Mineralized urbanization in Africa in the twenty-first century: Becoming urban through mining extraction

This article focuses on the urbanizing impact of the post-millennial mineral boom at artisanal and small-scale (ASM) or large-scale (LSM) mining sites in three mineral-rich countries, involving gold in Ghana, diamonds in Angola, and both minerals in Tanzania. The focus is on comparing the agency of miners and other residents migrating to, settling in, and making the mining site habitable. Their mobility and settlement patterns reveal an urbanization trend marked by population agglomeration and expanding labour complexity, taking distinct forms at the rush and mature stages of gold and diamond ASM and LSM sites. Citing data from household surveys conducted at 12 mining sites, we trace how ‘mineralized urbanization’ propels in-migration, rising localized purchasing power, and proliferating service sector and trade activities, fuelling both urban demographic and economic change along the mining extraction trajectory. LSM and ASM generate synergies as well as detractive forces, depending on the size, age and history of the mining settlement development. What emerges is the differential development of households and settlements through strategic economic manoeuvring and the rough and tumble of happenstance, underlined by a compelling, albeit fluctuating, trajectory of non-renewable mineralized urbanization

Study flow diagram for patients seeking care at Kilimanjaro Christian Medical Centre and Mawenzi Regional referral hospital in Moshi, Tanzania, 2012–14.

<p>Study flow diagram for patients seeking care at Kilimanjaro Christian Medical Centre and Mawenzi Regional referral hospital in Moshi, Tanzania, 2012–14.</p

Bivariable and multivariable logistic regression models of association between exposure scales and acute leptospirosis among patients with febrile illness in northern Tanzania, 2012–14.

<p>Bivariable and multivariable logistic regression models of association between exposure scales and acute leptospirosis among patients with febrile illness in northern Tanzania, 2012–14.</p

Participant scores of exposure to animal urine and surface water, northern Tanzania, 2012–14 (N = 844).

<p>Participant scores of exposure to animal urine and surface water, northern Tanzania, 2012–14 (N = 844).</p

Multivariable logistic regression of individual risk factors <i>Leptospira</i> seropositivity among patients with febrile illness in northern Tanzania, 2012–14.

<p>Multivariable logistic regression of individual risk factors <i>Leptospira</i> seropositivity among patients with febrile illness in northern Tanzania, 2012–14.</p

Bivaraite logistic regression of temporal and geo-referenced risk factors for <i>Leptospira</i> seropositivity among patients with febrile illness, in northern Tanzania, 2012–14.

<p>Bivaraite logistic regression of temporal and geo-referenced risk factors for <i>Leptospira</i> seropositivity among patients with febrile illness, in northern Tanzania, 2012–14.</p

Predominantly reactive serogroup of leptospirosis cases and participants seropositive to <i>Leptospira</i>, northern Tanzania, 2012–14.

<p>Predominantly reactive serogroup of leptospirosis cases and participants seropositive to <i>Leptospira</i>, northern Tanzania, 2012–14.</p

Bivariable logistic regression of temporal and geo-referenced risk factors for acute leptospirosis among patients with febrile illness, northern Tanzania, 2012–14.

<p>Bivariable logistic regression of temporal and geo-referenced risk factors for acute leptospirosis among patients with febrile illness, northern Tanzania, 2012–14.</p

Demographic and clinical characteristics of study participants, northern Tanzania, 2012–14.

<p>Demographic and clinical characteristics of study participants, northern Tanzania, 2012–14.</p

Component risk factors and relative weights for exposure to multiple leptospirosis infection sources derived from an analytic hierarchy process conducted among East African subject matter experts, 2015.

<p>Component risk factors and relative weights for exposure to multiple leptospirosis infection sources derived from an analytic hierarchy process conducted among East African subject matter experts, 2015.</p