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2022
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Urban Climatic Map of Salvador, Brazil, using a Land Use Pattern Methodology

Une carte climatique urbaine de Salvador (Brésil) selon une méthodologie fondée sur l’occupation des sols
Mapa del clima urbano de Salvador (Brasil) a partir de una metodología basada en el uso del suelo
Mapa de Clima Urbano de Salvador, Brasil, uma Metodologia apoiada no Mapa de Padrões de Ocupação do Solo
Tereza Moura, Jussana Nery, Eduardo Prado, Carolina Vieira, Heliana Mettig Rocha et Lutz Katzschner

Résumés

Cet article présente le développement d’une méthodologie destinée à créer une carte analytique du climat urbain (UC-AnMap selon l’acronyme anglais) fondée sur un plan d’occupation des sols (MPO selon l’acronyme anglais) dans le cas de la ville de Salvador. Située dans la région nord-est du Brésil, Salvador est actuellement la quatrième ville la plus peuplée du pays avec près de trois millions d'habitants. La MPO était une juste une strate dans la première version, mais dans la version actualisée elle est devenue une strate fondamentale à partir de laquelle sont dérivées les cartes thématiques relatives aux diverses morphologies urbaines de la ville. Les MPOs sont proposées ici comme une base méthodologique de départ, un outil d'assistance à la construction de la carte climatique urbaine – UCMap (selon l’acronyme anglais). Elles permettent de capturer la variété des modes d’occupation des sols urbains et se basent sur des informations universellement disponibles telles que les images et les outils Google Earth, sans recourir à des bases de données sophistiquées et coûteuses auxquelles les villes des pays en développement n’ont généralement pas accès. Les MPOs consistent à identifier et à délimiter des zones d’occupation des sols de manière visuellement homogène par des polygones à l'échelle adéquate de 1:10.000. Les zones similaires sont regroupées sous le même modèle, indépendamment de leurs fonctions urbaines, créant ainsi un système de classification qui caractérise les différents modèles existants par leurs attributs urbanistiques distincts. Les inégalités sociales et économiques de Salvador ont produit un modèle urbain inégal, et les facteurs institutionnels et structurels ont contribué à l'absence de bases de données urbaines numériques à jour. En conclusion, étant donné le contexte local, l'UC-AnMap de Salvador- ne pouvait pas commencer sans le développement d’un MPO.

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Texte intégral

The authors would like to thank the Public Ministry of the State of Bahia for the student grants and some financial support, the Permanecer Program/UFBA (2018 and 2019), to the photographer Manu Dias for making the digital photographs available, and the students, Gabriel Nunes, Lucas C. Rodrigues, Davi Santos, Igor Santana Ferreira, Matheus Silva Cabral, Amanda Rodrigues Santana, and Beatriz Pontes Lisa.

Introduction

  • 1 This technical-scientific collaboration with the German institutions, first the University of Kasse (...)

1Urban climate studies of the city of Salvador, Brazil, have been a research field at the Laboratory of Environmental Comfort and Sustainable Technologies of Architecture, Urbanism, and Landscaping (LACAM-Tec) at the Faculty of Architecture of the Federal University of Bahia for the last 26 years, with a particular focus on the urban thermal comfort conditions yielded by urbanization and global climate change. The presented Urban Climatic Map (UCMap) methodology was first developed in cooperation with the University of Kassel, Germany, and more recently with the Institute for Climate and Energy Concepts in Germany (INKEK), in accordance with the technical-scientific guidelines for climate research applied to urban planning in Germany (VDI, 2015).1

2Broadly speaking, the built environment is the single most important contributor to the urban climate at the mesoscale and at smaller scales. Accordingly, UCMap integrates different information layers of both natural and urban climate factors into a single Urban Climatic Analytical Map (UC-AnMap). The thermal load and the dynamic potential of the wind field are evaluated with respect to thermal comfort and the results are spatially distributed. The UC-AnMap is then translated into an Urban Climatic Planning Recommendation Map (UC-ReMap), which provides planning strategies and guidelines for each thermal condition area to improve the thermal environment and promote a quality urban environment for residents.

3The earliest UC-AnMap for Salvador, Brazil, was published at the Sixth International Conference on Urban Climate by the LACAM-Tec research group (Nery et al., 2006; Andrade et al., 2015; Nery et al., 2019). Given the outdated and limited cartographic information and the contrasting urban morphologies that constituted Salvador’s urban fabric at that time, the development of the UCMap started with the results of a Land Use Patterns Map (MPO), which was finalized in 2004. In 2019, MPO 2004 was updated using more recent technological resources.

4From the outset in 2002, the MPO approach has emerged as an alternative methodology, especially applicable as a support tool to develop UC-AnMaps under similar conditions to those found in Salvador in the early 2000s, i.e., a mixed-type urban morphology with only basic mapping information available. MPO 2004 allowed the various urban land use patterns characteristic of a city, Salvador, in a developing country to be captured; in such cities, sophisticated and costly databases and analytical resources are often not available. Therefore, the main question at the outset of this project related to how to develop climatic maps under these conditions.

5To the best of our knowledge, the above scenario has remained relatively unchanged for many medium-sized and some large-sized cities in Brazil. At present, most cities lack high-resolution digital information. Moreover, the majority of Brazilian municipalities do not possess updated cartographic maps or adequate information concerning a vast portion of their urban territories, namely, those areas that constitute the majority of informal urban expansions. These urban areas are spatial expressions of historical processes produced by highly socioeconomic inequalities of land occupation, as discussed later in this paper.

6For these reasons, an MPO can serve as a starting point for the development of a UC-AnMap. Therefore, the main objective of this study is to show how the Salvador MPOs, both the 2004 and 2019 versions, were used as tools to assist in the development of a UCMap in an inhomogeneous city morphology with insufficiently updated urban information and data. The development process for the Salvador UC-AnMap presented here serves as a case study to present an alternative initial procedure for its development under the above conditions. This paper traces the chronological development stages, starting with the first UC-AnMap and MPO 2004, followed by the update process for MPO 2019 and concluding with its application toward the calculation of the 2019 Salvador UC-AnMap.

Inequality in Salvador

7The city of Salvador is no exception to the inequalities that characterize Brazilian cities. On the contrary, founded in the early modern age under the backing of mercantilism in 1549, Salvador, simultaneously the first capital and the first official city in Brazil, was a Portuguese-style planned town. It was the most important commercial Portuguese colonial city of the time and thrived until the mid-18th century when Rio de Janeiro became the capital of the colony. That change affected Salvador’s economy, and other historical events later altered its growth and development in comparison to the more southern cities of Brazil. The city remained practically unchanged until the first decades of the 20th century, when late industrialization attracted large contingents of rural migration into the city, expanding its land occupation (Figures 1 and 2).

Figure 1: Population growth of Salvador, Brazil

Figure 1: Population growth of Salvador, Brazil
  • 2 A well-known term, usually translated as “shantytown.”

