# Abstract

The built environment possessed determinants of more active lifestyles, related to social and cultural reality. Thus, relevant walkability variables in large cities and in developed countries may not be suitable for mid-sized Brazilian towns. Therefore, from a case study, the objective of this research was to evaluate the relevance of eight objective walkability variables: Residential Density; Retail Floor Area Ratio; Mixed Land Use (Entropy); Space Syntax - Integration and Choice; Land and Real Estate values in a case study of a mid-sized Brazilian town. From the geocoding of data from the Municipal Urban Mobility Plan, urban form variables were aggregated and tested in 1000 meter network buffers. Analyzes were performed using a machine learning approach, through the Random Forest algorithm, in relation to self-reported walking (meters walked per unit of area). Results indicated that the most relevant characteristics were: Entropy, Integration within a 2000 meter radius and Residential Density. Contributions include the possibility of subsidizing urban planning policies in adopting an evidence-based approach.

Keywords:
Built environment; Walkability; Active transport; Urban health

Palavras-chave:

# Introduction

According to the World Health Organization, non-communicable diseases such as cardiovascular diseases, hypertension, and type 2 diabetes represent a threat to human development, and the susceptibility to them increases due to physical inactivity (PI) (WORLD…, 2017WORLD HEALTH ORGANIZATION. Noncommunicable diseases progress monitor 2017. 2017. ). Facing the prevalence of PI worldwide and its negative effects on health (DUMITH et al., 2011DUMITH, S. C. et al. Worldwide prevalence of physical inactivity and its association with human development index in 76 countries. Preventive Medicine, v. 53, n. 1/2, p. 24-28, 2011. ), understanding aspects that influence active behaviors is paramount. Notwithstanding, active behaviors are shaped by different factors, levels of determinants and their interactions (BAUMAN et al., 2012BAUMAN, A. E. et al. Correlates of physical activity: why are some people physically active and others not? The Lancet, v. 380, n. 9838, p. 258-271, 2012. ). Individual variables are widely studied (SALLIS et al., 2016SALLIS, J. F. et al. Use of science to guide city planning policy and practice: how to achieve healthy and sustainable future cities. The Lancet, v. 388, n. 10062, p. 2936-2947, 2016. ) whereas environmental factors are less researched, despite their recognized effects on behavior (BAUMAN et al., 2012BAUMAN, A. E. et al. Correlates of physical activity: why are some people physically active and others not? The Lancet, v. 380, n. 9838, p. 258-271, 2012. ). With the growing burdens of motorized transportation (MURRAY; LOPEZ; CAMBRIDGE, 2014MURRAY, C.; LOPEZ, A. D.; CAMBRIDGE, M. A. Transport for health: the global burden of disease from motorized road transport.The world bank; Institute for heath metrics and Evaluation. Seattle, 2014.) urban qualities have lead researchers to gain understanding of urban form’s influence on travel behavior (CAMPOLI, 2012CAMPOLI, J. Made for walking: density and neighborhood form. 2. ed. Cambridge: Lincoln Institute of Land Policy, 2012.). They have been centered as important emerging topics in the dialogue concerning sustainability and as the core of city planning strategies of developed countries (GILDERBLOOM; RIGGS; MEARES, 2015GILDERBLOOM, J. I.; RIGGS, W. W.; MEARES, W. L. Does walkability matter? An examination of walkability’s impact on housing values, foreclosures and crime. Cities, v. 42, n. PA, p. 13-24, 2015.). One of the strategies to evaluate the built environment (BE) is walkability, defined as “[…] the extent to which characteristics of the built environment may or may not be conducive to walking for either leisure, exercise or recreation, to access services, or to travel to work […]” (LESLIE et al., 2007LESLIE, E. et al. Objectively assessing walkability’ of local communities: using GIS to identify the relevant environmental attributes. Berlin, Heidelberg: Springer, 2007. , p. 91). Besides contributing to health, walking is at the core of sustainable mobility, reducing motorized transportation and minimizing environmental impacts. Walking demands fewer resources than other means of transportation, it is cheap, silent, and non-polluting (GEHL, 2013GEHL, J. Cidade para pessoas. 2. ed. São Paulo: Editora Perspectiva, 2013. ).

In low and middle-income countries, studies on environmental correlates of walking are urgently needed (BAUMAN et al., 2012BAUMAN, A. E. et al. Correlates of physical activity: why are some people physically active and others not? The Lancet, v. 380, n. 9838, p. 258-271, 2012. ) to attenuate the rapidly changing determinants of PI that occur due to urbanization, passive entertainment, and motorized transport. Thus, the need for a better understanding of urban mobility patterns in Brazilian cities is evident. Therefore, this research tackles the phenomenon of the BE as support for walking through the study of objective walkability variables in Brazilian cities.

These cities present themselves very differently from high-income countries, in spatial, functional, socio-economic and environmental qualities (CARMONA et al., 2010CARMONA, M. et al. Public urban spaces: the dimensions of urban design. Oxford: Architectural Press, 2010. ). Such differences emphasize the need for context-specific studies in designing and implementing environmental strategies to increase physical activity (PA) levels (SALVO et al., 2014SALVO, D. et al. Characteristics of the built environment in relation to objectively measured physical activity among Mexican adults, 2011. Preventing Chronic Disease, v. 11, 2014. ).

