Monitoring of cherry flowering phenology with Google Trends

Abstract

Google Trends (GT) is an online tool designed for searching for changes over time. We assessed its use for evaluating changes in the timing of cherry flowering phenology, which is of intense interest to Japanese people. We examined the relationship between time-series of relative search volume (RSV: relative change in search requests over time obtained from the GT access engine) and cherry flowering information published on websites (as ground truth) in relation to three famous ancient cherry trees. The time-series of RSV showed an annual bell-shaped seasonal variability, and the dates of the maximum RSV tended to correspond to the dates of full bloom. Our results suggest that GT allows monitoring of multiple famous cherry flowering sites where we cannot obtain long-term flowering data to evaluate the spatiotemporal variability of cherry flowering phenology.

Perspective and review: how to develop our understanding of temporal changes in the relationship between people and the landscape under societal and climate change in Northeast Asia?

“…In search engine analyses, researchers can examine temporal changes in people's interests for a search term and time period by using the "relative search volumes" (RSV) in GT, which are relative values (from 0 to 100) for a given time period, and the "impressions" scores for Yandex, which are absolute values (Stephens-Davidowitz and Varian, 2015;Shin et al, 2022a). Examples of this kind of study include topics such as temporal changes in cherry flowering phenology (Shin et al, 2022d), utilization of berries (Kotani et al, 2021;Shin et al, 2022a), demand for mushrooms (Diaz-Balteiro et al, 2023), appearance of insects (Takada, 2012), the COVID-19 pandemic (Mavragani and Gkillas, 2020;Amusa et al, 2022), and pollens that trigger seasonal allergies (Hall et al, 2020;Iinuma et al, 2020). GT provides search by country and region, whereas Yandex provides impressions scores for a variety of administrative units (continent, country, region, city, and Russian administrative wards).…”

“…GT also includes uncertainty caused by changing analytical specifications, which have changed at least three times (https://trends.google.com/trends/). As a result, as the target period of study goes farther into the past, the quality of the RSV data gradually decreases (Shin et al, 2022d).…”

“…However, given the almost ubiquitous nature of smartphones and the Internet in the modern world, the analysis of human communication in cyberspace is a useful approach to remove the interpretability gap in our understanding based on field work alone. Second, this approach provides data on the spatio-temporal distribution of phenology (Kotani et al, 2021;Shin et al, 2022a;2022d;Fitchett et al, 2022), which can be estimated from the seasonality of people's interests in foods, events, and recreation (i.e., cultural ecosystem services), as well as ground-truth data for disasters (e.g., Fohringer et al, 2015;Khan et al, 2022) to enhance our understanding based on satellite observations. The potential of spatial analysis utilizing social sensing data is found in, for example, detecting landscape value (van Zanten et al, 2016) and users' mobility patterns in urban (Soliman et al, 2017).…”

Retrieval of cherry flowering phenology on Flickr and YouTube: a case study along the Tarumi railway, Gifu, Japan

“…However, such labor-intensive and time-consuming visual inspection prevents us from taking multiple observations over many years on a wide scale. To resolve this limitation, recent studies reported the usefulness of cherry flowering information published on web sites (e.g., "tenki.jp, " https://tenki.jp/sakura/; "Weather News, " https://weathernews.jp/sakura/) and of analytical statistics of Internet search engines (e.g., Google Trends; Shin et al, 2022b). In fact, by analyzing leaf-coloring information published on web sites, two studies reported the spatio-temporal characteristic of leaf-coloring phenology along latitudinal and elevational gradients (Nagai et al, 2020a) and the validity of the spatio-temporal distribution of leaf-coloring and leaf-fall dates detected by the analysis of time-series of satellite-observed vegetation index data (Tsutsumida et al, 2022), albeit not in cherry.…”

Spatiotemporal Mapping of Cherry Blossom Blooming by Semi-Automatic Observation System with Street-Level Photos

“…Some studies have focused on using social media, as blossoming events can be identified from this data without the need for careful interpretation (August et al, 2020;ElQadi et al, 2021;Horikawa et al, 2022;Morishita et al, 2015;Shin et al, 2022). Shin et al (2022) employed Google Trends to analyze user interest in cherry blossoms through Google searches over time, demonstrating its usefulness in estimating cherry flowering phenology when observational data is lacking. ElQadi et al ( 2021) used geotagged and text-tagged social sensing photos from Flickr, extracting cherry-related images from the attached labels.…”