https://padlet.com/1990zqs/81p992ee7hmvx9di Made with charisma en-us 2020-11-09 16:33:18 UTC 2023-05-29 13:06:44 UTC hello@padlet.com 1990zqs https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607827722 2023-05-29 11:46:26 UTC https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607827722 https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607838968 Both papers use a deep learning approach, first building a model with the research data as input data, which is processed by the model to obtain the best results. The first proposed a multivariate learning framework based on deep learning, comparing the performance improvements between the data fusion framework and the traditional type-attribute and image-only models, which the group believed used a convolutional model for data fusion, and the second developed a multi-input deep learning framework that fused GIS data and a fusion model of a multilayer perceptron network to improve classification accuracy, which the group believed utilized a long short-term memory artificial neural networks.]]> 2023-05-29 12:05:03 UTC https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607838968 https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607843055 我们组认为
相同点:
1.这两个研究都是基于研究地区的大数据模型展开的，通过获取到的数据来建立模型进行分析。
2.这两个研究都和我们生活的环境有关。
不同点:
1.两个研究采用的算法不同，第一个研究侧重于深度学习，第二个侧重多输入DL模型。
2.两个研究应用的范围不同，第一个研究应用的范围更广。
如何发展下去:
1.数据是不断更新的，研究可以随着数据的变化不断更新。
2.对研究进行拓展，将研究的内容应用于更多的城市和场景得到反馈，从而完善研究。


]]> 2023-05-29 12:11:46 UTC https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607843055 https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607843125 1.Similarities: Deep learning is used for research
Difference: The first paper analyzes the development of a multi-input deep learning model to help identify trees. The second paper discusses the potential of deep learning methods in building energy efficiency prediction. Deep learning can help carbon neutrality.
2. Expand the research area, investigate the regional adaptability of the newly developed multi-input deep learning model, add more data and improve the model.]]> 2023-05-29 12:11:52 UTC https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607843125 1990zqs https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607865366 2023-05-29 12:47:04 UTC https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607865366 https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607873330 Mobile phone data can provide valuable insights for addressing city planning and public policy questions. Here are some applications of mobile phone data in these domains:

Transportation Planning: Mobile phone data can be used to analyze travel patterns and identify congestion hotspots. It helps in understanding the movement of people, their travel routes, and modes of transportation used. This information can aid in optimizing public transportation systems, identifying the need for infrastructure improvements, and planning efficient traffic management strategies.

Urban Planning and Land Use: Mobile phone data can offer insights into population density, footfall patterns, and land use dynamics. It helps in understanding which areas are more densely populated, where people spend their time, and which locations are underutilized. Planners can use this information to optimize land use, identify areas for urban development or revitalization, and allocate resources effectively.

Public Health Planning: Mobile phone data can provide valuable information about population movements and social interactions. During disease outbreaks or public health emergencies, it can help track the spread of diseases, identify high-risk areas, and analyze compliance with social distancing measures. This data can assist public health authorities in formulating targeted interventions and assessing the effectiveness of public health campaigns.

Emergency Response and Disaster Management: Mobile phone data can be instrumental in emergency response and disaster management. It helps in identifying population density during emergencies, tracking people's movements during evacuations, and assessing the effectiveness of evacuation plans. The data can also provide valuable information for resource allocation and coordination of relief efforts.

Environmental Planning: Mobile phone data can be used to analyze people's mobility patterns and identify areas with high pollution levels or traffic congestion. This information can aid in designing green spaces, implementing pollution control measures, and developing sustainable transportation systems.

Social Equity and Inclusion: Mobile phone data can help identify areas with limited access to essential services such as healthcare, education, and public transportation. By analyzing mobility patterns and usage of services, policymakers can identify areas that require targeted interventions to address social inequities and ensure equal access to resources.

It is important to note that while mobile phone data can provide valuable insights, privacy and ethical considerations must be taken into account. Anonymization and aggregation techniques should be used to protect individuals' privacy and ensure that data is used in compliance with relevant regulations.]]> 2023-05-29 12:56:50 UTC https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607873330 https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607876670 Navigation software, such as Amap and Baidu map data, determines the length of traffic lights, making traffic more smooth, reducing people's waiting time and improving people's sense of experience.]]> 2023-05-29 13:01:30 UTC https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607876670 https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607877702 2023-05-29 13:02:42 UTC https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607877702 https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607880527 交通管理：通过分析移动设备的数据，可以了解人们的出行方式，并在需要的地方增加公共交通工具，以减少交通拥堵和提高交通效率。
城市规划：手机的数据可以用于分析人们的偏好和需求，以制定更好的城市规划。例如，可以了解人们在哪个区域居住和工作，以及他们需要什么样的公共设施和服务。
环境保护：通过分析手机的数据，可以了解人们的碳排放和能源消耗情况，从而制定更好的环保政策。例如，可以在污染严重的区域增加公共交通，以减少能源消耗和污染。
公共安全：移动设备的数据可以用于分析犯罪活动，帮助制定更好的安全政策。例如，可以了解哪些区域犯罪活动频繁，并在这些区域增加警察巡逻或其他安全措施。]]> 2023-05-29 13:06:27 UTC https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607880527 https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607880728 规划里原来没有办法测算就业岗位密度，但是通过手机信令数据就可以测算出就业岗位密度。
通过对流动人口活动轨迹的追踪，我们可以识别旅游人口，进一步分析其特征，包括客源地，到、离交通方式，观光游览顺序，驻留地及停留时间，可以为旅游规划、旅游交通规划提供宝贵的素材]]> 2023-05-29 13:06:44 UTC https://padlet.com/1990zqs/81p992ee7hmvx9di/wish/2607880728