Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Participatory GIS× | Spatial Poverty Mapping× | |
|---|---|---|
| Nyanja | Development Studies | Development Studies |
| Familia | Process / pipeline | Process / pipeline |
| Mwaka wa asili≠ | 2006 | 2007 |
| Mwanzilishi≠ | Robert Chambers; Jon Corbett; PGIS practitioner community | World Bank poverty-mapping programme; Bedi, Coudouel & Simler |
| Aina≠ | Participatory spatial data and mapping approach | Spatial-statistical and GIS method for analysing poverty distribution |
| Chanzo asilia≠ | Chambers, R. (2006). Participatory Mapping and Geographic Information Systems: Whose Map? Who is Empowered and Who Disempowered? Who Gains and Who Loses? The Electronic Journal of Information Systems in Developing Countries, 25(1), 1-11. DOI ↗ | Henderson, J. V., Storeygard, A., & Weil, D. N. (2012). Measuring Economic Growth from Outer Space. American Economic Review, 102(2), 994-1028. DOI ↗ |
| Majina mbadala | PGIS, PPGIS, Participatory mapping with GIS, Community mapping | Poverty mapping, Geographic targeting, Poverty maps, Spatial poverty analysis |
| Zinazohusiana | 4 | 4 |
| Muhtasari≠ | Participatory Geographic Information Systems (PGIS), and the related Public Participation GIS (PPGIS), are approaches in which communities themselves create and use spatial data and maps to represent local spatial knowledge for resource management, land and resource tenure, and planning. Spanning a continuum from sketch mapping with sticks and stones on the ground to georeferenced data held in formal GIS, the approach merges the empowering ethos of participatory development, articulated by Robert Chambers, with the analytical and communicative power of geographic information technology. | Spatial poverty mapping visualises and analyses the geographic distribution of poverty using geographic information systems and spatial statistics, turning poverty estimates into maps that reveal where the poor live at fine spatial scales. It combines small-area poverty estimates with spatial covariates — remote-sensing data, night-time lights, accessibility, and terrain — examines spatial patterns and autocorrelation, and supports the geographic targeting of resources. Consolidated through the World Bank programme documented by Bedi, Coudouel, and Simler and energised by data such as the satellite night-lights series analysed by Henderson, Storeygard, and Weil, it has become a standard tool for evidence-based geographic targeting. |
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