8The exponential growth of Salvador’s population combined with both the absence of adequately enforced planning ordinances and the implementation of efficient social housing programs — aimed at accommodating the low-income urban expansion — generated the historical land “invasions” (currently termed “occupations”) and favelas,2 which over time were somewhat improved by residents.

Figure 2: Urban expansion of Salvador

Figure 2: Urban expansion of Salvador

9In effect, public policies and state investments in Brazil prioritized not only the small middle- income class but also the higher income classes to the detriment of its larger and poorer income classes. The former groups inhabit pockets of urban built areas with good infrastructure that meet the formal parameters of urbanism, whereas the latter group still lives in precarious housing situations in peripheral areas with little access to quality public services, such as public transportation, or basic infrastructure services, such as water and sewage systems (Gordilho-Souza, 2008).

10Brazil is among the top 10 most unequal countries in the world with an estimated 2020 Gini Index of 0.539, where 0 indicates perfect equality and 1 indicates perfect inequality (World Bank, 2017). In the latest census in 2010, the city of Salvador, with the third largest national population in 2010 and fourth largest in 2020, had an even worse Gini Index of 0.6449 (Ministério da Saúde, 2021). Unsurprisingly, historical inequalities have produced ongoing processes that have unfolded into highly social, economic, environmental, and spatially segregated urban areas throughout the country.

11Despite the relevance of land use factors for the calculation of UCMaps, the emphasis on the land use patterns presented here does not replace the necessity to incorporate geographic factors affecting the climate. Geographic factors modulate the climate at different scales (topoclimate and microclimate) and need to be integrated into the UCMap methodology, as illustrated below by Salvador’s own unique geographic and climatic sites (Figures 3 and 4).

12Indeed, in the early days of UCMap studies, urban land use zones were typically used to classify the types of “climatopes” (Ren et al., 2011), which were taken as the basic units of UC-AnMaps. However, according to Ren (2015) and based on the work of other scholars such as Baumüller et al. (2015), climatopes represent a spatial distribution of urban climate types resulting from the product of distinct urban land uses and covers, which vary from place to place according to the climate specificities at each location. Therefore, the climatope categories are set according to both natural and built urban areas and the climate functions that affect these climatopes. This means that the determination of climatopes requires qualitative and subjective assessments of climatologists and goes beyond simply identifying land use zones.

13As previously mentioned, the patchwork urban fabric found in Salvador requires a further distinction of the climatope categories in the development of UCMaps. However, other contexts may also cause highly inhomogeneous urban morphologies, as shown by climate studies of large cities such as Hong Kong (Ng, 2015; Ren, 2015) or even in the early UCMap stages of medium-size cities such as Berlin (Welsch, 2015). Because of its high density and high-rise urban morphology, simply using land use information does not suffice for a city such as Hong Kong. Instead, in addition to land use zoning information, it is necessary to include calculations such as the building volume density and ground coverage ratio, adding more layers to the Geographic Information System (GIS) calculations to capture the conditions in Hong Kong in the UC-AnMap (Ng, 2015).

14Unlike in Salvador, the Hong Kong UCMap could rely on extensive digital information concerning the topography, i.e., a digital elevation model (DEM), the urban morphology, e.g., buildings, building footprint and road data, and greenery information from the Planning Department of Hong Kong (Ng, Ren, 2012). The quality and availability of digital data affect the reliability and seamless development of climatic map processes. In Hong Kong, this allowed new parameter layers to be calculated to describe the urban density and its UCMap.

15Facing circumstances more unfavorable than those in Hong Kong, the first UCMap for Salvador took advantage of the available data and proposed the development of MPO 2004. Later, the same procedure was followed to update the MPO to its 2019 version, MPO 2019, with the approach modified to account for the high-resolution digital data made available by the local government.

Salvador: location and climate

  • 3 According to IBGE (2017), the total municipal area of Salvador is 693,453 km2. The data presented h (...)

16Salvador is located in the northeastern region of Brazil (Figure 3), at a latitude of 13° 01' S and a longitude of 38°31' W, on a peninsula that narrows the entrance of the Todos-os-Santos Bay (Figure 4). The city relief is marked by a series of scattered hills, creating valleys throughout its territory. Altitudes vary from sea level to Salvador’s highest and only peak of 117 m, with an average territorial height of approximately 60–70 m. The city is surrounded by the sea on two sides: the Atlantic Ocean at the eastward side and the largest Brazilian bay, also the second largest bay in the world, on the westward side. Its municipal territory3 is composed of two geographically distinct spaces: the continental region, with an area of approximately 279 km² corresponding to approximately 90% of the city, and the island regions, which together amount to an area of approximately 25 km².

Figure 3: Location of Salvador using Google Earth images

Figure 3: Location of Salvador using Google Earth images

Figure 4: Aerial photo of the peninsula of Salvador

Figure 4: Aerial photo of the peninsula of Salvador

17The city’s proximity to the equator and its peninsular condition gives Salvador a very homogeneous tropical humid climate on both daily and yearly timescales. According to the latest climatological normals for the period of 1981–2010, the average monthly maximum air temperature reaches a highest value of 31°C in February and a lowest value of 26.5°C in July, whereas the minimum air temperature is approximately 24.2°C in summer and 21.1°C in winter. The relative humidity does not vary much annually. Indeed, the average annual range is approximately 6%: from 78.5% in December to 83.9% in May (INMET, 2021). Typically, southeastern trade winds and sea breezes characterize the wind pattern in Salvador. The average wind speed is 3.2 m·s−1 in winter and 2.8 m·s−1 in summer. June has the lowest insolation with 167 h and January the highest with 246 h (Nery et al., 2006).

18The graphs in Figure 5 represent three time series of the climatological normals for Salvador, corresponding to the periods of 1931–1960, 1961–1990, and 1981–2010, showing the averages of the maximum and minimum temperatures for each month during each period. In both figures, it is evident that the average temperatures of the city are increasing. The minimum temperature values are equivalent for the last two time periods between the months of April and October, remaining at approximately 0.5°C above the values corresponding to the first half of the 20th century. This behavior can be explained both by the 10-year overlap in the data between the last two periods, from 1981 to 1990, and the urban expansion in the vicinity of the meteorological station, which intensified from the 1960s.

Figure 5: Climatological normals showing the average minimum and maximum temperatures in Salvador for three different time periods

Figure 5: Climatological normals showing the average minimum and maximum temperatures in Salvador for three different time periods

19To assess the thermal comfort of the local population, LACAM-Tec carried out simultaneous in-situ measurements and interviews in Salvador. With the aim of statistically calibrating the thermal comfort index, the upper limit of comfort for the Physiological Equivalent Temperature (PET) index (in °C) according to the acclimatized population was set at a higher value of 26.8°C (Andrade et al., 2016) compared with that of 23°C defined by Matzarakis et al. (1999). A lower limit, however, could not be established given the tropical conditions in Salvador. Therefore, acclimatization associated with the low annual variation in the climate conditions in Salvador, from summer to winter, contributes to the low perceived importance of the urban climate, primarily because the majority of the population is concerned with more immediate economic and social issues. Climatic conditions do not present an imminent risk to life, nor do they result in extreme heat stress throughout most of the year. In general, climate-related issues that more strongly affect people are flooding and landslides (Figure 6), as well as the absence of adequate basic sanitation and the irregular land occupation, which present risks to the population and increase their vulnerability. Recently, heat waves have also become an adversity to the population, resulting in increased mortality rates in Salvador (McMichael et al., 2008) and primarily affecting low-income classes without the household resources needed to improve their indoor thermal conditions.

