal attributes of spatial units in riverscape analyses

Comparing airborne and spaceborne photon-counting LiDAR canopy structural estimates across different boreal forest types

The monitoring of forested ecosystems relies on an accurate description of forest structure. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2), launched in September 2018, carries the Advanced Topographic Laser Altimeter System (ATLAS), a Light Detection and Ranging (LiDAR) instrument capable of detecting individual photons reflected back from vegetation canopy. ICESat-2 data is delivering global estimates of forest structure; however, analysis of the performance of ICESat-2 on-orbit data across a range of forest conditions remains limited. This study derives structural estimates of (i) canopy height, (ii) canopy cover and (iii) canopy height variability from ICESat-2 data acquired in snow-free and low atmospheric scattering conditions over different boreal forest structural types in Ontario, Canada. ICESat-2 structural estimates were derived from the Global Geolocated Photon Data (ATL03) and Land and Vegetation Height (ATL08) data products and compared against single-photon detection airborne LiDAR (Leica SPL100). An extensive network of ground plots were used to stratify the study area into three distinct forest structural groups, each resulting from different stand development stages. ICESat-2 and SPL100 estimates of canopy height were compared at the ATL03 photon level, whereas estimates of height variability and canopy cover were compared for spatial analysis units (AU; mean size = 1287 m2). ICESat-2 photons returned from the top of the canopy underestimated canopy height relative to SPL100 by an average of 2.3 m overall and corresponded most strongly to the 90th percentile (P90) of coincident airborne SPL100 returns (root mean square difference (RMSD) = 2.9 m and correlation coefficient (r) = 0.84). The lowest average underestimation of SPL P90 was observed in homogeneous stands that were relatively simple, and single-layered with a single dominant species (RMSD = 2.5 m, r = 0.84). We observed the least agreement of ICESat-2 and SPL forest structural metrics in over-mature stands with complex structure and greater variability in canopy heights (RMSD = 3.5 m, r = 0.64). For the AUs, the strength of the relationship between SPL100 and ICESat-2 canopy height percentiles increased with increasing height percentiles (e.g. P25 RMSD% = 77.8%; P95 RMSD% = 23.7%). ICESat-2 generally underestimated canopy height variability relative to the SPL100 data, with both data having similar absolute variability (standard deviation of canopy heights RMSD% = 26.8%, r = 0.75), but lower agreement in relative variability (coefficient of variation of canopy heights RMSD% = 33.9%, r = 0.45). Herein we propose the use of the vegetation fill index as a method to estimate canopy cover with ICESat-2. Comparison of SPL100 and ICESat-2 vegetation fill indices at the AU level resulted in strong agreement overall (RMSD% = 19.7%; r = 0.57). These observations and results contribute to the overall objective of building a comprehensive understanding of the performance of ICESat-2 for characterizing vegetation structure in boreal forest environments.