A comparison of address geocoding techniques – case study of the city of Zagreb, Croatia

Cetl, Vlado; Kliment, Tomáš; Jogun, Tomislav

doi:10.1080/00396265.2016.1252517

Cited by 12 publications

(11 citation statements)

References 15 publications

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“…Collected home addresses (Fig. 2) were used to geocode each subject by means of MMQGis QGIS plugin; positioning accuracy achieved by MMQGis is declared lower than 25m (Cetl et al, 2018). Reference frame is WGS84 UTM 32N.…”

Section: 3epidemiological Datamentioning

confidence: 99%

Geomatics and epidemiology: Associating oxidative stress and greenness in urban areas

Petris

Squillacioti²,

Bono³

et al. 2021

Environmental Research

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Green spaces may benefit human health mainly by mitigating noise and air pollution, promoting physical or social activities and improving mental health. Based on the influence that green space exposure seems to exert on Public Health and using a multidisciplinary approach, we investigated, the association between oxidative stress (OS) and green exposure in children. Overall, 207 subjects (10-13 yrs.) living in Torino (NW-Italy) were involved in this study. Each participant provided a urinary sample, used to quantify a reliable OS biomarker (15-F2t-IsoP), and their residence addresses, used for geocoding. Green exposure was characterised by calculating i) the Soil Adjusted Vegetation Index (SAVI) within fixed buffers around each participant's home, using remotely-sensed data; ii) Tree Map accounting for evergreen/broadleaf species;iii) The percentage of green cover (PGC). Significant negative correlation (Pearson's r = -0.758, p < 0.001) between PGC and 15-F2t-IsoP was found. Greater SAVI was associated with lower OS (Pearson's r = -0.717, p < 0.001). Noticeably, evergreens seemed to determine a significant OS reduction compared to broadleaves (slope = -0.12 and -0.02, respectively; Warton-test F = 12.48, p = 0.0011). Finally, a spatial distribution of 15-F2t-IsoP estimates map, overlying with 2011 Census Data on sameaged dwellers of Torino, was generated. Predictive models accounting for green spaces influence on OS can be useful tool derived from geomatic employ in Public Health field. Future developments of such a multidisciplinary approach should be considered in urban planning and policy-makers decisions to better define priority zones to requalify in urban settings.

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Section: 3epidemiological Datamentioning

confidence: 99%

Geomatics and epidemiology: Associating oxidative stress and greenness in urban areas

Petris

Squillacioti²,

Bono³

et al. 2021

Environmental Research

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“…Another part of the current geocoding literature deals with geocoding application and issues solving in an explicit geographic area such as in Croatia [11], Turkish [12], Brazil [13], China [6][7][8], Australia [14], South Africa [15], Morocco [16], Cuba [17] and India [18]. Most of these countries are developing ones and have to deal with rapid urban growth which introduces problems such as ambiguous region boundaries and lack of convention in spellings of toponyms.…”

Section: B Related Workmentioning

confidence: 99%

A multi-agent-based approach for address geocoding in poorly mapped areas through public company data

Kebe¹,

Faye²,

Lishou³

2021

IJITAS

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In this study, we present an original method that enhances geocoding systems in poorly mapped areas thanks to public company data and a multi-agent system. In contrast with industrialized countries, many developing countries lack formal postal address systems assignments and usage, making the operation of translating text-based addresses to absolute spatial coordinates, known as geocoding, a big challenge. We recreated a standard of address as it is perceived and used by local people, a kind of non-official national address standard since there is no official one in these areas. Then, we designed a multi-agent system in which agents are assigned different tasks of geocoding process and can perform negotiation to achieve a global objective: find the best possible match or approximation of a location-based on current knowledge. Verification of the usefulness of the proposed approach is made in comparison with Google Geocoding API which shows that the proposed approach has great potential to geocode addresses considering local context semantic issues.

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“…GIS analysis and representations are here presented only for one of the three retrofit scenarios: case 1. In order to integrate energy savings computed with the ANNs with spatial information it has been necessary to run a geocoding script [50]. The address field in the CENED cleaned DB has been corrected according to the format required by the Google Application Programming Interface (API), since the geocoding process has been carried out exploiting the online open geocoding service provided by Google.…”

Section: Gographic Information System Integration For Portfolio Managmentioning

confidence: 99%

Application of artificial neural network and geographic information system to evaluate retrofit potential in public school buildings

Cecconi

Moretti

Tagliabue

2019

Renewable and Sustainable Energy Reviews

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School buildings in Italy are outdated, in critical maintenance conditions and they often perform below acceptable service levels and quality standards. Nevertheless, data supporting renovation policies are missing or very expensive to be obtained. The paper presents a methods for evaluating building's energy savings potential, using the Building Energy Certification (Certificazione Energetica degli Edifici-CENED) open database. The aim of the research concerns the development of a data-driven set of methods, based on the use of open data, machine learning (ML) and Geographic Information Systems (GIS) to support regional energy retrofit policies on school buildings. The main advantage concerns the possibility to predict the post-retrofit energy savings, avoiding the expensive on-site Condition Assessment (CA) phase. Data have been first clustered to identify the most common thermo-physical properties of the envelope, then three retrofit scenarios have been defined to allow the retrofit of homogeneous types of buildings. The energy saving potentials have been evaluated through the implementation of eight Artificial Neural Networks. Ultimately, data have been geolocated and further processed to support the definition of the energy retrofit policies for the most critical regional areas. The Lombardy region has been chosen as case study to test the robustness of the proposed methods. The results of the case study proved that school buildings energy retrofit policies can be defined and compared using available open data, ML and GIS. The future developments of the research concern the further integration of GIS for retrofit cost assessment and scenario analysis.

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A comparison of address geocoding techniques – case study of the city of Zagreb, Croatia

Cited by 12 publications

References 15 publications

Geomatics and epidemiology: Associating oxidative stress and greenness in urban areas

Geomatics and epidemiology: Associating oxidative stress and greenness in urban areas

A multi-agent-based approach for address geocoding in poorly mapped areas through public company data

Application of artificial neural network and geographic information system to evaluate retrofit potential in public school buildings

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