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Job Quality in the Gig Economy: How do the workers perceive it? : An exploratory study on the perceived job quality for gig workers in the geographically tethered gig economyHedvall, Oskar, Gustavsson, Oskar January 2022 (has links)
No description available.
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Assessing the Determinants of Maternal Healthcare Service Utilization and Effectiveness of Interventions to Improve Institutional Births in Jimma Zone, EthiopiaKurji, Jaameeta 19 May 2021 (has links)
The strong emphasis placed on improving equality and well-being for all in the Sustainable Development Goals underscores the importance of tackling persistent within-country disparities in maternal mortality and poor health outcomes. Addressing maternal healthcare access barriers is, thus, crucial, particularly in low-resource settings. Numerous studies investigating determinants of maternal healthcare service use in Ethiopia exist but are limited by their focus on individual and household factors, and by methodological weaknesses. A nuanced understanding of the role of socioeconomic and geographic context in influencing access to care is needed to respond effectively.
Maternity waiting homes (MWHs) are a potential strategy to address geographical barriers that delay women’s access to obstetric care. However, in addition to concerns about service quality, there is limited evidence on their effectiveness and on what models meet women’s needs. My research goals were, therefore, to contribute to the understanding of what contextual factors influence maternal healthcare service use in general; and to determine whether or not upgraded MWHs operating in an enabling environment could improve delivery care use in rural Ethiopia. My primary data sources were household surveys conducted as part of a cluster-randomized controlled trial evaluating MWHs and local leader training in Jimma Zone, Ethiopia.
Random effects multivariable logistic regression analysis of survey data brought to light the social and financial resources that facilitate MWH use, highlighting the need for complementary interventions to make access more equitable. Spatial analyses identified subnational variation in service use at a finer scale than routinely reported and unmasked local variation in the relevance and magnitude of associations between individual-, interpersonal-, and health system factors and maternal healthcare use. These findings have implications for relying upon homogenous national responses to improve equality in access to care and health outcomes. Finally, analysis of trial data found a non-significant effect of interventions on delivery care use likely due to implementation issues and extraneous factors. The need to generate strong evidence of effectiveness of MWHs in improving maternal healthcare service use using sustainable and equitable MWH models using methods appropriate for complex intervention evaluation remains.
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THE SPATIAL SPILLOVER IMPACT OF LAND BANK PROPERTIES ON NEARBY HOME SALE VALUES IN CLEVELAND, OHHong, Chansun 17 December 2018 (has links)
No description available.
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用地理加權迴歸分析獨立式與集合式住宅之價格分布-以改制前台中市為例 / The Price Distribution of Detached Houses and Condominiums in Taichung: Geographically Weighted Regression Approach程稚茵, Cheng, Chih Yin Unknown Date (has links)
