IMMerSe: An integrated methodology for mapping and classifying precarious settlements
The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a m...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
da Fonseca Feitosa, Flávia [verfasserIn] Moutinho Duque de Pinho, Carolina |
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021transfer abstract |
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Enthalten in: Tau kinetics in the human cns - Sato, Chihiro ELSEVIER, 2015, putting the world's human and physical resource problems in a geographical perspective, New York, NY [u.a.] |
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| Übergeordnetes Werk: |
volume:133 ; year:2021 ; pages:0 |
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10.1016/j.apgeog.2021.102494 |
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| 520 | |a The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes. | ||
| 520 | |a The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes. | ||
| 650 | 7 | |a Co-production of knowledge |2 Elsevier | |
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| 650 | 7 | |a Typologies of precarious settlements |2 Elsevier | |
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10.1016/j.apgeog.2021.102494 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001447.pica (DE-627)ELV054595711 (ELSEVIER)S0143-6228(21)00110-7 DE-627 ger DE-627 rakwb eng 610 VZ 530 VZ 52.56 bkl da Fonseca Feitosa, Flávia verfasserin aut IMMerSe: An integrated methodology for mapping and classifying precarious settlements 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes. The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes. Co-production of knowledge Elsevier Slums Elsevier Classification modelling Elsevier Precarious settlements mapping Elsevier Informal settlements Elsevier Typologies of precarious settlements Elsevier Vieira Vasconcelos, Vitor oth Moutinho Duque de Pinho, Carolina oth Frizzi Galdino da Silva, Guilherme oth da Silva Gonçalves, Gilmara oth Correa Danna, Lana Carolina oth Seixas Lisboa, Flávia oth Enthalten in Elsevier Sato, Chihiro ELSEVIER Tau kinetics in the human cns 2015 putting the world's human and physical resource problems in a geographical perspective New York, NY [u.a.] (DE-627)ELV01283484X volume:133 year:2021 pages:0 https://doi.org/10.1016/j.apgeog.2021.102494 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_11 GBV_ILN_20 GBV_ILN_70 GBV_ILN_2547 52.56 Regenerative Energieformen alternative Energieformen VZ AR 133 2021 0 |
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10.1016/j.apgeog.2021.102494 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001447.pica (DE-627)ELV054595711 (ELSEVIER)S0143-6228(21)00110-7 DE-627 ger DE-627 rakwb eng 610 VZ 530 VZ 52.56 bkl da Fonseca Feitosa, Flávia verfasserin aut IMMerSe: An integrated methodology for mapping and classifying precarious settlements 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes. The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes. Co-production of knowledge Elsevier Slums Elsevier Classification modelling Elsevier Precarious settlements mapping Elsevier Informal settlements Elsevier Typologies of precarious settlements Elsevier Vieira Vasconcelos, Vitor oth Moutinho Duque de Pinho, Carolina oth Frizzi Galdino da Silva, Guilherme oth da Silva Gonçalves, Gilmara oth Correa Danna, Lana Carolina oth Seixas Lisboa, Flávia oth Enthalten in Elsevier Sato, Chihiro ELSEVIER Tau kinetics in the human cns 2015 putting the world's human and physical resource problems in a geographical perspective New York, NY [u.a.] (DE-627)ELV01283484X volume:133 year:2021 pages:0 https://doi.org/10.1016/j.apgeog.2021.102494 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_11 GBV_ILN_20 GBV_ILN_70 GBV_ILN_2547 52.56 Regenerative Energieformen alternative Energieformen VZ AR 133 2021 0 |
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10.1016/j.apgeog.2021.102494 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001447.pica (DE-627)ELV054595711 (ELSEVIER)S0143-6228(21)00110-7 DE-627 ger DE-627 rakwb eng 610 VZ 530 VZ 52.56 bkl da Fonseca Feitosa, Flávia verfasserin aut IMMerSe: An integrated methodology for mapping and classifying precarious settlements 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes. The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes. Co-production of knowledge Elsevier Slums Elsevier Classification modelling Elsevier Precarious settlements mapping Elsevier Informal settlements Elsevier Typologies of precarious settlements Elsevier Vieira Vasconcelos, Vitor oth Moutinho Duque de Pinho, Carolina oth Frizzi Galdino da Silva, Guilherme oth da Silva Gonçalves, Gilmara oth Correa Danna, Lana Carolina oth Seixas Lisboa, Flávia oth Enthalten in Elsevier Sato, Chihiro ELSEVIER Tau kinetics in the human cns 2015 putting the world's human and physical resource problems in a geographical perspective New York, NY [u.a.] (DE-627)ELV01283484X volume:133 year:2021 pages:0 https://doi.org/10.1016/j.apgeog.2021.102494 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_11 GBV_ILN_20 GBV_ILN_70 GBV_ILN_2547 52.56 Regenerative Energieformen alternative Energieformen VZ AR 133 2021 0 |