20Other major concerns of the population relate to urban mobility and the absence or precariousness of urban public equipment and housing. These pressing basic issues, resulting from the social disparities in Brazil and Salvador, constitute priorities for academic investigations; simultaneously, the public administration is attempting to deal with harsh socioeconomic realities, while neglecting the thermal aspects of urban environmental quality.

21Climate, in general, does not receive much public attention. In addition, because of its tropical climate, the relationships between urban climate and urbanization and between urban climate and public health are not obvious. People may be aware of climate and weather types, but most are unaware that the urban climate is derived from the form of urbanization.

Figure 6: Photo of an urban area in Salvador subject to landslides

Figure 6: Photo of an urban area in Salvador subject to landslides

22Regardless, the low annual climatic variability undergoes significant changes at the microclimatic scale due to the complex morphology of urban areas. For example, two neighboring areas that present distinct land use characteristics despite their close proximity were investigated by Morais et al. (2011). The Nordeste de Amaralina neighborhood represents an area of spontaneous occupation, while Pituba is a planned middle-higher class neighborhood. The first was categorized as urban typology pattern 4 (P4) and the second as pattern 2 (P2) in MPO 2019, described below. Figure 7 shows an aerial view of the two neighborhoods with their different urban typology patterns; both are contiguous and are located within the same topoclimatic conditions.

Figure 7: Aerial photo of part of the city including Nordeste de Amaralina (P4) and Pituba (P2) neighborhoods and other urban patterns

Figure 7: Aerial photo of part of the city including Nordeste de Amaralina (P4) and Pituba (P2) neighborhoods and other urban patterns

23Figure 8 shows side-by-side details of the two neighborhoods and the location of the official meteorological station of Salvador for reference. For each pattern, a close-up photo and an aerial view show the locations of the respective measurement points of the climate variables affecting thermal comfort. The two measurement points and the meteorological station are near each other and close to the southeastern Atlantic seafront of the city.

Figure 8: Photos and locations of the measurement points in the Nordeste de Amaralina and Pituba neighborhoods, and INMET meteorological station.

Figure 8: Photos and locations of the measurement points in the Nordeste de Amaralina and Pituba neighborhoods, and INMET meteorological station.

24In Figure 8, (a) represents Pattern P2 in Pituba: a planned urban land plot, densely built, consisting of formally constructed buildings with more than 10 floors that correspond to formal urban parameters; (b) represents Pattern P4 in Nordeste de Amaralina: a densely and yet informally self-built urban area comprised of row-house constructions of up to five floors disregarding any urban parameters, and (c), Pattern P9: a large green area with the location of the Instituto Nacional de Meteorologia (INMET) meteorological station for reference.

25The 2010 measurement campaign showed that Nordeste de Amaralina (pattern P4) presented a higher thermal amplitude, with a minimum air temperature value of 25°C and a maximum value of 33°C. The predominant values were from 28°C (1st quartile) to 30°C (3rd quartile), with a median of 29°C. In Pituba (pattern P2), the air temperature values varied from 26°C to 29°C, with most values between 27°C (1st quartile) and 28°C (3rd quartile), with a median of 28°C. Therefore, Pituba registered a lower air temperature amplitude than Nordeste de Amaralina (Morais et al., 2011).

26The graphs in Figure 9 compare the hourly air temperatures measured on three different days, corresponding to spring, summer and autumn, in Salvador. Clearly, throughout the day, the air temperature curves at both measurement points, Pituba and Nordeste de Amaralina, are above that at the meteorological station, an expected outcome because of the large green area surrounding the meteorological station. However, the temperatures measured in Nordeste de Amaralina are also above those obtained in Pituba. Pattern P4, Nordeste de Amaralina, recorded the highest temperature values for all three measured days.

Figure 9: Comparison of the air temperatures for three different land use patterns

Figure 9: Comparison of the air temperatures for three different land use patterns

27Located close to each other, the two neighborhoods are subjected to similar geographic conditions. The main prominent difference resides in their local morphologies, making it possible to infer that each land use typology produces specific urban microclimates. Importantly, considering that the P4 pattern has one of the most deteriorated thermal environments in the city, presenting the lowest environmental quality and occupying the largest proportion of the territory of Salvador, the measurement results highlight the need for in-depth investigations into the inhomogeneity of the urban morphology and the effects of distinct land use patterns on thermal comfort conditions.

Method

28UCMap has two major components: UC-AnMap, or a synthetic climate function map, and UC-ReMap (Ren et al., 2011). Here, one presents the methodology used to calculate the UC-AnMap of Salvador, justifying the adoption of the MPO as an alternative tool within the historical and ongoing socioeconomic contexts. This method may help in the development of UCMaps in other cities under similar circumstances.

29In initial discussions concerning the availability of inputs to calculate the UC-AnMap of Salvador, it was decided to adapt the original UCMap methodology to the specific characteristics of the city, in particular, to the diversity of the land use typologies, which required a more detailed approach. This resulted in the development of the MPO to serve as an additional resource to calculate the UCMap of the city, taking advantage of the available data, albeit outdated at the beginning of our climate studies. Later results from specific climate research projects confirmed the appropriateness of this adjustment.

30Figure 10 illustrates the contribution of the MPO to the calculation of the UC-AnMap, highlighting its main differences in the details of the land use pattern categories. In this diagram, it can be seen that the urban functional areas are subdivided into distinct land use patterns: Pn, Pn+1, Pn+2, and so on. The MPO methodology considers the urban morphologies, even though urban functions also influence the urban climate.

Figure 10: Flowchart for the development of the Land Use Patterns Map (MPO) and the Urban Climatic Analytical Map (UC-AnMap)

Figure 10: Flowchart for the development of the Land Use Patterns Map (MPO) and the Urban Climatic Analytical Map (UC-AnMap)

Method for MPO 2004

31An MPO represents differences in the urban morphology according to distinguishing occupation patterns throughout the city. In the case of Salvador, these patterns mostly correspond to the residential areas of the city.

32Salvador’s first MPO was initiated in 2002 and finalized in 2004. The LACAM-Tec research group developed a non-digital methodology aimed at identifying, characterizing, and classifying areas via visual examinations of homogeneous land use patterns. This was followed by pattern classification using urban features, such as the proximity of buildings, building heights, and presence of vegetation. Unlike the initial UC-AnMap constructed for Berlin, the Salvador MPO 2004 generated a thematic map layer associating the land use morphologies with these urban characteristics, irrespective of their urban functions. The initial “handmade” maps were then migrated to a computer-aided design platform and then transferred to the QGIS system to calculate the 2006 UC-AnMap.

