How do you integrate and analyze data from multiple environmental sensors and platforms?

由人工智能和领英社区提供技术支持

Environmental data analytics is the process of collecting, integrating, and analyzing data from various sensors and platforms that measure the physical and biological conditions of the earth. This data can help researchers, policymakers, businesses, and communities understand and address environmental issues such as climate change, air quality, biodiversity, water resources, and natural disasters. However, environmental data analytics also poses many challenges, such as dealing with diverse, complex, and large-scale data sources, ensuring data quality and interoperability, and applying appropriate methods and tools for data analysis and visualization. In this article, we will discuss how you can integrate and analyze data from multiple environmental sensors and platforms using some common steps and best practices.

此文章中的业界达人

由社区从 5 条内容中精选。了解更多

1 Data sources and formats

The first step in environmental data analytics is to identify and access the data sources and formats that are relevant for your research question or problem. There are many types of environmental sensors and platforms, such as satellites, drones, weather stations, air quality monitors, soil probes, wildlife cameras, and citizen science projects. Each of these sources may produce different kinds of data, such as images, videos, audio, text, numeric, or categorical data. Moreover, each data source may have its own format, structure, metadata, and standards, such as CSV, JSON, XML, HDF, NetCDF, GeoTIFF, or Shapefile. Therefore, you need to be familiar with the data sources and formats that you want to use, and how to access them, either through online portals, APIs, FTP servers, or local storage.

添加您的观点

Mike Erkulev

Analytics Consultant at Wells Fargo | CEMS Management MSc | Complexity Simplifyer
举报内容
Your data are only as good as your sources. As you embark on the quest for knowledge, keep in mind that the quality, accuracy, and trustworthiness of your sources play a pivotal role in shaping the insights you gain. A well-informed decision is grounded in reliable data, and this can only be achieved by diligently scrutinizing the sources you depend on. Expertise is not merely about gathering data but mastering the art of discerning the invaluable from the irrelevant. Remember, the strength of your insights lies in the quality of the sources you choose. With a steadfast commitment to accuracy and credibility, you can rise above the noise and drive effective decision-making with data.

已翻译

赞
Kurt Cagle

Editor In Chief @ The Cagle Report | AI, Data Modeling
举报内容
Beyond this, it is usually important to understand what ontology or data model these formats encode. Simply because two documents encoded as JSON does not mean that any structure in such document is understandable by the consumer of that data. This is one of the key roles that a knowledge graph has - a vehicle that can recognize and map from one semantic schema (or ontology) to another.

已翻译

赞

2 Data integration and preprocessing

The next step in environmental data analytics is to integrate and preprocess data from multiple sources and formats. Data integration combines data from different sources into a common framework or database, while data preprocessing cleans, transforms, and enriches the data for analysis. Common tasks and challenges in this process include data alignment, quality checks for errors or inconsistencies, transformation of the data format or structure, and enrichment of the existing data with additional information or features. Depending on the sources and formats used, different tools and libraries such as Python, R, SQL, GDAL, or ArcGIS may be needed to carry out the integration and preprocessing.

添加您的观点

Kurt Cagle

Editor In Chief @ The Cagle Report | AI, Data Modeling
举报内容
In keeping with the previous post, transformations play a huge, perhaps even primary role in data integration. Tools such as Python, R, SQL and so forth are useful for query, but anyone who works across different data types should also be conversant with languages such as XSLT or Sparql to better facilitate such conversions. Many of these are also masked by service calls across microservice access points, but it should be understood from a cost, efficiency and accuracy standpoint what exactly the conversion is in the background.

已翻译

赞
Muhammad Ali

Research Data Specialist | Data Architect | Data Analytics & AI Solutions| Cloud Data Science | Data Spaces | Australia
举报内容
For data integration, schema mapping is also important. For instance, defining how data elements from different sources correspond to each other. This is to ensure consistent interpretation of data from different sources. Using data transformations data from from multiple sources can be converted into a single standardised format to ensure Interoperability. The interoperability enables seamless exchange of data between different systems and applications.

已翻译

赞

3 Data analysis and visualization

The final step in environmental data analytics is to analyze and visualize data from multiple sources and formats. Data exploration involves examining the data using descriptive statistics, summary tables, histograms, box plots, scatter plots, or maps to understand the distribution, relationship, or pattern of the data. Data modeling applies statistical or machine learning models to the data to test hypotheses, make predictions, or discover hidden structures or patterns. Data interpretation evaluates the results of the analysis using statistical tests, metrics, or indicators to assess the validity, significance, accuracy, or relevance of the findings. Depending on what you want to do with your data, you may need different tools and libraries such as Python, R, Excel, Tableau, or Power BI.

添加您的观点

Sarah McRae

Data Scientist | Linguist | Founder of The Violin Seed Project
举报内容
Never underestimate the power of Excel. For an introduction on some of the advanced analytics’ capacity of the software, I highly recommend reading John W. Foreman’s book “Data Smart: Using Data Science to Transform Information into Insight.”

已翻译

赞

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Data Analytics

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

How do you integrate and analyze data from multiple environmental sensors and platforms?

1

2

3

4

1 Data sources and formats

2 Data integration and preprocessing

3 Data analysis and visualization

4 Here’s what else to consider

Data Analytics

给文章评分

感谢您的反馈

更多Data Analytics相关文章

更多相关阅读内容