Such research interest is made even more relevant in cities where nonmotorized transportation is largely present and public transport is less used (ASSOCIAÇÃO…, 2018ASSOCIAÇÃO NACIONAL DE TRANSPORTES PÚBLICOS. Sistema de informações da mobilidade urbana: relatório geral 2016. 2018. Disponível em: http://files.antp.org.br/simob/simob-2016-v6.pdf. Acesso em: 3 maio. 2019.
http://files.antp.org.br/simob/simob-201...
). Brazil has most of its cities represented by an average of 5 to 100 thousand inhabitants (INSTITUTO…, 2015INSTITUTO BRASILEIRO DE GEOGRAFIA E ESTATÍSTICA. Estimativas populacionais dos municípios em 2015. 2015. Disponível em: http://saladeimprensa.ibge.gov.br/noticias?view=noticia&id=1&busca=1&idnoticia=2972. Acesso em: 25 jul. 2017.
). According to the Associação Nacional de Transportes Públicos, active travel by foot is inversely proportional to the dimension of the city - the smaller the city, the higher the rates of active travel (ASSOCIAÇÃO…, 2018ASSOCIAÇÃO NACIONAL DE TRANSPORTES PÚBLICOS. Sistema de informações da mobilidade urbana: relatório geral 2016. 2018. Disponível em: http://files.antp.org.br/simob/simob-2016-v6.pdf. Acesso em: 3 maio. 2019.
http://files.antp.org.br/simob/simob-201...
). Notwithstanding, there is a lack of studies on walkability in medium and small-sized Brazilian cities (MOTOMURA et al., 2018MOTOMURA, M. C. et al. Understanding walkable areas: applicability and analysis of a walkability index in a Brazilian city. Ambiente Construído, Porto Alegre, v. 18, n. 4, p. 413-425, out./dez. 2018.).

Considering the above-presented topics, the need for a greater understanding of active travel patterns in Brazilian cities is evident for tailored mobility policies. Therefore, the main objective of this research is to evaluate the relevance of objective walkability variables of the BE in a mid-size Brazilian city. To that end, urban form walkability-related characteristics were tested through a comparison with self-reported travel behaviors. This work has the theoretical assumption that when comparing objective walkability variables to travel behaviors on mid-size Brazilian cities it would be possible to uncover the specific variables that influence walking in Brazilian cities.

# Background

The benefits of walking are widely recognized, it is more than a utilitarian mean of transportation. It holds social, recreational and cultural values (SOUTHWORTH, 2005SOUTHWORTH, M. Designing the walkable city. Journal of Urban Planning and Development, v. 131, n. 4, p. 246-257, 2005. ). Walking is the most equitable, accessible and available mean of transportation (ORELLANA; HERMIDA; OSORIO, 2016ORELLANA, D.; HERMIDA, C.; OSORIO, P. A multidisciplinary analytical framework for studying active mobility patterns. In: GONGRESS OF INTERNATIONAL ARCHIVES OF THE PHOTOGRAMMETRY, REMOTE SENSING AND SPATIAL INFORMATION SCIENCES, 23., Prague, 2016. Proceddings […] Prague, 2016. ). The BE is an influence able to facilitate or hinder walking behaviors (SAELENS; HANDY, 2008SAELENS, B. E.; HANDY, S. L. Built environment correlates of walking: a review. Medicine and Science in Sports and Exercise, v. 40, n. 7, p. 550-566, supp. 1, 2008. ).

One of the strategies to evaluate BE for supporting a more active daily life are walkability indices, being that the most widespread one was proposed by Frank et al. (2010)FRANK, L. D. et al. The development of a walkability index: application to the Neighborhood Quality of Life Study. British Journal of Sports Medicine, v. 44, n. 13, p. 924-933, 2010. . It is based on an equation that represents walkability considering four variables: Residential Density, Land use mix (Entropy), Intersection Density, and Retail Floor Area Ratio.

Residential density is considered paramount for shorter and more convenient walking trips. and have positive effects on utilitarian walking, land use balance and street connectivity (SAELENS; SALLIS; FRANK, 2003SAELENS, B. E.; SALLIS, J. F.; FRANK, L. D. Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning literatures. Annals of Behavioral Medicine, v. 25, n. 2, p. 80-91, 2003. ). The development of higher population densities is one of the factors that can reduce the number of motorized trips and increase the number of walking trips (CERVERO; KOCKELMAN, 1997CERVERO, R.; KOCKELMAN, K. Travel demand and the 3Ds: density, diversity, and design. Transportation Research Part D: Transport and Environment, v. 2, n. 3, p. 199-219, 1997. ). It has been the basis of neighborhoods designed for sustainability with the purpose of housing enough people to be able to support urban services such as local shops, schools and public transport. Even though compact-high density development is encouraged in contemporary urban planning, it often conflicts with sociocultural contexts (CARMONA et al., 2010CARMONA, M. et al. Public urban spaces: the dimensions of urban design. Oxford: Architectural Press, 2010. ), especially in middle-income countries. Seeking optimal densities for development thus remains one of the most challenging of the sustainable urban design principles.

