Analisis Keterjangkauan dan Pola Persebaran SMA/MA Negeri di Kabupaten Banyuwangi Menggunakan Analisis Buffering dan Nearest Neighbor pada Aplikasi Q-GIS

Pemerataan fasilitas pendidikan termasuk sekolah harus diperhatikan secara khusus agar mudah dijangkau oleh masyarakat termasuk masyarakat Kabupaten Banyuwangi. Perkembangan teknologi yang begitu pesat mampu mempermudah menganalisis perencanaan pembangunan fasilitas pendidikan menggunakan Sistem Informasi Geografis (SIG) yang digunakan untuk evaluasi pemerataan fasilitas pendidikan sesuai standard perencanaan lingkingan nasional. Metode yang digunakan yaitu metode dekriptif dengan pendekatan kuantitatif. Sampel yang digunakan yaitu SMA/MA Negeri di Kabupaten Banyuwangi. Jenis data yang digunakan yaitu data spasial titik lokasi koordinat sekolah SMA/MA Negeri di Kabupaten Banyuwangi dan lokasi sebaran permukiman. Analisis data yang digunakan yaitu menggunakan analisis Buffering dan analisis Nearest Neighbor pada aplikasi Q-GIS untuk mengetahui keterjangkauan dan pola persebaran lokasi SMA/MA terhadap lokasi. Hasil analisis menunjukan 46,65% wilayah permukiman menjangkau lokasi SMA/MA dan 64,35% wilayah permukiman tidak terjangkau lokasi SMA/MA atau seluas 593,46 km2 dari 1272,15 km2 permukiman yang mampu menjangkau lokasi SMA/MA. Hasil analisis Nearest Neighbor menujukan pola persebaran lokasi SMA/MA Negeri di Banyuwangi tergolong dalam klasifikasi pola persebaran acak dengan skor Nearest Neighbor Index sebesar 0,93 ditinjau dari 21 titik lokasi SMA/MA Negeri. Hal ini mengartikan bahwasannya lokasi SMA/MA Negeri belum terjangkau oleh keseluruhan permukiman masyarakat Banyuwangi dan belum tersebar merata

Correlation between Socio-Economic Characteristics and Housing Quality of Residential Neighbourhoods in Akure, Southwest Nigeria

There is a general paucity of explanations for the emerging social and spatial changes in the pattern and socio-economic traits of urban residential housing units in Nigeria. Hence, this study examined the spatial pattern of residential neighbourhoods; assessed the quality of residential housing units; and evaluated the correlation between the socio-economic position of respondents and housing quality in Akure, Nigeria. Data were derived from the household questionnaire, remotely sensed data (Landsat 8 OLI/TIR, 2021), a Google Earth map, a Street Map of Akure Township, and personal observations. The study adopted a step-wise sampling technique to select 383 samples from 139,069 heads of households in Akure in 2021. Data were analysed using percentage distribution, Pearson Correlation Coefficient, and t-test. Results indicated varying housing qualities across three residential zones in Akure, Nigeria; houses in the low-density residential areas were of better quality than the other residential zones. The quality of houses occupied was influenced by the type of occupation, level of education and average annual income of residents. The study concluded that inadequate housing facilities are fundamental to the observed deteriorating housing qualities in the study area. Therefore, the study suggested improvement of the existing infrastructures and the provision of new ones in the study area. The main contribution of this study is to proffer solutions for a sustainable housing delivery system to facilitate a better quality of life in Nigerian urban centres

Mapping and Classifying Settlement Locations

“Mapping and Classifying Settlement Locations” discusses GRID3’s work on collecting and analyzing settlements data. GRID3’s settlements work has two areas of focus: creating a comprehensive settlement layer that enables a real-world picture of communities, and using building footprints, geospatial data layers, and machine learning algorithms to classify structures and local areas within settlements. The paper also discusses the applications of GRID3’s methods in Nigeria, the Democratic Republic of the Congo, and Zambia. GRID3 works with countries to generate, validate and use geospatial data on population, settlements, infrastructure, and subnational boundaries. For more information, see https://grid3.org/. Keywords: area-level classification; building footprints; comprehensive settlement layer; extent; intra-settlement categorisation; machine learning; polygon layer; point layer; settlement; settlement data; settlement layer; settlement mapping; settlement point; ; GRID3; database schema; geospatial data; neighbourhood classification; open-source; health zones; participatory cartography; GIS; vaccination; immunisation; census; micro-plans; CIESIN; UNFPA; Flowminder; WorldPop; probability model; areal; built-up areas; small settlements; hamlets; hamlet areas; polio; Afric

