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Rapid improvements in inexpensive, low-power, movement and environmental sensors have sparked a revolution in animal behavior research by enabling the creation of data loggers (henceforth, tags) that can capture fine-grained behavioral data over many months. Nevertheless, development of tags that are suitable for use with small species, for example, birds under 25 g, remains challenging because of the extreme mass (under 1$$\textrm{g}$$$\text{g}$) and power (average current under 1$$\upmu$$$\mu$A) constraints. These constraints dictate that a tag should carry exactly the sensors required for a given experiment and the data collection protocol should be specialized to the experiment. Furthermore, it can be extremely challenging to design hardware and software to achieve the energy efficiency required for long tag life.

We present an activity monitor, BitTag, that can continuously collect activity data for 4–12 months at 0.5–0.8$$\textrm{g}$$$\text{g}$, depending upon battery choice, and which has been used to collect more than 500,000 h of data in a variety of experiments. The BitTag architecture provides a general platform to support the development and deployment of custom sub-$$\textrm{g}$$$\text{g}$tags. This platform consists of a flexible tag architecture, software for both tags and host computers, and hardware to provide the host/tag interface necessary for preparing tags for “flight” and for accessing tag data “post-flight”. We demonstrate how the BitTag platform can be extended to quickly develop novel tags with other sensors while satisfying the 1g/1$$\upmu$$$\mu$A mass and power requirements through the design of a novel barometric pressure sensing tag that can collect pressure and temperature data every 60$$\textrm{s}$$$\text{s}$for a year with mass under 0.6$$\textrm{g}$$$\text{g}$.

We investigate surname affinities among areas of modern‐day China, by constructing a spatial network, and making community detection. It reports a geographical genealogy of the Chinese population that is result of population origins, historical migrations, and societal evolutions.

We acquire data from the census records supplied by China's National Citizen Identity Information System, including the surname and regional information of 1.28 billion registered Chinese citizens. We propose a multilayer minimum spanning tree (MMST) to construct a spatial network based on the matrix of isonymic distances, which is often used to characterize the dissimilarity of surname structure among areas. We use the fast unfolding algorithm to detect network communities.

We obtain a 10‐layer MMST network of 362 prefecture nodes and 3,610 edges derived from the matrix of the Euclidean distances among these areas. These prefectures are divided into eight groups in the spatial network via community detection. We measure the partition by comparing the inter‐distances and intra‐distances of the communities and obtain meaningful regional ethnicity classification.

The visualization of the resulting communities on the map indicates that the prefectures in the same community are usually geographically adjacent. The formation of this partition is influenced by geographical factors, historic migrations, trade and economic factors, as well as isolation of culture and language. The MMST algorithm proves to be effective in geo‐genealogy and ethnicity classification for it retains essential information about surname affinity and highlights the geographical consanguinity of the population.