不動產價格的影響因素可按影響範圍區分為三大類,分別為影響整體不動產市場的「總體環境因素」,對一定範圍內不動產產生價格影響的「區域環境因素」,及對於單一不動產價格有所影響的「房屋個體因素」。其中,區域環境因素為影響個別不動產價格之首要因素,不動產之價格會受到所屬區域之政治、經濟、自然、社會等因素影響,「公共建設因素」為重要之區域環境之一,包含公共設施水準及其配置狀態。影響個別不動產價格之次要因素為「房屋個體因素」,可再次細分為三大影響因素如下:房屋本身所具有的特徵因素,即建築物之內部結構;房屋的建築方式,住宅類型等與全棟房屋有關的因素;與房屋鄰近地區環境有關的因素。而集合式與獨立式住宅因分屬不同房屋類型,即上述房屋價格形成因素中「房屋之建築方式」。實際交易上,獨立式住宅多半以「整棟建物」作為交易計算單位,對於坐落之基地權利持分通常為全部,而集合式住宅係以「樓層」、「戶」作為交易之計算單位,所有之基地持分與其他住戶共同持有,基於上述差異,過去研究多將建築方式視為影響房屋價格的條件之一,並據此分類次市場,因此較少有研究同時探討二者在空間分布上所具有的區位差異,及購屋者對於環境的偏好是否有所不同。且過去文獻多半以使用傳統迴歸模型為主要分析方法。但傳統迴歸分析所使用最小平方法迴歸模型,經常會產生殘差項存在有空間自相關的問題,及空間本身所存在之空間異質性偏誤,即空間不穩定性。因此 本文以台中市都會區內之住家使用房屋為樣本,依特徵價格理論將獨立式住宅與集合式住宅視為差異化商品,其內外特徵納入變數,使用GeoDa軟體進行空間自相關分析,並使用ArcGIS軟體中的地理加權迴歸模組(GWR)進行迴歸分析,藉以探討不同類型房屋所偏好之外部特徵,瞭解不同空間環境對房屋價格之影響及台中市都會區空間發展型態,並驗證其於規劃建設產生的空間不穩定性。
研究結果顯示,台中市建立之重大市政建設及土地開發計畫會影響集合式住宅與獨立式住宅之地價熱點分布,其共同之房價熱點均座落於高地價市地重劃區及重大市政建設分布位置,而獨立式住宅之房價熱點,進一步分布於與高地價市重劃區鄰近之市地重劃區;在購屋者對周圍設施偏好方面,集合式住宅購屋者對於國中小學、大學、重大市政建設、市場、公園均有顯著偏好,惟獨立式住宅購屋者對於大學、重大市政建設、公園有顯著偏好,對於國中小學、市場有不偏好情形,顯示不同類型住宅對於公共設施之偏好不完全相同;集合式住宅與獨立式住宅之房屋特徵屬性呈現空間不穩定性,分析結果顯示,上述二種住宅類型,對於本研究所有公共設施距離特徵屬性均呈現空間不穩定、非均質性的結果,顯示不同類型住宅均會與彼此具有相依性,並形成各區域間的異質性。 / Locational characteristics are the determinants of house prices. While former research have examined the effects of proximity to resources and facilities have on residential property values, and the change of the importance as located regions or submarkets vary, the effects of different types of houses are rarely compared due to their dissimilarity in ways of building and ownership. Do house price effects of the same facility alter when properties are situated in different submarkets? Further, the issues of spatial non-stationarity are usually overlooked by previous studies.
By using transaction data of two common types of residential houses in Taichung City, we found house price hot spots of both detached houses and condos in regions with major constructions and development plans. Apart from the mutual hot spots found in high land price redevelopment zones, we also discovery hot spots of detached houses in areas in proximity to these redevelopment zones. As for desirable facilities for home buyers, neighborhood schools, universities, major constructions, local markets and parks were found to have an notable price impact on condos, whereas only universities, major constructions and parks in vicinity of in detached houses can we found significant price effects, suggesting the differences in the preference of consumers in distinct regions. Also, spatial dependence and heterogeneity are verified in both types of houses, making the entire market area spatial non-stationary.
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Konkurenceschopnost veřejné hromadné dopravy na příkladu Pardubického kraje / The competitiveness of public transport on example of the Pardubice RegionHrbek, Martin January 2016 (has links)
The competitiveness of public transport on example of the Pardubice Region Abstract This diploma thesis is devoted to the competitiveness of public transport in the municipalities of the Pardubice Region. Competitiveness is understood mainly in terms of the price difference between travel time and cost of public and individual car transport, and also in terms of the real demand in the municipalities, thus the share of commuting by public transport. Other parameters of mode choice, that is understood as the main indicator of competitiveness, is the number of public transport lines and automobilization. The main objective of this work is to determine how public transportation depends on the other transport characteristics of municipalities. To select significant variables, multiple linear regression analysis was used. After that, geographically weighted regression was applied in order to explain the share of commuting from municipalities. Most data originate in public databases (The Register of vehicles of Department of Transport, population census, digital geographic databases ArcČR and CEDA) and web portals (OREDO, IDOS), part of the data was obtained within questionnaire survey in selected municipalities. An expected negative relationship between the degree of automobilization and the number of public...