  • 4 It is not usual to find a scaled physical model in most cities; however, since 2005, the Google Ear (...)

33Figure 11 shows the methodology diagram used to generate MPO 2004. This process incorporated the researchers’ empirical knowledge of Salvador’s urban areas, subsidized by in-situ visits to various locations in the city, and a physical model of the city4 at a scale of 1:2,000, made available by the City Hall department, for the three-dimensional component. The primary two-dimensional information regarding the delimitation of the land use areas was derived from a non-digital map with topographic contour lines at a scale of 1:10,000, obtained from the official SICAR Cartographic Information System, Flight 1992 (CONDER, 1992), and from available aerophotogrammetric images.

Figure 11: Methodology diagram for the Salvador MPO 2004

Figure 11: Methodology diagram for the Salvador MPO 2004

Method for the 2006 UC-AnMap

34The first UC-AnMap of Salvador, published in 2006 (see Figure 12 for the methodology diagram), used three thematic maps, with each one considering different urban climate factors: (a) MPO 2004; (b) a topographic map based on contour lines from the non-digital map; and (c) a ventilation map using wind data from the Instituto Nacional de Meteorologia INMET station. The UC-AnMap was empirically drawn, supported by the expert supervision of Lutz Katzschner, who is also a co-author of this article. Therefore, for the most part, this analysis was qualitatively derived from data that were available at that time. Even though the need for a specialized analysis represents a limitation in the replicability of the development of the ventilation map, depending on the case, alternative methods could be used to circumvent such gaps. For example, technical cooperation agreements could be established with specialists from regional public universities in the format of a research project funded by public or inter/national non-governmental organizations, as well as consortiums aggregating near cities, which could share in the cost of hiring a single expert. Remote access via the Internet, and open data legislation with respect to public information, reduces costs and increases the probability of obtaining, at minimum, an empirical map of the ventilation.

Figure 12: Methodology diagram for the 2006 UC-AnMap of Salvador

Figure 12: Methodology diagram for the 2006 UC-AnMap of Salvador

35For each thematic map, weighting factors were selected, interpreted, and evaluated for each input datum according to its influence on the heat load and ventilation dynamic potential as well as additional data from the mobile measurements and mesoclimatic data provided by the meteorological station in Salvador. For the thematic map and the MPO, nine categories of differing urban morphologies (patterns P1–P9) were defined. Each received a weighting factor based on the study “Air Temperature and Occupancy Patterns in Salvador'' (Nery et al., 2003), which cross-analyzed in-situ mobile measurements with the locational pattern of their respective microclimatic measurement points (Table 1).

36The topographic thematic map divides the altitude into three different levels, followed by distinct weighting factors corresponding to the effect of the height on the thermal conditions. The lowest level was up to an altitude of 20 m, the second level was between 20 m and 60 m, and the highest level was above an altitude of 60 m.

37The ventilation thematic map considered the hilly relief in Salvador, as well as the wind data and flow into the urban area. The map was divided into six categories classified by the distance from the sea, air paths, high roughness, regional wind regime including the prevailing directions and average speeds, vegetation areas and dune areas (Table 1). The development of all of these maps relied on expert qualitative and empirical assessments. These maps were superimposed using a borrowed proprietary GIS program to produce a UC-AnMap, which was the result of the interaction between the morphological and urban structure data, e.g., the heat storage, roughness, heat island effect, and ventilation distribution. This qualitatively based GIS calculation generated a first UC-AnMap divided into classes. Each class corresponds to a thermal comfort condition, which integrates both the thermal load contributing a warming (+) effect to the thermal environment and the ventilation dynamic potential aspects, whose contributions constitute a cooling (−) effect (Table1).

Table 1: Weighting factors for the 2006 UC-AnMap

Classes ↓

Criteria

Thematic Map Layers

MPO 2004

Topography

Ventilation

Effect→

 

( + )

( - )

( - )

Ventilation

C

Central area: interior areas with high roughness

 

0

Ks

Coastal windy areas

 

 

2

V

Air paths

 

 

3

F -

Ventilated residential areas

 

 

1

DV

Vegetation areas

 

 

1

DK

Dune areas

 

 

2

Topography

L1

h > 60 m

 

1

 

L2

20 m < h < 60 m

 

0

 

L3

h < 20 m

 

1

 

Patterns

P1

Tall buildings (> 10 storeys), High density

8

 

 

P2

Tall buildings (> 10 storeys), Medium density

6

 

 

P3

Medium-height buildings (1-4 storeys), Medium density

4

 

 

P4

Small buildings (< 4 storeys), Very high density, No vegetation

7

 

 

P5

Small buildings (< 4 storeys) and Medium density

5

 

 

P6

Small buildings (< 4 storeys), Low density, Vegetation

3

 

 

P7

Small buildings (< 4 storeys), Low density, No Vegetation

2

 

 

P8

Specially large structures : industries, shopping centers, hospitals

1

 

 

P9

Green areas, environmental reserves, urban parks, vegetated squares

1

 

 

MPO 2004 indicates the Land Use Patterns Map published in 2006 for the city of Salvador
 “h” indicates the height above sea level.
“Very high density” implies adjoining buildings with no setbacks; “High density” implies small setbacks; “Medium density” implies medium setbacks; and “Low density” implies sparse buildings. 
 “No vegetation” may also imply that any existing vegetation has no effect at the topoclimatic scale.

Method for MPO 2019

  • 5 The City Hall Cartographic Mapping system for Salvador made the city’s orthoimages and the complete (...)
  • 6 The Finance Department of the Salvador Municipal Government granted special permission to access th (...)

38The MPO 2019 methodology diagram is shown in Figure 13. Starting in 2019, Salvador’s MPO was updated using the Google Earth viewing tools as a three-dimensional resource. The online availability of digital orthoimages (PMS, 2019)5, in association with Google Earth, expedited the process. The aforementioned tools and a stable version of the open source QGIS system were unavailable in the early 2000s and digital orthophotos were more easily obtainable on online resource platforms in mid-2019.6

  • 7 SIRGAS 2000 - System of Geocentric Reference for Americas.

39Accordingly, the MPO update procedure involved adjustments to the vector data and specific settings during the export process using the then-accessible QGIS version 2.18.28 and the System of Geocentric Reference for the Americas (SIRGAS) 20007 reference system.

40The digital orthophotos of the city served as a visual reference to review and reassess the polygons. Updating the polygon boundaries from the MPO 2004 version involved identifying urban built typologies with visually homogeneous textures visualized at a scale of 1:10,000 and, when necessary, at a scale of 1:5,000. In addition, Google Earth Street View was used to check specific blocks. This process resulted in an intermediary 582-polygon map (Land Use Patterns Polygons 2019, as seen in Figure 13), as classified below.