Classifying Building Usages: A Machine Learning Approach on Building Extractions

This paper considers methods to infer building usage from the geographic and geometric spatial distribution of building extractions. Focusing on Knox County, TN, a Random Forest (RF) and Support Vector Machine (SVM) were used to classify a polygonized building map developed from a Convolutional Neural Network (CNN) based upon remote sensing imagery. The resulting classification metrics of nine building usages are then compared to the RF and SVM building usage classification of Knox County’s LiDAR building footprints and CNN building extractions with removal of false positives. It is shown that the raw CNN building extractions have acceptable building usage classification accuracies. This result is a useful addition to our understanding of building usage because the best remote sensing data (LiDAR building footprints) are not always accessible and completing tedious editing work (CNN building extractions with removal of false positives) is not feasible. Using the methods developed here, the effect of increasing CNN building detection training data for Knox County for testing on Knox County is also investigated. This case study assists in the process of examining if training a model on all Knox County CNN building detections can classify building usages in the similar urban-rural geographic location of Hamilton County, TN. ArcMap and R programming are utilized in gathering the data to conduct the machine learning algorithms while the building usage is defined by CoreLogic Parcel Land - Use codes

Cartographie et classification des localités

Ce document présente le travail de GRID3 sur le recueil et l'analyse des données de localité. Les activités de GRID3 sur les localités se s’articule autour de deux axes principaux : la création d'une couche des localités exhaustive qui permet d'obtenir une image réelle des communautés, et l'utilisation d'empreintes de bâtiments, de couches de données géospatiales et d'algorithmes d'apprentissage automatique pour classer les structures et les zones locales au sein des localités. Ce document aborde également les applications des méthodes de GRID3 au Nigeria, en République démocratique du Congo et en Zambie. GRID3 accompagne les pays dans la création, la validation et l'utilisation des données géospatiales de population, localités, infrastructures et de limites infranationales. Pour plus d'informations, voir https://grid3.org/. Keywords (Mots clés) : classification au niveau de la zone ; empreintes de bâtiments ; couche de localités complète ; étendue ; catégorisation intra localité ; apprentissage automatique ; couche de polygones ; couche de points ; localités ; données de localités ; couche de localités ; cartographie de localités ; point de localités ; GRID3 ; schéma de base de données ; données géospatiales ; classification des quartiers ; source ouverte ; zones de santé ; cartographie participative ; SIG ; vaccination ; immunisation ; recensement ; micro-plans ; CIESIN ; UNFPA ; Flowminder ; WorldPop ; modèle de probabilité ; aréal ; zones bâties ; petites agglomérations ; hameaux ; zones de hameaux ; polio ; Afriqu

Identifying residential neighbourhood types from settlement points in a machine learning approach

Remote sensing techniques are now commonly applied to map and monitor urban land uses to measure growth and to assist with development and planning. Recent work in this area has highlighted the use of textures and other spatial features that can be measured in very high spatial resolution imagery. Far less attention has been given to using geospatial vector data (i.e. points, lines, polygons) to map land uses. This paper presents an approach to distinguish residential settlement types (regular vs. irregular) using an existing database of settlement points locating structures. Nine data features describing the density, distance, angles, and spacing of the settlement points are calculated at multiple spatial scales. These data are analysed alone and with five common remote sensing measures on elevation, slope, vegetation, and nighttime lights in a supervised machine learning approach to classify land use areas. The method was tested in seven provinces of Afghanistan (Balkh, Helmand, Herat, Kabul, Kandahar, Kunduz, Nangarhar). Overall accuracy ranged from 78% in Kandahar to 90% in Nangarhar. This research demonstrates the potential to accurately map land uses from even the simplest representation of structures

AUZOLABS: Recomendaciones Estratégicas desde la Innovación Social para la Regeneración Urbana Integral mediante Barrios-Laboratorio (Urban Living Labs) – NEIGHBOURHOODLABS: Neighbourhood Laboratories for the Urban Integral Regeneration through Strategic Recommendations from the Social Innovation

Calzada, I. (2018), AUZOLABS: Recomendaciones Estratégicas desde la Innovación Social para la Regeneración Urbana Integral mediante Barrios-Laboratorio (Urban Living Labs) – NEIGHBOURHOODLABS: Neighbourhood Laboratories for the Urban Integral Regeneration through Strategic Recommendations from the Social Innovation. EUSKO JAURLARITZA//GOBIERNO VASCO. Ingurumena, Lurralde Plangintza eta Etxebizitza Saila//Departamento de Medio Ambiente, Planificación Territorial y Vivienda. Zumaia: Translokal – Academic Entrepreneurship for Policy Making y University of Oxford. ISBN: 978-84-946385-4-1