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Spatial crash prediction models: an evaluation of the impacts of enriched information on model performance and the suitability of different spatial modeling approaches / Modelos espaciais de previsão de acidentes: uma avaliação do desempenho dos modelos a partir da incorporação de informações aprimoradas e a adequação de diferentes abordagens de modelagem espacialGomes, Monique Martins 04 December 2018 (has links)
The unavailability of crash-related data has been a long lasting challenge in Brazil. In addition to the poor implementation and follow-up of road safety strategies, this drawback has hampered the development of studies that could contribute to national goals toward road safety. In contrast, developed countries have built their effective strategies on solid data basis, therefore, investing a considerable time and money in obtaining and creating pertinent information. In this research, we aim to assess the potential impacts of supplementary data on spatial model performance and the suitability of different spatial modeling approaches on crash prediction. The intention is to notify the authorities in Brazil and other developing countries, about the importance of having appropriate data. In this thesis, we set two specific objectives: (I) to investigate the spatial model prediction accuracy at unsampled subzones; (II) to evaluate the performance of spatial data analysis approaches on crash prediction. Firstly, we carry out a benchmarking based on Geographically Weighted Regression (GWR) models developed for Flanders, Belgium, and São Paulo, Brazil. Models are developed for two modes of transport: active (i.e. pedestrians and cyclists) and motorized transport (i.e. motorized vehicles occupants). Subsequently, we apply the repeated holdout method on the Flemish models, introducing two GWR validation approaches, named GWR holdout1 and GWR holdout2. While the former is based on the local coefficient estimates derived from the neighboring subzones and measures of the explanatory variables for the validation subzones, the latter uses the casualty estimates of the neighboring subzones directly to estimate outcomes for the missing subzones. Lastly, we compare the performance of GWR models with Mean Imputation (MEI), K-Nearest Neighbor (KNN) and Kriging with External Drift (KED). Findings showed that by adding the supplementary data, reductions of 20% and 25% for motorized transport, and 25% and 35% for active transport resulted in corrected Akaike Information Criterion (AICc) and Mean Squared Prediction Errors (MSPE), respectively. From a practical perspective, the results could help us identify hotspots and prioritize data collection strategies besides identify, implement and enforce appropriate countermeasures. Concerning the spatial approaches, GWR holdout2 out performed all other techniques and proved that GWR is an appropriate spatial technique for both prediction and impact analyses. Especially in countries where data availability has been an issue, this validation framework allows casualties or crash frequencies to be estimated while effectively capturing the spatial variation of the data. / A indisponibilidade de variáveis explicativas de acidentes de trânsito tem sido um desafio duradouro no Brasil. Além da má implementação e acompanhamento de estratégias de segurança viária, esse inconveniente tem dificultado o desenvolvimento de estudos que poderiam contribuir com as metas nacionais de segurança no trânsito. Em contraste, países desenvolvidos tem construído suas estratégias efetivas com base em dados sólidos, e portanto, investindo tempo e dinheiro consideráveis na obtenção e criação de informações pertinentes. O objetivo dessa pesquisa é avaliar os possíveis impactos de dados suplementares sobre o desempenho de modelos espaciais, e a adequação de diferentes abordagens de modelagem espacial na previsão de acidentes. A intenção é notificar as autoridades brasileiras e de outros países em desenvolvimento sobre a importância de dados adequados. Nesta tese, foram definidos dois objetivos específicos: (I) investigar a acurácia do modelo espacial em subzonas sem amostragem; (II) avaliar o desempenho de técnicas de análise espacial de dados na previsão de acidentes. Primeiramente, foi realizado um estudo comparativo, baseado em modelos desenvolvidos para Flandres (Bélgica) e São Paulo (Brasil), através do método de Regressão Geograficamente Ponderada (RGP). Os modelos foram desenvolvidos para dois modos de transporte: ativos (pedestres e ciclistas) e motorizados (ocupantes de veículos motorizados). Subsequentemente, foi aplicado o método de holdout repetido nos modelos Flamengos, introduzindo duas abordagens de validação para GWR, denominados RGP holdout1 e RGP holdout2. Enquanto o primeiro é baseado nas estimativas de coeficientes locais derivados das subzonas vizinhas e medidas das variáveis explicativas para as subzonas de validação, o último usa as estimativas de acidentes das subzonas vizinhas, diretamente, para estimar os resultados para as subzonas ausentes. Por fim, foi comparado o desempenho de modelos RGP e outras abordagens, tais como Imputação pela Média de dados faltantes (IM), K-vizinhos mais próximos (KNN) e Krigagem com Deriva Externa (KDE). Os resultados mostraram que, adicionando os dados suplementares, reduções de 20% e 25% para o transporte motorizado, e 25% e 35% para o transporte ativo, foram resultantes em termos de Critério de Informação de Akaike corrigido (AICc) e Erro Quadrático Médio da Predição (EQMP), respectivamente. Do ponto de vista prático, os resultados poderiam ajudar a identificar hotspots e priorizar estratégias de coleta de dados, além de identificar, implementar e aplicar contramedidas adequadas. No que diz respeito às abordagens espaciais, RGP holdout2 teve melhor desempenho em relação a todas as outras técnicas e, provou que a RGP é uma técnica espacial apropriada para ambas as análises de previsão e impactos. Especialmente em países onde a disponibilidade de dados tem sido um problema, essa estrutura de validação permite que as acidentes sejam estimados enquanto, capturando efetivamente a variação espacial dos dados.