Figure 13: Methodology diagram for MPO 2019

Figure 13: Methodology diagram for MPO 2019

41The following procedural step involved classifying these (582) polygons into land use patterns according to their observable similarities via a purely visual assessment of the 1:10,000 scale map. The same previous MPO 2004 classification criteria—building density, building heights, and the presence of vegetation—were applied. The high resolution of the orthoimages allowed the addition of other visual criteria, such as the overall street width and the presence of bare soil and water bodies. This resulted in 12 categories of urban patterns in the updated MPO 2019.

  • 8 Note that the lot limit data are not fully available in the city’s mapping database. The lot limit (...)

42After generating MPO 2019, further additional quantitative criteria for the urban morphology per pattern were defined, primarily resulting from visual inhomogeneities only discernible at a larger scale, such as 1:5,000 or 1:2,000. Hence, the process was supplemented by characterizing the classified patterns using a quantitative approach, which takes the block, rather than the lot,8 as its basic unit. Accordingly, several morphological features were calculated to quantitatively characterize the urban patterns using the measurement tools provided by the City Hall Cartographic Mapping system and the Google Earth platform. Currently, two to four blocks per pattern constitute the samples selected and calculated by the researchers as a function of their representativeness, data completeness, and availability. For example, a block necessarily requires the existence of surrounding streets; however, this is not the case for some land use pattern blocks. In addition, Google Street View does not entirely cover Salvador’s urban fabric.

43The features, including the building heights (estimated by the number of floors as seen in Google Street View), the frontal, rear, and lateral setbacks between the buildings, and the street width, were the primary urban parameters measured; because of the reduced number of calculated blocks, these features were then treated only according to the descriptive statistics to provide an initial quantification for each pattern. At this stage, the range and average from the set of block parameters were considered to represent a quantitative standard for each respective pattern. The above-mentioned data blocks were then used to generate urban and morphological indicators and indices for each pattern, namely, the building plan fraction, empty space fraction, urban canyon aspect ratio, sky view factor, mean height of roughness elements, and density of the built volume. Vieira (2020) summarizes the equations and the urban morphological representation model of the blocks, including the calculation of each descriptor.

44Unlike MPO 2004, which generated only one layer in its final form (the MPO layer) for the 2006 UC-AnMap calculation, MPO 2019 improved on the previous methodology by enabling the derivation of thematic map layers related to the different morphological indices of land use shown for the 2019 UC-AnMap (Figure 14). Importantly, the values of these indices were taken as a basis for assigning weighting factors to each built pattern (Table 2) according to the 2019 UC-AnMap method detailed below.

Method for the 2019 UC-AnMap

45Figure 14 presents the thematic maps that make up the 2019 UC-AnMap. Most of the layers represent an urban climate factor derived from MPO 2019, i.e., Roughness, Aspect Ratio, Building Volume, Openness, Green Areas, Bare Soil, and Water Bodies, while the factors Wind Corridors and Topoclimate are derived from the Digital Surface Model (DSM) (PMS, 2019) and the DEM (INPE, 2011), respectively. Both Figure 14 and Table 2 graphically and numerically display how the MPO assisted in the development of the urban climatic map. The values assigned to represent the averages were obtained from the values calculated for the sample blocks.

46The development of the Wind Corridor layer relied on a climatological expert analysis to identify the channeled areas influenced by the valleys and slopes from the DSM associated with the prevailing wind directions (Acero, Katzschner, 2015; Baumüller, Reuter, 2015). The topoclimate layer originated from the DEM and, with the assistance of a consultant, involved integrating the wind and altitude in a combined analysis process.

Figure 14: Methodology diagram for the 2019 UC-AnMap

Figure 14: Methodology diagram for the 2019 UC-AnMap

47These layers were used for thermal load analyses (positive effect) and to determine the dynamic potential (negative effect) (Table 2) in geoprocessing the UC-AnMap (Figure 14). Table 2 shows the weighting factors attributed to the classes of each thematic map layer. In general, these weighting factors were based on theoretical climate knowledge combined with the researchers’ understanding of the city and measured data from pattern blocks.

48The weighting factors of the topography-based layers, Topoclimate and Wind Corridors, were defined based on the physical principles of air movement. The weighting factors for the MPO 2019 layers, patterns P1–P6, were adapted from the calculated urban indices obtained from the sample block data (Vieira, 2020), whereas the values attributed to large built urban areas, such as shopping malls, hospitals, and the airport, were theoretically based on heat accumulation due to the buildings and impervious cover. For the natural landscape layers, e.g., Green Areas, Bare Soil, and Water Bodies, the weighting factors refer to either their presence or their absence (Table 2).

Table 2: Thematic map weighting factors for UC-AnMap 2019

Classes↓

Criteria

Thematic <ap Layers

Topoclimate

Wind Corridors

Roughness (Z0)

Aspect Ratio

Building Volume

Openess

Green Space

Bare Soil

Water Bodies

Effect →

(+)

(-)

(+)

(+)

(+)

(+)

(+)

(+)

(+)

Patterns

P1

> 10 storeys, high density

8

12

12

6

0

0

0

PX1

5 storeys, high density

7

10

6

5

0

0

0

P2

> 10 storeys, middle density

6

3

10

2

0

0

0

P3

5 storeys, middle density

4

8

7

3

0

0

0

P4

5 storeys, high density

3

11

8

7

0

0

0

PX4

5 storeys, high density

5

12

9

7

0

0

0

P5

4 storeys, middle density

2

4

5

4

0

0

0

P6

4 storeys, low density

1

0

0

1

0

0

0

P8

Services zones

1

2

2

1

0

0

0

P9

Green Space

0

0

0

0

1

0

0

P10

Bare Soil

0

0

0

0

0

1

0

P11

Water Bodies

0

0

0

0

0

0

1

Wind Corridors

Wind channels hills

4

Plains

0

Topoclimate

Hill tops ridges

0

Slopes

6

Coastal areas

3

Definitions of the visual criteria for the building density see Table 1.

49The thematic map layers were used to generate the UC-AnMap according to the attributes in the weighting factor table (Table 2) via the QGIS calculation. This calculation balanced the influence of each urban climate factor in each pattern. With the availability of newer or updated urban climate factor data, such as slopes, slope orientations, DEMs, intra-urban ventilation simulations, and air pollution, new themes can be incorporated into the UC-AnMap, enabling the methodology to become more comprehensive and the results to become more precise. The next step is to validate the most recent UC-AnMap of Salvador using in-situ measurements and neighborhood-scale thermal comfort simulations.

Results and analysis

50The first land use classification for Salvador, MPO 2004 (Figure 15), used data from 1992 to identify the nine patterns described in Table 3, taking the building heights, built densities, vegetation, and special building areas as criteria and, in a further stage, associating them with their respective weighting factors to generate the 2006 UC-AnMap.