Land-use mix can be seen as a complement to residential density, aiming to quantify the heterogeneity of land uses (DUNCAN et al., 2010DUNCAN, M. J. et al. Relationships of land use mix with walking for transport: do land uses and geographical scale matter? Journal of Urban Health, v. 87, n. 5, p. 782-795, 2010. ). Such attribute has been shown to be associated with walking and other PA behaviors (FRANK et al., 2005FRANK, L. D. et al. Linking objectively measured physical activity with objectively measured urban form: Findings from SMARTRAQ. American Journal of Preventive Medicine, v. 28, n. 2, p. 117-125, supp. 2, 2005. ). In neighborhoods with a greater mix of uses, utilitarian destinations are within a shorter reach from residences, increasing the convenience for walking (SAELENS; SALLIS; FRANK, 2003SAELENS, B. E.; SALLIS, J. F.; FRANK, L. D. Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning literatures. Annals of Behavioral Medicine, v. 25, n. 2, p. 80-91, 2003. ). Currently, land use mix is a walkability variable most often assessed through a variation of the Shannon Entropy equations (FRANK; ANDRESEN; SCHMID, 2004FRANK, L. D.; ANDRESEN, M. A.; SCHMID, T. L. Obesity relationships with community design, physical activity, and time spent in cars. American Journal of Preventive Medicine, v. 27, n. 2, p. 87-96, 2004. ; GEBEL; BAUMAN; OWEN, 2009GEBEL, K.; BAUMAN, A.; OWEN, N. Correlates of Non-Concordance between Perceived and objective measures of walkability. Annals of Behavioral Medicine, v. 37, n. 2, p. 228-238, 2009. ; GRASSER; TITZE; STRONEGGER, 2016GRASSER, G.; TITZE, S.; STRONEGGER, W. J. Are residents of high-walkable areas satisfied with their neighbourhood? Journal of Public Health, v. 24, n. 6, p. 469, 2016..) which represents the extent of variation in the distribution of land uses. However, in some studies, land use mix has not been found to be associated with PA behaviors (FORSYTH et al., 2008FORSYTH, A. et al. Design and destinations: factors influencing walking and total physical activity. Urban Studies, v. 45, n. 9, p. 1973-1996, 2008. ; GRASSER et al., 2013GRASSER, G. et al. Objectively measured walkability and active transport and weight-related outcomes in adults: a systematic review. International Journal of Public Health, v. 58, n. 4, p. 615-625, 2013. ; MCCORMACK; SHIELL, 2011MCCORMACK, G. R.; SHIELL, A. In search of causality: a systematic review of the relationship between the built environment and physical activity among adults. The International Journal of Behavioral Nutrition and Physical Activity, v. 8, p. 125, 2011. ). Such inconsistent findings may be partly due to the lack of specificity in the land use categories considered (DUNCAN et al., 2010DUNCAN, M. J. et al. Relationships of land use mix with walking for transport: do land uses and geographical scale matter? Journal of Urban Health, v. 87, n. 5, p. 782-795, 2010. ).

A walkability measure naturally connected to land use mix is Retail Floor Area Ratio (FAR), which is the ratio or the sum of commercial building floor area to the total commercially used land area (FRANK et al., 2010FRANK, L. D. et al. The development of a walkability index: application to the Neighborhood Quality of Life Study. British Journal of Sports Medicine, v. 44, n. 13, p. 924-933, 2010. ). It was created as a reflection of more options for destinations where goods and services may be purchased (LESLIE et al., 2007LESLIE, E. et al. Objectively assessing walkability’ of local communities: using GIS to identify the relevant environmental attributes. Berlin, Heidelberg: Springer, 2007. ), but more importantly as a measure of pedestrian-oriented community design. Retail parcels with a high retail floor area ratio may be less likely to have the ‘pedestrian-unfriendly’ design with large hostile parking lots (CERVERO; KOCKELMAN, 1997CERVERO, R.; KOCKELMAN, K. Travel demand and the 3Ds: density, diversity, and design. Transportation Research Part D: Transport and Environment, v. 2, n. 3, p. 199-219, 1997. ). This measure is greatly linked to large retail chains and shopping malls from North American cities. It has even been considered only for large retail activities with three or more shops or a single shop of 250 square meters or larger (LESLIE et al., 2007LESLIE, E. et al. Objectively assessing walkability’ of local communities: using GIS to identify the relevant environmental attributes. Berlin, Heidelberg: Springer, 2007. ). It can be interpreted as a derivation of the early metrics of parking ratios, that indicated the relationship between the space allotted for parking and the space occupied by retail buildings (GIBBS, 2012GIBBS, R. Principles of urban retail planning & development. New Jersey: Jon Wiley and Sons, 2012. ).