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Spatial crash prediction models: an evaluation of the impacts of enriched information on model performance and the suitability of different spatial modeling approaches / Modelos espaciais de previsão de acidentes: uma avaliação do desempenho dos modelos a partir da incorporação de informações aprimoradas e a adequação de diferentes abordagens de modelagem espacialMonique Martins Gomes 04 December 2018 (has links)
The unavailability of crash-related data has been a long lasting challenge in Brazil. In addition to the poor implementation and follow-up of road safety strategies, this drawback has hampered the development of studies that could contribute to national goals toward road safety. In contrast, developed countries have built their effective strategies on solid data basis, therefore, investing a considerable time and money in obtaining and creating pertinent information. In this research, we aim to assess the potential impacts of supplementary data on spatial model performance and the suitability of different spatial modeling approaches on crash prediction. The intention is to notify the authorities in Brazil and other developing countries, about the importance of having appropriate data. In this thesis, we set two specific objectives: (I) to investigate the spatial model prediction accuracy at unsampled subzones; (II) to evaluate the performance of spatial data analysis approaches on crash prediction. Firstly, we carry out a benchmarking based on Geographically Weighted Regression (GWR) models developed for Flanders, Belgium, and São Paulo, Brazil. Models are developed for two modes of transport: active (i.e. pedestrians and cyclists) and motorized transport (i.e. motorized vehicles occupants). Subsequently, we apply the repeated holdout method on the Flemish models, introducing two GWR validation approaches, named GWR holdout1 and GWR holdout2. While the former is based on the local coefficient estimates derived from the neighboring subzones and measures of the explanatory variables for the validation subzones, the latter uses the casualty estimates of the neighboring subzones directly to estimate outcomes for the missing subzones. Lastly, we compare the performance of GWR models with Mean Imputation (MEI), K-Nearest Neighbor (KNN) and Kriging with External Drift (KED). Findings showed that by adding the supplementary data, reductions of 20% and 25% for motorized transport, and 25% and 35% for active transport resulted in corrected Akaike Information Criterion (AICc) and Mean Squared Prediction Errors (MSPE), respectively. From a practical perspective, the results could help us identify hotspots and prioritize data collection strategies besides identify, implement and enforce appropriate countermeasures. Concerning the spatial approaches, GWR holdout2 out performed all other techniques and proved that GWR is an appropriate spatial technique for both prediction and impact analyses. Especially in countries where data availability has been an issue, this validation framework allows casualties or crash frequencies to be estimated while effectively capturing the spatial variation of the data. / A indisponibilidade de variáveis explicativas de acidentes de trânsito tem sido um desafio duradouro no Brasil. Além da má implementação e acompanhamento de estratégias de segurança viária, esse inconveniente tem dificultado o desenvolvimento de estudos que poderiam contribuir com as metas nacionais de segurança no trânsito. Em contraste, países desenvolvidos tem construído suas estratégias efetivas com base em dados sólidos, e portanto, investindo tempo e dinheiro consideráveis na obtenção e criação de informações pertinentes. O objetivo dessa pesquisa é avaliar os possíveis impactos de dados suplementares sobre o desempenho de modelos espaciais, e a adequação de diferentes abordagens de modelagem espacial na previsão de acidentes. A intenção é notificar as autoridades brasileiras e de outros países em desenvolvimento sobre a importância de dados adequados. Nesta tese, foram definidos dois objetivos específicos: (I) investigar a acurácia do modelo espacial em subzonas sem amostragem; (II) avaliar o desempenho de técnicas de análise espacial de dados na previsão de acidentes. Primeiramente, foi realizado um estudo comparativo, baseado em modelos desenvolvidos para Flandres (Bélgica) e São Paulo (Brasil), através do