Figure 15: Salvador MPO 2004

Figure 15: Salvador MPO 2004

51Based on the 1992 data, the largest territorial area (39.8%) of Salvador corresponds to pattern P9, Green Areas. Its main descriptor tracks the concentration of tall trees in medium to large areas of the municipal territory, that is, in a few large environmental reserves, urban parks, and open areas with trees. Pattern P4 covered the second largest percentage area at 28.3%. This typical pattern, mostly originating from self-built spontaneous and informal land occupation, presents a very high-density typology that results in practically no open areas or vegetation. Patterns P1 and P2 also correspond to dense areas of the city with higher buildings. The difference between these two patterns lies in the setbacks between their buildings, where the former presents neither lateral nor frontal setbacks while the latter does. Together they cover only approximately 4% of the total urban area. Pattern P3 covers approximately 1% of the city and consists of medium to high buildings and a medium built density. Patterns P5 and P6 each correspond to approximately 9% of the total municipal area. Both consist of low-rise buildings. Pattern 8 is a special classification for large building areas, such as industries, dry ports, shopping centers, bus stations, and hospitals (see the pattern criteria information in Table 1).

52In addition to MPO 2004, the 2006 UC-AnMap incorporated thematic maps of the topography and an informed ventilation analysis. The topographic map layer was classified according to the altitude relative to sea level. Higher altitudes were related to both slightly lower temperatures and greater wind exposure, which, potentially, could qualitatively result in more thermally comfortable areas. For the ventilation map layer, areas were distributed according to their exposure to the prevailing winds (Nery et al., 2006; Andrade et al., 2015; Nery et al., 2019).

53The UC-AnMap (Figure 16) resulted in seven different climatic classifications: maximum heat island, heat island, reduced ventilation and small heat island, vegetation climate, strongly ventilated areas, and dune climate. The spatial distributions of these categories over the city are very different from the typical heat island effect for a continental city because the weighting factors attributed to the land use were scattered throughout the urban fabric. This can occur in coastal areas because of either high urban densities or wind shadows. The maximum heat island effect occurs in the older city areas, along the geological fault, and in inland areas. Less intense heat islands were scattered throughout the entire area depending on the integration of each factor. Figure 16 indicates that strongly ventilated areas occupy a small percentage of the total area and are concentrated along the coast, air-path corridors and in higher altitude areas.

Figure 16: 2006 UC-AnMap of Salvador

Figure 16: 2006 UC-AnMap of Salvador

54The updated MPO 2019 is shown in Figure 17. The number of classified patterns increased from 9 to 12. Table 3 presents a brief description of the urban parameters that characterize each of the various patterns of land use in the city of Salvador, the total occupied area per pattern, and the relative percentages of their spatial distributions. Note that the criterion regarding vegetated areas within the urban fabric itself refers to patterns P1–P6. The presence or absence of vegetation indicated in Table 3 is related to the topoclimatic scale, not the microclimatic scale; as such, it expresses the vegetation effect at the topoclimatic scale and should be interpreted accordingly.

Figure 17: MPO 2019 of Salvador

Figure 17: MPO 2019 of Salvador

55The criteria established for identifying each pattern of MPO 2019 remain basically the same as those established for MPO 2004, that is, the height of the buildings, block density, street width, impervious cover, and presence of vegetation. In MPO 2019, the P1 pattern is characterized by blocks with buildings that usually have 5–15 floors, occupying 0.25% of the continental area of Salvador; meanwhile, the P1 pattern in MPO 2004 was characterized by blocks with buildings having approximately 10 floors. The PX1 pattern, a new pattern, has characteristics very similar to P1, differing only in the height of the buildings, which are lower, up to five floors, occupying 0.51% of the area. This pattern was defined to allow its future classification as an isolated pattern or its insertion into a more prevalent occupation pattern in the city (Table 3).

56Patterns P2 (3.84%), P3 (4.51%), P5 (11.40%), P6 (1.53%), and P8 (13.50%) remain the same as in the previous MPO with respect to their individual distinction criteria. The P4 pattern (28.20%) still occupies the largest area, with spontaneous occupation, lower height buildings with little or no setbacks, and narrow streets. It consists of informal row-house buildings of up to five floors and presents an ever-increasingly high density. PX4 (1.63%) is a new pattern that resembles P4, differing from it only in its planned format, which results in wider streets.

57The P7 pattern is no longer present in Salvador. This pattern consisted of low-height buildings, low density, and areas with large amounts of pavement and no vegetation. The availability of higher accuracy images has resulted in these P7 areas being subdivided and incorporated into neighboring patterns: patterns P9 (26.98%), P10 (6.80%), and P11 (0.86%), which represent green areas, bare soil, and water bodies, respectively.

Table 3: Description of the MPO 2019 patterns

Pattern

Land Use Pattern Features

Occupied area

Number of floors

Urban density

Street width

Paving/ imperviousness

Vegetation presence

(km2)

(%)

P1

5 to 15

High

Wide

High

No

0.70

0.25%

PX1

< 5

High

Wide

High

No

1.41

0.51%

P2

> 10

Medium

Wide

High

No

10.71

3.84%

P3

< 5

Medium

High

High

No

12.61

4.51%

P4

< 5

High

Narrow

High

No

78.66

28.2%

PX4

< 5

High

Wide

High

No

4.54

1.63%

P5

< 4

Medium

Wide

Medium

Yes

31.82

11.4%

P6

< 4

Low

Wide

Low

Yes

4.27

1.53%

P8

Large area special zones: malls, universities, airports, industries etc.

37.63

13.5%

P9

Green areas, environmental reserves, urban parks, vegetated squares

75.25

26.98%

P10

Bare soil with little or no vegetation cover 

19.01

6.8%

P11

Water bodies, rivers, lakes

2.40

0.86%

Total

279

100,0%

Definitions of the visual criteria for the urban density see Table 1.
No vegetation may also imply that any existing vegetation has no effect at the topoclimatic scale.
The total area of 269.55 km2 was obtained by adding up the partial values of the occupied areas. It differs from the total continental municipal area of Salvador of 279 km2 (SEDHAM, 2009). This is a result of the migration process from the “handmade” non-digital map to the digitized computer-aided design base. This error was corrected after updating to MPO 2019 by proportional distribution. 

58Comparing the two MPOs depicting Salvador’s land use based on data separated by 25 years, the rapid urban expansion of Salvador is evident because the urban area has essentially spread over the entire territory of the municipality. Note that verticalization has caused a densification of the consolidated urbanized areas, the visual perception of which requires careful attention. The non-urbanized areas shown in MPO 2004 correspond to the most recent urban expansion, which occurred primarily in formerly dense green areas, usually remnants of the Atlantic Forest.

59Compared with MPO 2004, MPO 2019 exhibited a larger number of polygons arranged in a complex mosaic of urban patterns. The expansion toward the northern areas is a consequence of population growth, the pressure of real estate speculation, and social housing programs occupying the peripheral areas of Salvador, resulting from an economic situation that boosted civil construction and the purchase of cheaper land plots for real estate investment (Mettig-Rocha et al., 2019).