Another fundamental walkability measure, street connectivity quantifies the linkage between destinations. It is argued that connectivity is an urban design measure that underpins walkable neighborhoods (KOOHSARI et al., 2016aKOOHSARI, M. J. et al. Walkability and walking for transport: characterizing the built environment using space syntax. International Journal of Behavioral Nutrition and Physical Activity, v. 13, n. 1, 2016a. ). Connected street networks provide more direct routes to destinations (FRANK et al., 2010FRANK, L. D. et al. The development of a walkability index: application to the Neighborhood Quality of Life Study. British Journal of Sports Medicine, v. 44, n. 13, p. 924-933, 2010. ), being a prerequisite for increasing pedestrian activity (ELLIS et al., 2015ELLIS, G. et al. Connectivity and physical activity : using footpath networks to measure the walkability of built environments. Environment and Planning B: Planning and Design, v. 42, n. 1, p. 1-22, 2015. ). Such importance is supported by several empirical findings that indicate consistent positive associations between walking, especially for transport, and street connectivity (BERRIGAN; PICKLE; DILL, 2010BERRIGAN, D.; PICKLE, L. W.; DILL, J. Associations between street connectivity and active transportation. International Journal of Health Geographics, v. 9, n. 1, p. 20, 2010. ; OAKES; FORSYTH; SCHMITZ, 2007OAKES, J. M.; FORSYTH, A.; SCHMITZ, K. H. The effects of neighborhood density and street connectivity on walking behavior: the twin cities walking study. Epidemiologic Perspectives & Innovations, v. 4, n. 16, 2007.; SUGIYAMA et al., 2012SUGIYAMA, T. et al. Destination and route attributes associated with adults’ walking: a review. Medicine And Science In Sports And Exercise, v. 44, n. 7, p. 1275-1286, 2012. ). Street connectivity is commonly operationalized as the quantification of intersection density by unit area. It is often represented by the mean block size per area, indicating the average distance between intersection (ELLIS et al., 2015ELLIS, G. et al. Connectivity and physical activity : using footpath networks to measure the walkability of built environments. Environment and Planning B: Planning and Design, v. 42, n. 1, p. 1-22, 2015. ).

Apart from potential environmental, social and individual benefits, walkable neighborhoods have been linked to a naturally occurring increase in property values (GUO; PEETA; SOMENAHALLI, 2017GUO, Y.; PEETA, S.; SOMENAHALLI, S. The Impact of walkable environment on single family residential property values. Journal of Transport and Land Use, v. 10, n. 1, p. 241-261, 2017. ). More walkable areas tend to be more developed and consequently closer to amenities. Such amenities only come to be where their price is sufficiently valued (BOYLE; BARRILLEAUX; SCHELLER, 2014BOYLE, A.; BARRILLEAUX, C.; SCHELLER, D. Does walkability influence housing prices? Social Science Quarterly, v. 95, n. 3, p. 852-867, 2014. ). Hence, walkability has an important connection to urban economies. Therefore, an important aspect to be considered is the ample evidence linking land and real estate values to walkability (GUO; PEETA; SOMENAHALLI, 2017GUO, Y.; PEETA, S.; SOMENAHALLI, S. The Impact of walkable environment on single family residential property values. Journal of Transport and Land Use, v. 10, n. 1, p. 241-261, 2017. ; MATTHEWS; TURNBULL, 2007MATTHEWS, J. W.; TURNBULL, G. K. Neighborhood street layout and property value: the interaction of accessibility and land use mix. Journal of Real Estate Finance and Economics, v. 35, n. 2, p. 111-141, 2007. ; RAUTERKUS; MILLER, 2011RAUTERKUS, S. Y.; MILLER, N. G. Residential land values and walkability. Journal of Sustainable Real Estate, v. 3, n. 1, p. 23-43, 2011. ). Neighborhoods closer to centralities and established in older settlements have been found to be more walkable and more economically valued. Taking such evidence into account, it is safe to conclude that land and property price are environmental/social variables intrinsically related to walkability and walkable characteristics (CHIARADIA et al., 2012CHIARADIA, A. et al. Compositional and urban form effects on centres in Greater London. Proceedings of the Institution of Civil Engineers - Urban Design and Planning, v. 165, n. 1, p. 21-42, 2012. ).

# Data and methodology

Considering the phenomenon under investigation as real-life and contemporary, dynamic and complex therefore indissociable from its contextuality, the most adequate research strategy is the case study (YIN, 2001YIN, R. K. Estudo de caso: planejamento e métodos. 2. ed. São Paulo: Bookman, 2001. ), focusing on Rolândia-PR, Brazil. For the development of this strategy, a Correlational methodology was adopted to identify spatial and behavioral patterns with many variables, using statistics (GROAT; WANG, 2002GROAT, L.; WANG, D. Architectural research methods. 2nd. ed. Danvers: John Wiley & Sons, Incorporated, 2002. ).

On account of database availability and populational representation of a mid-size Brazilian city the selected case study is Rolândia. This city has recently developed its Urban Mobility Plan, therefore an extensive subjective database based on an Origin-Destination (OD) survey was provided by ITEDES - Institute of Technology, Economic, and Social Development. The OD survey collects detailed travel behavior data by asking participants to describe all trips made the day before a questionnaire was applied. The precise addresses of each trip’s origin and destination were collected, along with purpose, mode, time of day and duration. A trip was established as any time you went from one address to another in a vehicle, by walking or biking. Each trip made was accounted for, providing data on the pedestrian movement that was spatialized in geoprocessing procedures that connect geocoded origins and destination through georeferenced routes. Walking levels, the final correlational data for this study, is represented by 394 walking trips that were quantified in meters per unit area.

This study has conducted all analysis trough network buffers of 1000 meters extending along the street-network of around the households of respondents. Even though it is suggested that the optimal measurement scale might depend on the research context, this radius follows general literature tendencies that consider BE exposure classifications to 1000 meters, as most walks are shorter than 600m and few exceed 1200 m (HOUSTON, 2014HOUSTON, D. Implications of the modifiable areal unit problem for assessing built environment correlates of moderate and vigorous physical activity. Applied Geography, v. 50, p. 40-47, 2014. ). Further, the available data regarding self-reported travel behaviors shows a majority of walking trips restricted within the 1000-meter distance range. Therefore, the selected metric reflects walking patterns present in the case study considered.