método de Regressão Geograficamente Ponderada (RGP). Os modelos foram desenvolvidos para dois modos de transporte: ativos (pedestres e ciclistas) e motorizados (ocupantes de veículos motorizados). Subsequentemente, foi aplicado o método de holdout repetido nos modelos Flamengos, introduzindo duas abordagens de validação para GWR, denominados RGP holdout1 e RGP holdout2. Enquanto o primeiro é baseado nas estimativas de coeficientes locais derivados das subzonas vizinhas e medidas das variáveis explicativas para as subzonas de validação, o último usa as estimativas de acidentes das subzonas vizinhas, diretamente, para estimar os resultados para as subzonas ausentes. Por fim, foi comparado o desempenho de modelos RGP e outras abordagens, tais como Imputação pela Média de dados faltantes (IM), K-vizinhos mais próximos (KNN) e Krigagem com Deriva Externa (KDE). Os resultados mostraram que, adicionando os dados suplementares, reduções de 20% e 25% para o transporte motorizado, e 25% e 35% para o transporte ativo, foram resultantes em termos de Critério de Informação de Akaike corrigido (AICc) e Erro Quadrático Médio da Predição (EQMP), respectivamente. Do ponto de vista prático, os resultados poderiam ajudar a identificar hotspots e priorizar estratégias de coleta de dados, além de identificar, implementar e aplicar contramedidas adequadas. No que diz respeito às abordagens espaciais, RGP holdout2 teve melhor desempenho em relação a todas as outras técnicas e, provou que a RGP é uma técnica espacial apropriada para ambas as análises de previsão e impactos. Especialmente em países onde a disponibilidade de dados tem sido um problema, essa estrutura de validação permite que as acidentes sejam estimados enquanto, capturando efetivamente a variação espacial dos dados.
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Accelerating Global Product Innovation through Cross-cultural Collaboration : Organizational Mechanisms that Influence Knowledge-sharing within the MNCJensen, Karina 04 June 2012 (has links) (PDF)
Globalization, time to market, and customer responsiveness present continuous challenges for achieving market innovation across cultures. A cross-cultural and networked business environment has created increased demand for knowledge-sharing within the multinational corporation (MNC). The inability of geographically distributed team members to effectively share and communicate ideas and solutions can result in a lack of product innovation, delayed product introductions, and reduced sales and market opportunities. This requires managers to leverage cross-cultural team knowledge in order to improve the design and delivery of innovative customer solutions worldwide. This dissertation thus intends to examine and identify organizational mechanisms that facilitate cross-cultural collaboration and knowledge-sharing for geographically distributed teams responsible for the front end of innovation.The resource-based and knowledge-based views of the firm inform this dissertation where integrated cognitive and social practices serve an important role for innovation. Through qualitative research, I will examine organizational mechanisms that influence interactions between the project leader and the geographically distributed team during global product launches, from product concept to market introduction. Since there is a lack of empirical research conducted with organizations on cross-cultural collaboration and global innovation, there is a significant opportunity to advance research within innovation management while assisting organizations in the development of knowledge-sharing capabilities that serve as competitive advantage in conceiving and introducing new products to international markets.The purpose of this dissertation research is to investigate and demonstrate how MNCs can facilitate the cross-cultural collaboration process in order to effectively conceive and execute innovation strategies for new products. The research intends to develop a framework and model for cross-cultural team collaboration in exploring and responding to the following research question: How can MNCs optimize cross-cultural team collaboration in order to strengthen the planning and execution of global innovation strategies? This research responds to organizational needs for sharing knowledge amongst cross-cultural teams in order to accelerate responsiveness to international market opportunities.
