Fast optimization for large scale in complex urban systems using the hybrid sparrow search algo

Title：Fast optimization for large scale logistics in complex urban systems using the hybrid sparrow search algorithmAbstractUrban logistics is vital t

Estimating China’s poverty reduction efficiency by integrating multi-source data and deep learning

Title：Estimating China’s poverty reduction efficiency by integrating multi-source geospatial data and deep learning techniquesAbstractPoverty threaten

Extracting human perceptions from street view images for better assessing urban renewal potential

Title：Extracting human perceptions from street view images for better assessing urban renewal potentialHighlightsHuman visual and emotional perception

交通驱动下微观地块尺度的城市土地利用变化模拟 —— 以深圳市为例

交通驱动下微观地块尺度的城市土地利用变化模拟 —— 以深圳市为例摘要城市交通作为土地利用空间格局变化的重要驱动因素，在城市发展模拟研究中值得重视。如何有效挖掘城市交通因素并引入地块尺度城市土地利用模拟成为重要议题。本文提出一套基于矢量元胞自动机考虑交通因素的城市土地利用变化模拟框架（T-VCA）。该

How do urban services facilities affect social segregation among people of diff economic levels

Title：How do urban services facilities affect social segregation among people of different economic levels? A case study of Shenzhen cityAbstractSocia

The inequalities of different dimensions of visible street urban green space provision

Title：The inequalities of different dimensions of visible street urban green space provision: A machine learning approachHighlightsA machine-learning

Understanding China’s urban functional patterns at the county scale by time-series social media data

Title：Understanding China’s urban functional patterns at the county scale by using time-series social media dataAbstractUnderstanding urban functions

Estimating the spatial variation of electricity consumpution

Title：Estimating the spatial variation of electricity consumputionAbstractEffective detection of abnormal electricity users and analysis of the spatia

Assessing myocardial infarction severity from the urban environment perspective in Wuhan, China

Title：Assessing myocardial infarction severity from the urban environment perspective in Wuhan, ChinaHighlightsRFA-SHAP far outperforms other models f

Estimating urban functional distributions with semantics preserved POI embedding

We present a novel approach for estimating the proportional distributions of function types (i.e. functional distributions) in an urban area through learning semantics preserved embeddings of points-of-interest (POIs). Specifically, we represent POIs as low-dimensional vectors to capture (1) the spatial co-occurrence patterns of POIs and (2) the semantics conveyed by the POI hierarchical categories (i.e. categorical semantics). The proposed approach utilizes spatially explicit random walks in a POI network to learn spatial co-occurrence patterns, and a manifold learning algorithm to capture categorical semantics. The learned POI vector embeddings are then aggregated to generate regional embeddings with long short-term memory (LSTM) and attention mechanisms, to take account of the different levels of importance among the POIs in a region. Finally, a multilayer perceptron (MLP) maps regional embeddings to functional distributions. A case study in Xiamen Island, China implements and evaluates the proposed approach. The results indicate that our approach outperforms several competitive baseline models in all evaluation measures, and yields a relatively high consistency between the estimation and ground truth. In addition, a comprehensive error analysis unveils several intrinsic limitations of POI data for this task, e.g. ambiguous linkage between POIs and functions.