Predicting Multi-level Socioeconomic Indicators from Structural Urban Imagery

Funding Information: This research has been supported in part by the National Key Research and Development Program of China under Grant 2020YFB2104005; in part by the National Natural Science Foundation of China under Grant U20B2060, and Grant U21B2036; in part by the International Postdoctoral Exchange Fellowship Program (Talent-Introduction Program) under YJ20210274; and in part by the Academy of Finland under Project 319669, Project 319670, Project 325570, Project 326305, Project 325774, and Project 335934. Publisher Copyright: © 2022 Owner/Author.Understanding economic development and designing government policies requires accurate and timely measurements of socioeconomic activities. In this paper, we show how to leverage city structural information and urban imagery like satellite images and street view images to accurately predict multi-level socioeconomic indicators. Our framework consists of four steps. First, we extract structural information from cities by transforming real-world street networks into city graphs (GeoStruct). Second, we design a contrastive learning-based model to refine urban image features by looking at geographic similarity between images, with images that are geographically close together having similar features (GeoCLR). Third, we propose using street segments as containers to adaptively fuse the features of multi-view urban images, including satellite images and street view images (GeoFuse). Finally, given the city graph with a street segment as a node and a neighborhood area as a subgraph, we jointly model street- and neighborhood-level socioeconomic indicator predictions as node and subgraph classification tasks. The novelty of our method is that we introduce city structure to organize multi-view urban images and model the relationships between socioeconomic indicators at different levels. We evaluate our framework on the basis of real-world datasets collected in multiple cities. Our proposed framework improves performance by over 10% when compared to state-of-the-art baselines in terms of prediction accuracy and recall.Peer reviewe

I Seed Permanent Implantation as a Palliative Treatment for Stage III and IV Hypopharyngeal Carcinoma

Objectives. The aim of this study was to investigate the feasibility and safety of percutaneous 125I seed permanent implantation for advanced hypopharyngeal carcinoma from toxicity, tumor response, and short-term outcome. Methods. 125I seeds implant procedures were performed under computed tomography for 34 patients with advanced hypopharyngeal carcinoma. We observed the local control rate, overall survival, and acute or late toxicity rate. Results. In the 34 patients (stage III, n=6; stage IV, n=28), the sites of origin were pyriform sinus (n=29) and postcricoid area (n=5). All patients also received one to four cycles of chemotherapy after seed implantation. The post-plan showed that the actuarial D90 of 125I seeds ranged from 90 to 158 Gy (median, 127 Gy). The mean follow-up was 12.3 months (range, 3.4 to 43.2 months). The local control was 2.1–31.0 months with a median of 17.7 months (95% confidence interval [CI], 13.4 to 22.0 months). The 1-, 2-, and 3-year local controls were 65.3%, 28.6%, and 9.5% respectively. Twelve patients (35%) died of local recurrence, fourteen patients (41%) died of distant metastases, and three patients (9%) died of recurrence and metastases at the same time. Five patients (15%) still survived to follow-up. At the time of analysis, the median survival time was 12.5 months (95% CI, 9.5 to 15.4 months). The 1-, 2-, and 3-year overall survival rates were 55.2%, 20.3%, and 10.9%, respectively. Five patients (15%) experienced grade 3 toxic events and nine patients (26%) have experienced grade 2 toxic events. Conclusion. This review shows relatively low toxicity for interstitial 125I seed implantation in the patients with advanced stage hypopharyngeal cancer. The high local control results suggest that 125I seed brachytherapy implant as a salvage or palliative treatment for advanced hypopharyngeal carcinoma merit further investigation

Pen Culture Detection Using Filter Tensor Analysis with Multi-Temporal Landsat Imagery

