Drawing on established scholarship in the historical geography of science, the history of technology and science and technology studies, this paper argues for the significance of an historical geography of engineering. Large-scale and transformative infrastructure projects have been a common focus in historical geography, however comparatively little attention has been paid to the engineers responsible for designing and implementing them. This paper reviews recent work which has foregrounded engineers and their work across diverse times and places. It conceptualises engineering in three ways: as a form of knowledge about the world that is connected to, but distinct from, science; as a set of practices undertaken in specific locations; and as an identity that, since the profession's origin in the 18th century, has enabled individuals to claim expertise in relation to environmental management and therefore exert power over land, territory and people. The article reviews geographical inquiry that foregrounds these perspectives on engineering and suggests future directions for research in the field.
{"title":"Historical Geographies of Engineering: Knowledges, Practices, Identities","authors":"Rachel Dishington","doi":"10.1111/gec3.70011","DOIUrl":"https://doi.org/10.1111/gec3.70011","url":null,"abstract":"<p>Drawing on established scholarship in the historical geography of science, the history of technology and science and technology studies, this paper argues for the significance of an historical geography of engineering. Large-scale and transformative infrastructure projects have been a common focus in historical geography, however comparatively little attention has been paid to the engineers responsible for designing and implementing them. This paper reviews recent work which has foregrounded engineers and their work across diverse times and places. It conceptualises engineering in three ways: as a form of knowledge about the world that is connected to, but distinct from, science; as a set of practices undertaken in specific locations; and as an identity that, since the profession's origin in the 18th century, has enabled individuals to claim expertise in relation to environmental management and therefore exert power over land, territory and people. The article reviews geographical inquiry that foregrounds these perspectives on engineering and suggests future directions for research in the field.</p>","PeriodicalId":51411,"journal":{"name":"Geography Compass","volume":"18 11","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gec3.70011","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The use of data and statistics along with computational systems heralded the beginning of a quantitative revolution in Geography. Use of simulation models (Cellular Automata and Agent-Based Models) followed in the late 1990s, with ontology and epistemology of complexity theory and modelling being defined a little less than two decades ago. We are, however, entering a new era where sensors regularly collect and update large amounts of spatio-temporal data. We define this ‘Big Data’ as geolocated data collected in sufficiently high volume (exceeding storage capacities of the largest personal hard drives currently available), that is updated at least daily, from a variety of sources in different formats, often without recourse to verification of its accuracy. We then identify the exponential growth in the use of complexity simulation models in the past two decades via an extensive literature review (broken down by application area), but also notice a recent slowdown. Further, a gap in the utilisation of Big Data by modellers to calibrate and validate their models is noted, which we attribute to data availability issues. We contend that Big Data can significantly boost simulation modelling, if certain constraints and issues are managed properly.
{"title":"Big Data (R)evolution in Geography: Complexity Modelling in the Last Two Decades","authors":"Liliana Perez, Raja Sengupta","doi":"10.1111/gec3.70009","DOIUrl":"https://doi.org/10.1111/gec3.70009","url":null,"abstract":"<p>The use of data and statistics along with computational systems heralded the beginning of a quantitative revolution in Geography. Use of simulation models (Cellular Automata and Agent-Based Models) followed in the late 1990s, with ontology and epistemology of complexity theory and modelling being defined a little less than two decades ago. We are, however, entering a new era where sensors regularly collect and update large amounts of spatio-temporal data. We define this ‘Big Data’ as geolocated data collected in sufficiently high volume (exceeding storage capacities of the largest personal hard drives currently available), that is updated at least daily, from a variety of sources in different formats, often without recourse to verification of its accuracy. We then identify the exponential growth in the use of complexity simulation models in the past two decades via an extensive literature review (broken down by application area), but also notice a recent slowdown. Further, a gap in the utilisation of Big Data by modellers to calibrate and validate their models is noted, which we attribute to data availability issues. We contend that Big Data can significantly boost simulation modelling, if certain constraints and issues are managed properly.</p>","PeriodicalId":51411,"journal":{"name":"Geography Compass","volume":"18 11","pages":""},"PeriodicalIF":3.1,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gec3.70009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142664512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}