The methodological strategy of this research involves evaluating the relevance of objective walkability variables of the BE in the context of a Brazilian city. Tankibg evidence from the literature into account and the background presented previously in this work, the objective walkability variables considered are presented in Table 1. Data on objective walkability variables were either collected in the field or provided by the city hall of the case study. All data were geocoded by the researchers using the ArcGis 10.6.

Table 1
Objective walkability variables considered

Residential density is a measure of the number of residential units per unit of area (SAELENS; SALLIS; FRANK, 2003SAELENS, B. E.; SALLIS, J. F.; FRANK, L. D. Environmental correlates of walking and cycling: findings from the transportation, urban design, and planning literatures. Annals of Behavioral Medicine, v. 25, n. 2, p. 80-91, 2003. ). After counting all the households in the municipality of Rolândia, the residential density ratio was calculated for each unit of analysis considered.

Retail floor area ratio measures the area of the retail parcels divided by the footprint of the building destined for retail use. A low ratio would indicate that the plot is likely to direct more parking area while a larger value would indicate less surface area to be intended for this purpose. Dedicating less urban surface to parking lots is understood as facilitating pedestrian access (FRANK et al., 2010FRANK, L. D. et al. The development of a walkability index: application to the Neighborhood Quality of Life Study. British Journal of Sports Medicine, v. 44, n. 13, p. 924-933, 2010. ).

Intersection density is a measure related to the connectivity of the street network, represented by the ratio between the number of true intersections (between three or more roads) and the areal extension of the unit being considered (FRANK et al., 2010FRANK, L. D. et al. The development of a walkability index: application to the Neighborhood Quality of Life Study. British Journal of Sports Medicine, v. 44, n. 13, p. 924-933, 2010. ). This measure is, therefore obtained by the division of N true intersections contained in a unit and the area in square meters of that same unit.

Entropy, or land use mix, is a measure of the diversity of uses present in an area unit. In this research, taking as a starting point the work proposed by Frank et al. (2010)FRANK, L. D. et al. The development of a walkability index: application to the Neighborhood Quality of Life Study. British Journal of Sports Medicine, v. 44, n. 13, p. 924-933, 2010. , the mixture between 5 uses was considered: residential, commercial, entertainment services (including restaurants, for example), and institutional (including schools, government buildings, etc.). The resulting values are normalized between 0 and 1, where 0 would indicate the existence of only one use in a given area and 1 would indicate a complete and equal distribution of the five uses. The entropy was calculated through the equation proposed by Shannon (1948)SHANNON, C. E. A Mathematical theory of communication. Bell System Technical Journal, v. 27, n. 3, p. 379-423, 1948. .

Space Syntax seeks to describe, through quantitative measures, the configuration of the urban grid, relationships between public and private space, the urban system as the distribution of land use, cohesion and social exclusion, accessibility and security (CARVALHO; SABOYA, 2017CARVALHO, A.; SABOYA, R. T. de. A localização residencial em uma cidade vertical : um estudo sintático em Florianópolis. Revista Brasileira de Gestão Urbana, v. 9, n. 3, p. 414-429, 2017. ). In space syntax, the urban space is divided into spatial units known as axial lines. These are the longest straight lines capable of covering a whole system of public spaces (HILLIER; HANSON, 1984HILLIER, B.; HANSON, J. The social logic of space. Cambridge: Cambridge university press, 1984. ) The relations between axial lines of a system can be analyzed through the Integration (1) and Choice (2) measures. Both were calculated trough axial lines generated from street centerline data provided by the city hall of Rolândia. Then, axial lines were imported into the QGIS software, a free and open-source GIS. Through the Space Syntax toolkit, the syntactic integration and choice measures were calculated for each street segment in radii ranging from 100 to 2000 with 100 meter intervals.

Land price and real estate property price were obtained in the city hall of the case study under analysis. In this study, such data was considered as walkability constructs. Land prices and real estate prices are represented as mean scores of the values in Reais included in each unit of analysis.

The relationship between objective walkability variables and walking was analyzed through a Machine Learning approach. The Random Forest (RF) ensemble learning method for regression, proposed by Breiman (2001)BREIMAN, L. Random forests. Machine Learning Journal, v. 45, n. 1, p. 5-32, 2001., was applied. RF has excellent performance and, although it is not widely used in the urban planning field of study, it has several characteristics that make it ideal for its datasets. Some advantages of RF are the good predictive performance even when most predictive variables are noisy; does not require pre-selection of features; not prone to overfitting; handles both categorical and continuous predictors; incorporates interactions among predictor variables; and returns measures of variable importance (DÍAZ-URIARTE; ALVAREZ DE ANDRÉS, 2006DÍAZ-URIARTE, R.; ALVAREZ DE ANDRÉS, S. Gene selection and classification of microarray data using random forest. BMC Bioinformatics, v. 7, p. 1-13, 2006. ). Given these promising qualities, all individual variables considered in this research were tested using RF.

The RF quality measure of a model is the output value of the coefficient of determination (R2). R2 is defined as “the proportion of variance explained by the regression model” (NAGELKERKE, 1991NAGELKERKE, N. J. D. A note on a general definition of the coefficient of determination. Biometrika, v. 78, n. 3, p. 691-692, 1991. ). Thus, it can be seen as a measure of the model’s success in predicting the dependent variable through the independent ones.

