Inspiration

Inspired by another Kaggle user who did a similar project with Indian emigrants (https://www.kaggle.com/rajacsp/indian-migration-history)

Content

Data sourced from World Bank database at https://datacatalog.worldbank.org/dataset/global-bilateral-migration-database. In addition to selecting the decades from 1960-2000, I added a "Total" column and a least squares regression rate column. The original CSV (SG_IMMIGRANTS.csv) is a bit messy and contains a lot of blanks because ... well Singapore is a small country.

For the two limited "melted" versions, I used pandas pd.melt() to restructure the different decades into a new column "Year" with it's corresponding "Total". Only a select few countries with substantial number of total immigrants are included (Bangladesh, China, Indonesia, Pakistan, Thailand, Philippines, India, Malaysia, Vietnam, United Kingdom). Here, the ratio refers to either the ratio of gender to decade's total or the ratio of that decade's total to the country's all-time cumulative total . e.g. Male 1960 CHN Ratio =0.510563203 means males made up 51% of the total Chinese immigrants to SG in 1960 e.g. Total 1960 MYS Ratio = 0.081202409 means 1960 contributed only 8% of the total Malaysian immigrants to SG

Hope this is clear, leave a comment if anything needs clarification!

Thoughts/to-do:

Future version with global database csv, SG emigrants csv For select top origin/destination countries, show a positive-negative bar plot, coloured according to immigration/emigration multiple

The dataset used in this study was collected from a construction contractor in Singapore, which had requested for anonymity. Thus, the contractor will be referred to as Company X hereafter. Company X is currently registered with the Singapore Building and Construction Authority (BCA) with an A1 classification for general building and civil engineering works. A1 is the highest classification that permits a contractor to tender for public projects of unlimited value. They have published it under the CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license. This work may be used for academic purposes upon giving the above mentioned declaration and citing the following paper. My work on this dataset has been inspired by: https://www.sciencedirect.com/science/article/pii/S0926580517309147

Around from 12th century MODI script was used to write Indian languages as Marathi, Hindi, and Gujarati etc. It was used as administrative script from 17th century to mid of 19th century in Maharashtra state (India). At present, MODI script users are diminishing away, and countable persons can understand the MODI script. The preserved archaic historical MODI handwritten documents contained important and rare cultural, historic, and administrative kind of information which is usable in present-days. The significant information related to current era is preserved in the thousands of the archaic handwritten MODI documents at official and public sectors. MODI-HHDoc Dataset is a collection of three thousand three hundred and fifty handwritten ancient MODI document images. This dataset can be used to develop the handwritten ancient MODI document digitization, recognition, transcription, and transliteration system to gain the information written in MODI script. This dataset is collected in such way that the system should be robust enough to adapt all the variations in approach.

• In any resultant publications of research that uses the dataset, due credits will be provided to one or more following publications:  Deshmukh M. S., Patil, M. P., & Kolhe S. R. (2018). A hybrid text line segmentation approach for the ancient handwritten unconstrained freestyle Modi script documents. The Imaging Science Journal, 66(7), 433-442.  Deshmukh M. S., Patil M. P., & Kolhe S. R. (2017). The divide-and-conquer based algorithm to detect and correct the skew angle in the old age historical handwritten Modi Lipi documents. Int J Comput Sci Appl, 14(2), 47-63.  Deshmukh M. S., & Kolhe S. R. (2021). A modified approach for the segmentation of unconstrained cursive Modi touching characters cluster. In Recent Trends in Image Processing and Pattern Recognition: Third International Conference, RTIP2R 2020, Aurangabad, India, January 3–4, 2020, Revised Selected Papers, Part I 3 (pp. 431-444). Springer Singapore.  Deshmukh M. S., Patil M. P., & Kolhe S. R. (2017, September). A dynamic statistical nonparametric cleaning and enhancement system for highly degraded ancient handwritten Modi Lipi documents. In 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 1545-1551). IEEE.  Deshmukh M. S., & Kolhe S. R. (2022). A New Approach for Unified Characters Cluster Segmentation of Ancient Handwritten Modi Documents. In Computer Vision and Robotics: Proceedings of CVR 2021 (pp. 511-526). Singapore: Springer Singapore.  Deshmukh M. S., & Kolhe S. R. (2021, March). Unsupervised Page Area Detection Approach for the Unconstrained Chronic Handwritten Modi Document Images. In 2021 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 130-135). IEEE. 

Dataset from National Library Board. For more information, visit https://data.gov.sg/datasets/d_434d294555cbb371da63e9770d5b4ca1/view