Aquaculture plays an important role in China’s total fisheries production nowadays, and it leads to a few problems, for example water quality degradation, which has damaging effect on the sustainable development of environment. Among the many forms of aquaculture that deteriorate the water quality, disorderly pen culture is especially severe. Pen culture began very early in Yangchenghu Lake and Taihu Lake in China and part of the pen culture still exists. Thus, it is of great significance to evaluate the distribution and area of the pen culture in the two lakes. However, the traditional method for pen culture detection is based on the factual measurement, which is labor and time consuming. At present, with the development of remote sensing technologies, some target detection algorithms for multi/hyper-spectral data have been used in the pen culture detection, but most of them are intended for the single-temporal remote sensing data. Recently, a target detection algorithm called filter tensor analysis (FTA), which is specially designed for multi-temporal remote sensing data, has been reported and has achieved better detection results compared to the traditional single-temporal methods in many cases. This paper mainly aims to investigate the pen culture in Yangchenghu Lake and Taihu Lake with FTA implemented on the multi-temporal Landsat imagery, by determining the optimal time phases combination of the Landsat data in advance. Furthermore, the suitability and superiority of FTA over Constrained Energy Minimization (CEM) in the process of pen culture detection were tested. It was observed in the experiments on the data of those two lakes that FTA can detect the pen culture much more accurately than CEM with Landsat data of selected bands and of limited number of time phases

Healthy Cities, A comprehensive dataset for environmental determinants of health in England cities.

Acknowledgements: This work was supported in part by the National Key Research and Development Program of China under grant 2020AAA0106000 and the National Natural Science Foundation of China under U1936217. In this work, we use data from the UK government, Office for National Statistics, Department for Environment, Food & Rural Affairs, NHS Business Services Authority, the Met Office, and the Office for Health Improvement and Disparities licensed under the Open Government Licence v.3.0, and the produced dataset contains OS data © Crown copyright and database right [2022]. We use OpenStreetMap data under the Open Data Commons Open Database License 1.0. The satellite image data is collected from Esri under the Esri Master License Agreement. We acknowledge these publicly available data sources for promoting this study.This paper presents a fine-grained and multi-sourced dataset for environmental determinants of health collected from England cities. We provide health outcomes of citizens covering physical health (COVID-19 cases, asthma medication expenditure, etc.), mental health (psychological medication expenditure), and life expectancy estimations. We present the corresponding environmental determinants from four perspectives, including basic statistics (population, area, etc.), behavioural environment (availability of tobacco, health-care services, etc.), built environment (road density, street view features, etc.), and natural environment (air quality, temperature, etc.). To reveal regional differences, we extract and integrate massive environment and health indicators from heterogeneous sources into two unified spatial scales, i.e., at the middle layer super output area (MSOA) and the city level, via big data processing and deep learning. Our data holds great promise for diverse audiences, such as public health researchers and urban designers, to further unveil the environmental determinants of health and design methodology for a healthy, sustainable city

Healthy Cities, A comprehensive dataset for environmental determinants of health in England cities

Publisher Copyright: © 2023, The Author(s).This paper presents a fine-grained and multi-sourced dataset for environmental determinants of health collected from England cities. We provide health outcomes of citizens covering physical health (COVID-19 cases, asthma medication expenditure, etc.), mental health (psychological medication expenditure), and life expectancy estimations. We present the corresponding environmental determinants from four perspectives, including basic statistics (population, area, etc.), behavioural environment (availability of tobacco, health-care services, etc.), built environment (road density, street view features, etc.), and natural environment (air quality, temperature, etc.). To reveal regional differences, we extract and integrate massive environment and health indicators from heterogeneous sources into two unified spatial scales, i.e., at the middle layer super output area (MSOA) and the city level, via big data processing and deep learning. Our data holds great promise for diverse audiences, such as public health researchers and urban designers, to further unveil the environmental determinants of health and design methodology for a healthy, sustainable city.Peer reviewe

Healthy Cities, A comprehensive dataset for environmental determinants of health in England cities.

This paper presents a fine-grained and multi-sourced dataset for environmental determinants of health collected from England cities. We provide health outcomes of citizens covering physical health (COVID-19 cases, asthma medication expenditure, etc.), mental health (psychological medication expenditure), and life expectancy estimations. We present the corresponding environmental determinants from four perspectives, including basic statistics (population, area, etc.), behavioural environment (availability of tobacco, health-care services, etc.), built environment (road density, street view features, etc.), and natural environment (air quality, temperature, etc.). To reveal regional differences, we extract and integrate massive environment and health indicators from heterogeneous sources into two unified spatial scales, i.e., at the middle layer super output area (MSOA) and the city level, via big data processing and deep learning. Our data holds great promise for diverse audiences, such as public health researchers and urban designers, to further unveil the environmental determinants of health and design methodology for a healthy, sustainable city

Devil in the Landscapes : Inferring Epidemic Exposure Risks from Street View Imagery