60Higher accuracy and precision in the determination of the polygons was possible in the later MPO 2019 following the availability of the latest technical and computational resources and high-quality digital orthophotos. The orthophotos, with a spatial resolution of 10 cm and a radiometric resolution of 16 bits, resulted from an aerophotogrammetric survey supported by precision terrestrial geoid stations at an urban scale of 1:1,000 (PMS, 2019).

61The more precise MPO 2019 led to higher definition in the 2019 UC-AnMap (Figure 18), which includes six new layers derived from MPO 2019 (Figures 13 and 15). This resulted in seven urban climate classes selected via GIS processing for the 2019 UC-AnMap. The 2019 UC-AnMap still needs to be validated; however, here we focus on the methodological process.

Figure 18: 2019 UC-AnMap of Salvador

Figure 18: 2019 UC-AnMap of Salvador

62As opposed to the 2006 UC-AnMap, which used a climatope concept closer to its earlier definition, which was basically equivalent to land use zones, the 2019 UC-AnMap adopted an urban climate classification related to the resulting thermal load balance. Therefore, the procedure for its development followed a similar methodology to that of the UC-AnMap developed for Hong Kong (Ng, Ren, 2012). It was, nevertheless, adapted to the climatic conditions of Salvador because the lower limit of comfort is rarely reached, as previously noted.

63The classes are ordered from low to high with low numbers indicating the lowest net balances (classes 1–3) and the best conditions of thermal comfort in Salvador, i.e., a lower thermal load and a good dynamic potential are considered favorable. Conversely, larger number classes (5–7) indicate higher net balances and a negative impact on thermal comfort resulting from positive thermal stress. The intermediate class 4 represents a moderate net balance and therefore a moderate impact on thermal comfort (Figure 18).

64The percentage and aerial distribution of the thermal comfort conditions are shown in Table 4.Together, classes 1–3 cover approximately 51.0% of the urban area. At the upper end, 37.6% of the territory was classified as belonging to classes 5–7, while class 4 accounted for 11.4% of the continental area of Salvador. However, the spatial distribution of the classes over the city in the 2019 UCMap is more inhomogeneous and more fragmented than that in the 2006 UCMap.

Table 4: Urban climate classes in the 2019 UC-AnMap

CLASS

IMPACT ON THERMAL COMFORT

AREA (km²)

PERCENTEGE (%)

Class 1

Neutral

39.41

14.12

51.0

Class 2

Slight

76.03

27.25

Class 3

Moderately Low

26.87

9.63

Class 4

Moderate

31.73

11.37

11.37

Class 5

Moderately Strong

23.10

8.28

37.6

Class 6

Strong

51.65

18.51

Class 7

Very Strong

30.21

10.83

279

100

65Clearly, the areas with the lowest impacted thermal comfort (classes 1–3) are concentrated in the northern region of the territory (Figure 18), even though fragmented and small portions of these classes are scattered throughout the rest of the city. The northern concentration coincides with the largest environmental reserves and green areas in the city.

66Meanwhile, classes 6 and 7 (29.3%) are clustered in two areas in which disadvantaged socioeconomic classes reside. The first is composed of a large continuous stretch that cuts across the city from north to south, facing the Todos-os-Santos Bay and running along the geological fault, both in the lower and upper areas. The second is distributed in the central part of the city in a fragmented pattern.

67Note that, if the aggregated areas of the Green Areas (P9), Bare Soil (P10), and Water Bodies (P11) were to be excluded from the total territorial area, the remaining region would be restricted to areas that are effectively inhabited or house the tertiary economic sector. Given that the city’s economy is primarily focused in the service and commercial sectors, worse results would arise for the residential areas: classes 1 and 2 would be reduced to 14.5% of the remaining area, while classes 6 and 7 would now constitute 45.9% of the remaining area.

68There is a strong relationship between these results and the way in which the city is occupied. “Hard borders” are commonly found in the UC-AnMap of Salvador, that is, contiguous regions presenting significant differences in climate classes. Sometimes these abrupt divisions can be derived from factors such as altitude variations or the existence of wind corridors in the immediate neighboring regions. However, these differences may also result from the proximity of land use patterns possessing very different indices. Indeed, to improve the UCMap of Salvador, it is necessary to investigate effects, for example, at the neighborhood scale, during the geoprocessing of factors relevant to the urban climate.

Considerations and conclusions

69The development of the Salvador UC-AnMap methodology was primarily enabled by the MPO, initially as a layer in the 2006 UC-AnMap and later, in the 2019 UC-AnMap, as a basis for obtaining thematic map layers of the urban land use. In addition to considering the diversity of the existing patterns in the spatially segregated city of Salvador, the MPO provides an alternative resource in the absence of digital data regarding the specificities of the urban land use. It enabled classifications according to factors that influence the urban climate, such as the building volume and roughness required in the UCMap methodology.

70The development and universalization of technology and information benefited the production of the second MPO and UC-AnMap by providing free and improved computational tools that granted access to higher-quality and updated data. Hence, the 2019 UC-AnMap was produced with greater accuracy, a higher availability of data, and more information concerning the urban morphology.

71It is expected that this trend will continue, increasing the availability of three-dimensional information and expanding its coverage of towns and cities, especially those requiring the meeting of urban planning demands, such as medium-size and larger cities. To illustrate this point, urban legislation in Brazil, with few exceptions, requires a master plan for towns and cities with more than 20,000 inhabitants.

72Currently, Google Earth does not provide three-dimensional data for all cities, nor does it provide complete coverage of the urban fabric of all municipalities. However, a town or city complying with mandates demanding a master plan is expected to possess minimum essential data for its development, such as a non-digital cartographic map. Moreover, depending on the individual town or city, as well as the size of the information gaps and the political will, missing information could be supplemented by local data collected by the local government officials or via relatively inexpensive services contracted to third parties.

73When the issues of climate change, energy efficiency, and the safety and salubrity of buildings and urban environments are considered, it is evident that the urban climate should be an essential basis for city planning from the onset. Because a UC-AnMap is a helpful tool to achieve this goal, the authors propose the methodology presented here, which requires minimal resources, as an initial strategy for its production. Consequently, MPOs are suggested as the starting methodological procedure to generate UC-AnMaps under the highlighted circumstances.

74Regardless, a UCMap needs to be understood as a process, where the first step is a more general, qualitative approach to urban climate mapping. The second stage, or revision of the UCMap, should include greater precision and details because of improved information and research. Medium and small cities can benefit from the result of the first step to guide urban planning strategies and recommendations. Larger cities with greater complexity require greater methodological investment in terms of consulting, research, and hence financial resources. Under circumstances with limited resources, a longer process may occur, as is the case here.