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Previsão espaço-temporal de demanda incluindo alterações nos hábitos de consumidores residenciais /Mejia Alzate, Mario Andres January 2016 (has links)
Orientador: Antonio Padilha Feltrin / Resumo: Neste trabalho é apresentado um método que permite determinar o crescimento espaço-temporal da demanda de energia elétrica devido às mudanças nos hábitos de consumo no setor residencial. A proposta é baseada em uma regressão ponderada geograficamente que permite determinar a localização espacial dos setores com maior proporção de residências candidatas para comprar um novo eletrodoméstico, e uma regressão de distribuição logística que permite simular em cada setor, como vai ser o crescimento ao longo do tempo dessa proporção de residências candidatas para comprar o aparelho. Finalmente, o método determina o impacto nas curvas de carga dos transformadores de distribuição, considerando: o número de residências candidatas em cada setor, e informações do eletrodoméstico em estudo, tais como: curva de carga em p.u, potência nominal, fator de utilização, fator de coincidência e fator de potência. A região em estudo é dividida em pequenas subáreas, com o objetivo de melhorar a resolução espacial do prognóstico, e também considerar interrelações de proximidade entre as subáreas, para determinar como as decisões tomadas em um local influenciam nas preferências de seus vizinhos. O método proposto usa como dados de entrada variáveis socioeconômicas do censo da população que são de fácil acesso para as empresas do setor elétrico e que caracterizam a economia e as preferências da população da cidade em estudo. O método proposto foi aplicado em uma cidade de médio porte da República do Equ... (Resumo completo, clicar acesso eletrônico abaixo) / Mestre
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Previsão espaço-temporal de demanda incluindo alterações nos hábitos de consumidores residenciais / Previsión espacio-temporal de demanda incluyendo alteraciones en los hábitos de consumidores residencialesMejia Alzate, Mario Andres [UNESP] 19 December 2016 (has links)
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Previous issue date: 2016-12-19 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / Neste trabalho é apresentado um método que permite determinar o crescimento espaço-temporal da demanda de energia elétrica devido às mudanças nos hábitos de consumo no setor residencial. A proposta é baseada em uma regressão ponderada geograficamente que permite determinar a localização espacial dos setores com maior proporção de residências candidatas para comprar um novo eletrodoméstico, e uma regressão de distribuição logística que permite simular em cada setor, como vai ser o crescimento ao longo do tempo dessa proporção de residências candidatas para comprar o aparelho. Finalmente, o método determina o impacto nas curvas de carga dos transformadores de distribuição, considerando: o número de residências candidatas em cada setor, e informações do eletrodoméstico em estudo, tais como: curva de carga em p.u, potência nominal, fator de utilização, fator de coincidência e fator de potência. A região em estudo é dividida em pequenas subáreas, com o objetivo de melhorar a resolução espacial do prognóstico, e também considerar interrelações de proximidade entre as subáreas, para determinar como as decisões tomadas em um local influenciam nas preferências de seus vizinhos. O método proposto usa como dados de entrada variáveis socioeconômicas do censo da população que são de fácil acesso para as empresas do setor elétrico e que caracterizam a economia e as preferências da população da cidade em estudo. O método proposto foi aplicado em uma cidade de médio porte da República do Equador a fim de determinar o crescimento espaço-temporal da demanda de energia devido à compra de fogões de indução. Os resultados obtidos são mapas que permitem identificar os setores mais vulneráveis para apresentar crescimento da demanda devido à compra do eletrodoméstico. Também são apresentados gráficos que mostram o impacto nas curvas de carga dos transformadores durante o período de estudo estabelecido. Esses resultados fornecem informações importantes que servem de referência no planejamento do sistema de distribuição e do mercado de energia elétrica. / This work presents a method to determine the spatial-temporal growth of electric energy demand due to changes in consumption habits in the residential sector. The proposal is based on a geographically weighted regression that allows us to determine the spatial location of the sectors with the highest proportion of candidate households to buy a new appliance, and a logistic distribution regression that allows us to simulate in each of these sectors, the growth over time, the proportion of households that are candidates to buy this appliance. Finally, the method determines the impact on the load curves of the distribution transformers, considering: the number of candidate households in each sector, and information of the home appliance, such as: load curve in pu, nominal power, utilization factor, Coincidence factor and power factor. The study area is divided into small subareas with the aim of improving the spatial resolution of the prognosis and also considers the interrelation of proximity between the subareas to determine how decisions made in one place can influence the preferences of its neighbors. The input data of the proposed method are socioeconomic variables of the population census, which are easily accessible to companies in the electricity sector, and which characterize the economy and the preferences of the population of the studied city. The method was applied in a medium-sized city of the Republic of Ecuador in order to determine the spatial-temporal growth of energy demand due to the purchase of induction stoves. The results obtained are maps that allow identifying the most vulnerable sectors to show increased demand due to the purchase of the appliance. Also, graphs were obtained that show the impact on the load curves of the transformers during the established study period. These results provide important information that serve as a reference in planning the distribution system and the electricity market.
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