Built environment supports all the daily activities and shapes our health. Leveraging informative street view imagery, previous research has established the profound correlation between the built environment and chronic, non-communicable diseases; however, predicting the exposure risk of infectious diseases remains largely unexplored. The person-to-person contacts and interactions contribute to the complexity of infectious disease, which is inherently different from non-communicable diseases. Besides, the complex relationships between street view imagery and epidemic exposure also hinder accurate predictions. To address these problems, we construct a regional mobility graph informed by the gravity model, based on which we propose a transmission-aware graph neural network (GNN) to capture disease transmission patterns arising from human mobility. Experiments show that the proposed model significantly outperforms baseline models by 8.54% in weighted F1, shedding light on a low-cost, scalable approach to assess epidemic exposure risks from street view imagery.Peer reviewe

Learning Representations of Satellite Imagery by Leveraging Point-of-Interests

Satellite imagery depicts the Earth's surface remotely and provides comprehensive information for many applications, such as land use monitoring and urban planning. Existing studies on unsupervised representation learning for satellite images only take into account the images' geographic information, ignoring human activity factors. To bridge this gap, we propose using the Point-of-Interest (POI) data to capture human factors and designing a contrastive learning-based framework to consolidate the representation of satellite imagery with POI information. Besides, we introduce a season-invariant representation learning model on satellite imagery, considering that human factors are mostly unchanging with respect to seasons. An attention model is designed at last to merge the representations from the geographic, seasonal, and POI perspectives adaptively. On the basis of real-world datasets collected from Beijing,1 we evaluate our method for predicting socioeconomic indicators. The results show that the representation containing POI information outperforms the geographic representation in estimating commercial activity-related indicators. Our proposed attentional framework can estimate the socioeconomic indicators with R-2 of 0.874 and outperforms the baseline methods. Furthermore, we explore the differences in the representations of satellite imageswith varying socioeconomic statuses. Finally, we investigate the impact of geographic and POI perspective information in the representation learning process, as well as the effect of satellite imagery on various spatial resolutions.Peer reviewe

Healthy Cities, A comprehensive dataset of environmental determinants of health in England cities

The dataset contains a fine-grained and multi-sourced dataset for environmental determinants of health collected from 1039 middle layer super output areas (MSOAs) of 29 England cities. ├── HealthyCitiesDataset │ ├── environmental_determinants │ │ ├── basic_statistics │ │ │ ├── area │ │ │ ├── population │ │ │ ├── boundary │ │ │ ├── centroid │ │ ├── health_related_behaviour_environment │ │ ├── built_environment │ │ │ ├── house_price │ │ │ ├── building_density │ │ │ ├── rooad_density │ │ │ ├── street_view_features │ │ │ ├── walkability │ │ ├── natural_environment │ │ │ ├── air_quality │ │ │ ├── weather │ ├── health_outcome │ │ ├── physical_health │ │ │ ├── asthma │ │ │ ├── cancer │ │ │ ├── dementia │ │ │ ├── diabetes │ │ │ ├── hyperlipidemia │ │ │ ├── hypertension │ │ │ ├── obesity │ │ │ ├── covid_data │ │ ├── mental_health │ │ ├── life_expectanc

Does Soundscape Perception Lead to Environmentally Responsible Behavior? A Case Study in Longcanggou Forest Park, China

Soundscape perception (SP) plays an important role in promoting tourist-place interaction and enhancing tourists’ environmentally responsible behavior (ERB). In this study, we defined SP as a second-order factor and investigated its relationships with place attachment (PA) and tourists’ ERB using structural equation modeling (SEM). Our aim was to identify how a soundscape could be improved to enhance the ERB of forest park tourists. Our results confirm the multidimensionality of SP, i.e., the three subdimensions of physical soundscape perception (PSP), psychological soundscape perception (SSP), and regional soundscape perception (RSP). Furthermore, our SEM results show that PA mediates the effect of the three subdimensions of SP on high-effort ERB (HERB). Our empirical results also reveal that the enhancement of tourists’ SSP will foster their ERB. This study therefore extends the multisensory landscape literature by offering insights into the relationship between SP, PA, and tourists’ ERB. Our findings provide empirical evidence for understanding the influence of SP on tourists’ ERB in forest parks and demonstrate that PA should be considered an important context for soundscape design