75A second improved first-stage version of the Salvador 2019 UC-AnMap is still under construction, having been enhanced by updating the city’s first MPO. However, the fact that the UC-AnMap has not yet been validated is one of the aspects that has delayed the development of a UC-ReMap thus far. The lack of validation alone does not make the development of a UC-ReMap unfeasible; however, it does reduce its certainty. Nevertheless, even at the current stage, both the MPO and the UC-AnMap of Salvador allow a better understanding of the urban thermal environment and are capable of offering relevant input toward urban planning decision-making. Clearly, for smaller and less complex cities than Salvador, an incipient UCMap would help prevent the creation of future detrimental conditions, whose correction can be a challenge after consolidation of the urban fabric.

76The next goal of urban climate research in Salvador will be to establish a more objective weighting system for the input layers and their categories. This requires further investigation. Another goal is to develop a methodology for validating the UC-AnMap with the expected minimal resources. In actuality, the production of the UC-Map requires additional technical and financial resources; however, in Salvador, such work is not yet part of the governmental agenda. In addition, it is necessary to develop a more accurate dynamic ventilation potential map to further support the information derived for the UC-AnMap.

77In conclusion, this study indicates that it is feasible to use the UCMap methodology in small- and medium-size Brazilian cities because this approach provides a starting point using commonly available data, such as topographic data from non-digital land use maps. Once digitized, such data can be used for GIS calculations; in addition, wind data can be requested from the closest official meteorological station and the Google Earth platform is already available for many cities. Given climate change, it is prudent to include the climate dimension as a relevant urban planning criterion, despite the low availability of data.

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Bibliographie

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Notes

1 This technical-scientific collaboration with the German institutions, first the University of Kassel and then the Institute for Climate and Energy Concepts (INKEK), primarily involved Lutz Katzschner, who has been a consultant to urban climate studies at LACAM-Tec from the onset.

2 A well-known term, usually translated as “shantytown.”

3 According to IBGE (2017), the total municipal area of Salvador is 693,453 km2. The data presented here were calculated by the researchers using QGIS and the municipal shapefiles. Other official sites also reported different values. See also SEDHAM (2009).

4 It is not usual to find a scaled physical model in most cities; however, since 2005, the Google Earth platform has provided an alternative to such models. Currently, Google Earth does not include three-dimensional data for all cities, nor does it include complete coverage of the urban fabric of all municipalities. Depending on the case and the size of the gaps, missing information can be complemented by local data collected by the public agency’s own representatives or via relatively inexpensive services contracted to third parties. The current trend, however, is an increasing availability of three-dimensional information and an expansion of its coverage.

5 The City Hall Cartographic Mapping system for Salvador made the city’s orthoimages and the complete digital surface model (DSM) of the municipality available only around mid-2019. However, as of mid-2021, only 40% of the city’s territory had been finalized for the digital terrain model (DTM). In addition, the Database Set of Vector Geospaces (CDGV) still has an empty Attributes table. See PMS (2019) for the site.

6 The Finance Department of the Salvador Municipal Government granted special permission to access the 2016–2017 orthophotos via the Web Map Service (WMS) on December 4, 2018. In mid-2019, the online City Hall Cartographic Mapping system for Salvador included additional useful resources, which facilitated measurements of the building footprint, street width, roughness length, and other urban parameters.

7 SIRGAS 2000 - System of Geocentric Reference for Americas.

8 Note that the lot limit data are not fully available in the city’s mapping database. The lot limit information is incomplete in the SICAR database and, since mid-2019 in CDGV. Therefore, blocks appear to be better descriptors for urban land use patterns. However, lots are usually adopted for calculating urban parameters by the City Planning Department and ordinances.

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Table des illustrations

Titre Figure 1: Population growth of Salvador, Brazil
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-1.png
Fichier image/png, 408k
Titre Figure 2: Urban expansion of Salvador
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-2.png
Fichier image/png, 684k
Titre Figure 3: Location of Salvador using Google Earth images
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-3.png
Fichier image/png, 1,0M
Titre Figure 4: Aerial photo of the peninsula of Salvador
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-4.png
Fichier image/png, 1,4M
Titre Figure 5: Climatological normals showing the average minimum and maximum temperatures in Salvador for three different time periods
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-5.png
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Titre Figure 6: Photo of an urban area in Salvador subject to landslides
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-6.jpg
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Titre Figure 7: Aerial photo of part of the city including Nordeste de Amaralina (P4) and Pituba (P2) neighborhoods and other urban patterns
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-7.jpg
Fichier image/jpeg, 780k
Titre Figure 8: Photos and locations of the measurement points in the Nordeste de Amaralina and Pituba neighborhoods, and INMET meteorological station.
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-8.jpg
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Titre Figure 9: Comparison of the air temperatures for three different land use patterns
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-9.png
Fichier image/png, 454k
Titre Figure 10: Flowchart for the development of the Land Use Patterns Map (MPO) and the Urban Climatic Analytical Map (UC-AnMap)
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-10.png
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Titre Figure 11: Methodology diagram for the Salvador MPO 2004
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-11.jpg
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Titre Figure 12: Methodology diagram for the 2006 UC-AnMap of Salvador
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-12.jpg
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Titre Figure 13: Methodology diagram for MPO 2019
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-13.jpg
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Titre Figure 14: Methodology diagram for the 2019 UC-AnMap
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-14.jpg
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Titre Figure 15: Salvador MPO 2004
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-15.png
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Titre Figure 16: 2006 UC-AnMap of Salvador
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-16.jpg
Fichier image/jpeg, 284k
Titre Figure 17: MPO 2019 of Salvador
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-17.png
Fichier image/png, 322k
Titre Figure 18: 2019 UC-AnMap of Salvador
URL http://journals.openedition.org/cybergeo/docannexe/image/38634/img-18.png
Fichier image/png, 202k
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Référence électronique

Tereza Moura, Jussana Nery, Eduardo Prado, Carolina Vieira, Heliana Mettig Rocha et Lutz Katzschner, « Urban Climatic Map of Salvador, Brazil, using a Land Use Pattern Methodology », Cybergeo: European Journal of Geography [En ligne], Cartographie, Imagerie, SIG, document 1010, mis en ligne le 31 mars 2022, consulté le 19 mai 2022. URL : http://journals.openedition.org/cybergeo/38634 ; DOI : https://doi.org/10.4000/cybergeo.38634

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Auteurs

Tereza Moura

Faculty of Architecture - Federal University of Bahia, Brazil, assistant professor, tereza_moura2015@gmail.com

Jussana Nery

Faculty of Architecture - Federal University of Bahia, Brazil, retired assistant professor, jussana.nery@gmail.com

Eduardo Prado

Faculty of Architecture - Federal University of Bahia, Brazil, assistant professor, epprado@gmail.com

Carolina Vieira

Faculty of Architecture - Federal University of Bahia, Brazil, assistant professor, carolinanvieira@gmail.com

Heliana Mettig Rocha

Faculty of Architecture - Federal University of Bahia, Brazil, assistant professor, helianamettig@ufba.br

Lutz Katzschner

INKEK - Institute for Climate and Energy Strategies, Germany, international consultant, katzschn@uni-kassel.de

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La revue Cybergeo est mise à disposition selon les termes de la Licence Creative Commons Attribution 4.0 International.

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