The most relevant output from RF is an importance measure of the predictor variables. Variable importance is a difficult concept to define in general, because the relevance of a variable may be due to its possibly complex interaction with other variables (LIAW; WIENER, 2002LIAW, A; WIENER, M. Classification and regression by randomForest. R news, v. 2, p. 18-22, dec. 2002. ). In summary, the random forest algorithm estimates the importance of a variable by looking at how much prediction error increases when data for that variable is permuted while all others are left unchanged. In this work, feature importance was extracted with the intention of analyzing variables that are more related to the response variable, walking levels.

RF is a truly ‘random’ method, its results can vary from run to run. The verification of model stability is of utmost importance (SHIH, 2011SHIH, S. Random forests for classification trees and categorical dependent variables: an informal Quick Start R Guide- Report, Stanford University, 2011.). Therefore, R2 was cross-validated to obtain its distribution. Cross-validation is an essential common practice to avoid overfitting, the production of an analysis that corresponds too closely or exactly to a particular set of data, and may, therefore, fail to fit additional data or predict future observations reliably (EVERITT; SKRONDAL, 2010EVERITT, B. S.; SKRONDAL, A. The Cambridge dictionaryof statistics. 4th. ed. Cambridge: Cambridge University Press, 2010. ). In summary, it verified how well the model will generalize to new data. A random permutations cross-validation (or Shuffle & Split) was conducted for the results reported.

The RF Regression was implemented using the Scikit-learn machine learning library for the Python programming language (PEDREGOSA et al., 2012PEDREGOSA, F. et al. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, v. 12, p. 2825-2830, 2012. ). This language was chosen due to its ample use in geoprocessing (DOBESOVA, 2011DOBESOVA, Z. Programming language Python for data processing. In: INTERNATIONAL CONFERENCE ON ELECTRICAL AND CONTROL ENGINEERING, Yichang, 2011. Proceedings […] Yichang, 2011. ; GRASER; OLAYA, 2015GRASER, A.; OLAYA, V. Processing: a Python Framework for the seamless integration of geoprocessing tools in QGIS. ISPRS International Journal of Geo-Information, v. 4, n. 4, p. 2219-2245, 2015. ).

# Results

A RF regression model was constructed. The dependent variable was walking levels (meters walked per unit area) and predictor variables were those present in Table 01. After cross-validation through the Shuffle & Split method, the final R2 value was obtained. A mean R2 of 0.859 (Table 2) indicates a satisfactory model performance. The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse) (PEDREGOSA et al., 2012PEDREGOSA, F. et al. Scikit-learn: machine learning in Python. Journal of Machine Learning Research, v. 12, p. 2825-2830, 2012. ). The standard deviation for the model seems to be minimal (SD = 0,086).

Table 2
Mean R2 and standard deviation of RF regression

The measure of importance indicates the variables that are more closely related to the dependent one and contribute more for its variation. The 10 most relevant variables can be observed in Table 3. The most relevant individual variables were Entropy, Integration at a 2000m radius (0.236) and Residential density (0.060), however, the land use mix variable presented itself as substantially better than the others, with an importance value over 4 times larger than the second most important feature.

Table 3
Variable importance for RF regression model

A bar graph was generated to visually verify the distribution of feature importance values. As can be observed in Figure 1, there is an importance disproportion: Entropy is found at an excelling position of advantage over other variables.

Figura 1
Feature importance histogram of RF regression

Results indicate that Entropy (Figure 2) seems to be strongly associated with walking, consistent with previous studies on land use patterns. Land use mix is at the base of many urban planning and transport studies, in that people move between activities located in different places. If activities are close enough to make walking easier, more people will probably walk (FORSYTH et al., 2008FORSYTH, A. et al. Design and destinations: factors influencing walking and total physical activity. Urban Studies, v. 45, n. 9, p. 1973-1996, 2008. ). Mixed-use is also thought to provide more visual variety and informal policing. To date, many studies have found a number of destinations to be associated with active travel, especially walking (GILES-CORTI et al., 2005GILES-CORTI, B. et al. Understanding physical activity environmental correlates: Increased specificity for ecological models. Exercise and Sport Sciences Reviews, v. 33, n. 4, p. 175-181, 2005. ; LEE; MOUDON, 2006aLEE, C.; MOUDON, A. V. Correlates of walking for transportation or recreation purposes. Journal of Physical Activity & Health, v. 3, p. 77-98, 2006a. ). Considering such outcome, we are led to believe that measuring entropy using the Shannon equation can minimize possible bias. One aspect to highlight in this study is that, despite literature evidence (GUO; PEETA; SOMENAHALLI, 2017GUO, Y.; PEETA, S.; SOMENAHALLI, S. The Impact of walkable environment on single family residential property values. Journal of Transport and Land Use, v. 10, n. 1, p. 241-261, 2017. ; MATTHEWS; TURNBULL, 2007MATTHEWS, J. W.; TURNBULL, G. K. Neighborhood street layout and property value: the interaction of accessibility and land use mix. Journal of Real Estate Finance and Economics, v. 35, n. 2, p. 111-141, 2007. ), no relationship was found between property or land values with walkability.