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10.1016/j.apgeog.2021.102494 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001447.pica (DE-627)ELV054595711 (ELSEVIER)S0143-6228(21)00110-7 DE-627 ger DE-627 rakwb eng 610 VZ 530 VZ 52.56 bkl da Fonseca Feitosa, Flávia verfasserin aut IMMerSe: An integrated methodology for mapping and classifying precarious settlements 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes. The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes. Co-production of knowledge Elsevier Slums Elsevier Classification modelling Elsevier Precarious settlements mapping Elsevier Informal settlements Elsevier Typologies of precarious settlements Elsevier Vieira Vasconcelos, Vitor oth Moutinho Duque de Pinho, Carolina oth Frizzi Galdino da Silva, Guilherme oth da Silva Gonçalves, Gilmara oth Correa Danna, Lana Carolina oth Seixas Lisboa, Flávia oth Enthalten in Elsevier Sato, Chihiro ELSEVIER Tau kinetics in the human cns 2015 putting the world's human and physical resource problems in a geographical perspective New York, NY [u.a.] (DE-627)ELV01283484X volume:133 year:2021 pages:0 https://doi.org/10.1016/j.apgeog.2021.102494 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_11 GBV_ILN_20 GBV_ILN_70 GBV_ILN_2547 52.56 Regenerative Energieformen alternative Energieformen VZ AR 133 2021 0 |
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10.1016/j.apgeog.2021.102494 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001447.pica (DE-627)ELV054595711 (ELSEVIER)S0143-6228(21)00110-7 DE-627 ger DE-627 rakwb eng 610 VZ 530 VZ 52.56 bkl da Fonseca Feitosa, Flávia verfasserin aut IMMerSe: An integrated methodology for mapping and classifying precarious settlements 2021transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes. The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes. Co-production of knowledge Elsevier Slums Elsevier Classification modelling Elsevier Precarious settlements mapping Elsevier Informal settlements Elsevier Typologies of precarious settlements Elsevier Vieira Vasconcelos, Vitor oth Moutinho Duque de Pinho, Carolina oth Frizzi Galdino da Silva, Guilherme oth da Silva Gonçalves, Gilmara oth Correa Danna, Lana Carolina oth Seixas Lisboa, Flávia oth Enthalten in Elsevier Sato, Chihiro ELSEVIER Tau kinetics in the human cns 2015 putting the world's human and physical resource problems in a geographical perspective New York, NY [u.a.] (DE-627)ELV01283484X volume:133 year:2021 pages:0 https://doi.org/10.1016/j.apgeog.2021.102494 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_11 GBV_ILN_20 GBV_ILN_70 GBV_ILN_2547 52.56 Regenerative Energieformen alternative Energieformen VZ AR 133 2021 0 |
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The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes. |
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The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes. |
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The widespread presence of precarious settlements in developing countries remains a persistent and relevant issue to be addressed. Developing well-informed strategies to tackle it demands up-to-date and reliable information on these settlements, which are usually unavailable. This paper proposes a methodology for mapping and classifying precarious settlements into different typologies. The methodology, named IMMerSe (Integrated Methodology for Mapping and Classifying Precarious Settlements), integrates methods, spatial data and knowledge from different sources and nature. It consists in a flexible and easy-to-follow classification framework that involves: (a) integration of a wide range of physical, environmental, morphological, and census-derived variables; (b) estimation of logistic regression models to generate surfaces of probability for different typologies of precarious settlements; and (c) identification and classification of precarious settlements through classification trees. All stages rely on the collaboration between researchers and policymakers, in a process of co-production of knowledge including conceptual development, understanding local contexts, model building, validation of results, and improvement of applied tools for housing policies. The methodology was applied to the Metropolitan Region of Baixada Santista, Brazil. In order to evaluate the replication potential of this methodology, the models that were built based on data from Baixada Santista, were then applied to another Brazilian area, the ABC Region. The results describe the distinctive features of each settlement typology, help to identify previously unknown precarious settlements in the studied regions, and contribute to produce knowledge for policy purposes. |
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