Figura 2
Entropy Z-score map at the 1000m street network scale

The second RF finding indicated the relevance of the Integration Z-score at a 2000m radius variable (Figure 3), supporting Hillier’s theory and indicating that syntactic measures produce better outcomes when analyzing pedestrian movement than more common connectivity measures in walkability studies, such as intersection density (KOOHSARI et al., 2016aKOOHSARI, M. J. et al. Walkability and walking for transport: characterizing the built environment using space syntax. International Journal of Behavioral Nutrition and Physical Activity, v. 13, n. 1, 2016a. ). Hillier and colleagues have argued that street network, which is essentially a formal aspect of urban form, could influence pedestrian movement through the different distribution of commercial land uses according to the level of integration (HILLIER; HANSON, 1984HILLIER, B.; HANSON, J. The social logic of space. Cambridge: Cambridge university press, 1984. ). Considering the scale of the study case under investigation, the broader ranges of integration, that reach as much of the system as possible, were better related to walking. Therefore, the calculations that included the global Integration measure and the larger 2000 m local radius, which reaches whole sections of the system, had more relevant results.

Figura 3
Integration r2000m Z-score map at the 1000m street network scale

Many studies have been carried out over the past two decades on the correlations that can be found between pedestrian flow and syntactic measures of local integration. The basic conclusion is that local integration can be used to study people’s movements within urban systems (JIANG; CLARAMUNT; KLARQVIST, 2000JIANG, B.; CLARAMUNT, C.; KLARQVIST, B. Integration of space syntax into GIS for modelling urban spaces. ITC Journal, v. 2, n. 3-4, p. 161-171, 2000. ). Such conclusions are of great impact as a tool for urban planners and designers to foresee pedestrian movement by analyzing morphological structures using space syntax techniques.

Residential density Z-score (Figure 4) also showed to be significant. This result is supported by the literature, such as in the study conducted by Frank and colleagues (FRANK et al., 2008FRANK, L. D. et al. A hierarchy of sociodemographic and environmental correlates of walking and obesity. Preventive Medicine, v. 47, n. 2, p. 172-178, 2008. ), where individuals were more likely to walk if they lived in neighborhoods with greater residential density. Alike in the study conducted by Lee and Moudon (2006b)LEE, C.; MOUDON, A. V. The 3Ds + R: quantifying land use and urban form correlates of walking. Transportation Research Part D, v. 11, p. 204-215, 2006b. , residential density measures were found to be significantly associated with walking both at the parcel level and at the 1 km buffer area level. Overall, higher densities have many benefits in terms of efficient use of infrastructure, housing affordability and street life (FORSYTH et al., 2007FORSYTH, A. et al. Does residential density increase walking and other physical activity? Urban Studies, v. 44, n. 4, p. 679-697, 2007. ).

Figura 4
Residential density Z-score map at the 1000m street network scale

# Discussions

This researches’ aim was to evaluate the relevance of individual objective walkability measures in a mid-size Brazilian city. To that end, urban form variables were tested for a deeper understanding of the phenomenon. The analysis and results indicated that the BE as a support for walking on a mid-size Brazilian city is a particularly contextual phenomenon.

When comparing individual objective walkability variables to self-reported walking on the mid-size Brazilian city of Rolândia, it was possible to uncover the specific spatial elements that influence walking. The urban form measures of Entropy, Space Syntax integration at the 2000 m radius, and Residential Density were identified as being more strongly related to walking. Entropy, specifically, was found to be the main correlate of walking. These findings are consistent with the literature as they represent, in a context-specific way, the traditional 3D’s concept of land-use Diversity, pedestrian-oriented Design, and Density (CERVERO; KOCKELMAN, 1997CERVERO, R.; KOCKELMAN, K. Travel demand and the 3Ds: density, diversity, and design. Transportation Research Part D: Transport and Environment, v. 2, n. 3, p. 199-219, 1997. ).

Land-use diversity (land-use mix) is represented here by the Entropy measure, which has consistently been found associated with walking (SAELENS; HANDY, 2008SAELENS, B. E.; HANDY, S. L. Built environment correlates of walking: a review. Medicine and Science in Sports and Exercise, v. 40, n. 7, p. 550-566, supp. 1, 2008. ). Density is represented by the Residential density variable, regarded as important as it directly affects the compactness of an area, influencing walking (MOUDON et al., 2006MOUDON, A. V. et al. Operational definitions of waikable neighborhood: theoretical and empirical insights. Journal of Physical Activity and Health, v. 3, p. s99-s117, supp. 1, 2006. ). Design usually encompasses street connectivity - describing the degree to which destinations are connected by streets (LU; XIAO; YE, 2017LU, Y.; XIAO, Y.; YE, Y. Urban density, diversity and design: Is more always better for walking? A study from Hong Kong. Preventive Medicine, v. 103, p. S99-S103, 2017. ). The most common method for assessing connectivity in walkability studies is intersection density (FRANK et al., 2005FRANK, L. D. et al. Linking objectively measured physical activity with objectively measured urban form: Findings from SMARTRAQ. American Journal of Preventive Medicine, v. 28, n. 2, p. 117-125, supp. 2, 2005. ; OWEN et al., 2007OWEN, N. et al. Neighborhood walkability and the walking behavior of Australian adults. American Journal of Preventive Medicine, v. 33, n. 5, p. 387-395, 2007. ), however, this work’s results indicate that the space syntax measure of local integration greatly surpassed the traditional metric for street connectivity in its relevance to predict walking levels. When compared with intersection density, the space syntax measure of integration is less intuitive and thus may be more difficult to grasp for practitioners and decision-makers. Nonetheless, space syntax has in its favor greater ease of obtaining necessary data, the properties of capturing aspects of the street network that are relevant to pedestrians and the possibility of identifying connectivity not only of an area but also of a single street segment (KOOHSARI et al., 2016aKOOHSARI, M. J. et al. Walkability and walking for transport: characterizing the built environment using space syntax. International Journal of Behavioral Nutrition and Physical Activity, v. 13, n. 1, 2016a. ).

After such considerations, one important aspect of the performance of individual walkable urban form variables is the superior relevance of Entropy. This means that land use mix may exert the main role in impacting walking levels in the context of a mid-size Brazilian city. Recent evidence presented by Humberto et al. (2019)HUMBERTO, M. et al. Walking and walkability: do built environment measures correspond with pedestrian activity? Ambiente Construído, Porto Alegre, v. 19, n. 4, p. 23-36, out./dez. 2019. for the city of São Paulo exemplifies the context of a larger Brazilian city, where an index containing the variable Diversity of land uses was tested on its relationship to pedestrian movement and yielded insufficient results. Therefore, as hypothesized, the environmental variables related to walking behavior are not necessarily the same in mid-size Brazilian cities as in larger ones or high income developed countries. Consequently, there is a demand for specific approaches to measuring the objective walkability-built environment effectively, possibly considering land use mix as a central walkability measure.

# Conclusions

This study provided an exploration of the relevance of several walkability constructs in a mid-size Brazilian city. This analysis was conducted through the understanding of these measures in relation to walking levels. When analyzing walkability measures of the BE in relation to walking levels, the most relevant variables were entropy, the space syntax measure of integration at a 2000m radius and residential density. These findings are of great implication to the operationalization of walkability measurement in Brazilian cities, indicating that more widespread walkability variables, such as intersection density, might not be suited for our social, cultural and urban reality. Further, this outcome indicates the relevance of mesoscale walkability measures in predicting walking behaviors and representing walkability.

The literature emphasizes the need for policy-relevant interdisciplinary research, which may lead to more contextually desirable outcomes (SALLIS et al., 2016SALLIS, J. F. et al. Use of science to guide city planning policy and practice: how to achieve healthy and sustainable future cities. The Lancet, v. 388, n. 10062, p. 2936-2947, 2016. ). This work goes towards this recommendation, presenting methods that include a case study, with an emphasis on local evidence, that may lead interventions in specific urban environments. Considering the relevance of land use mix, residential density and space syntax to walking behaviors, guidance for designing urban developments to support walkable communities could be subsidized.

This study presents some limitations but also moves forward in the discussion of specific walkability variables for mid-size Brazilian cities. The main limitation is that the OD survey has not been created in the specificity of analyzing walkability, even though the database was an important and coherent source of information.

It is essential to emphasize that the authors acknowledge the limitation in the self-report information approach (RIBEIRO et al., 2014RIBEIRO, M. D. et al. Influence of GPS and self-reported data in travel demand models. Procedia - Social and Behavioral Sciences, v. 162, p. 467-476, Panam, 2014. ), recall bias and inaccuracy are always a possibility.

It must be highlighted that entropy was measured in this study in a detailed-systematic approach, considering specific land use categories for walkability analysis and building typologies, though data constructed by the authors’ research group. Such specificity has, probably, contributed to the outcome of this research. However, such data is not readily available in most municipalities of Brazil, making it difficult to so precisely utilize the entropy variable effectively or create comparisons between case studies.

Furthermore, as the relationship between people and their environment changes over time, using longitudinal study designs is of utter importance (RIVA; GAUVIN; BARNETT, 2007RIVA, M.; GAUVIN, L.; BARNETT, T. A. Toward the next generation of research into small area effects on health: a synthesis of multilevel investigations published since July 1998. Journal of Epidemiology and Community Health, v. 61, n. 10, p. 853-861, 2007. ). To investigate how walking behaviors are influenced by the BE it is necessary to outperform cross-sectional associations through prospective and intervention studies that uncover the relationships between environment and behavior, indicating causality (OWEN et al., 2004OWEN, N. et al. Understanding environmental influences on walking: Review and research agenda. American Journal of Preventive Medicine, v. 27, n. 1, p. 67-76, 2004.).

And finally, it must be emphasized that the relevant variables discussed here have a threshold of positive influence on walking. We do not indicate such a quantity baseline. It can only be inferred that such variables influence walking behaviors.

In the research paradigm that aims to analyze the existing relationships between urban form and active travel, it is understood that BE characteristics can influence travel patterns. In this study, indications of how walkability variables might affect walking behaviors in Brazil are made, pointing out the outstanding influence of Land Use Mix over walking levels. Considering such an outcome, it is necessary to further investigate how walkability indices, composite metrics of individual walkability variables, might be effective for the Brazilian urban scenario. The walkability approach to understanding urban form can provide insights on how the BE might contribute to active human behaviors that can possibly subsidize strategies to promote daily life PA in the mid-size Brazilian city scenario.

# Acknowledgments

We thank the National Council for the Improvement of Higher Education (CAPES) for the scholarship, the components the Urban Environmental Design Research Group for their support, and the evaluators for their contributions.

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# Publication Dates

• Publication in this collection
08 May 2020
• Date of issue
Apr-Jun 2020