Estimating the population at-risk of homelessness in small areas

This research produces Small Area Estimates (SAE) of the population at-risk of homelessness in Australia. The incidence of homelessness risk is measured as a rate per 10,000 residents aged 15 years and over, at the ABS defined spatial scales Statistical Area level 2 (SA2), with a population ranging from 3,000 to 25,000 persons, and Statistical Area level 3 (SA3), which are an aggregation of SA2s and have a population ranging from 30,000 to 130,000.

SPATIAL ANALYSIS: PENDEKATAN METROPOLITAN STATISTICAL AREA UNTUK PERENCANAAN PEMBANGUNAN MANUSIA DI PROVINSI JAWA TENGAH

Pembangunan manusia menjadi salah satu fokus utama pemerintah dalam Rencana Pembangunan Jangka Menengah Nasional (RPJMN) 2020-2024. Hal tersebut tertuang pada agenda ketiga dari tujuh agenda pembangunan, yaitu “Meningkatkan Sumber Daya Manusia yang Berkualitas dan Berdaya Saing”. Tingkat pembangunan manusia pada suatu wilayah dapat tercermin melalui Indeks Pembangunan Manusia (IPM). Pada tahun 2020, IPM Provinsi Jawa Tengah bernilai 71,87. Meski sudah termasuk pada kategori tinggi (70-80), namun IPM Provinsi Jawa Tengah tersebut masih termasuk ke rentang bawah yang lebih mendekati kategori sedang (<70). Jika dibandingkan dengan provinsi lain, IPM Provinsi Jawa Tengah hanya menempati posisi ke-13 diantara 34 provinsi lainnya. Oleh karena itu, pada penelitian ini, pendekatan analisis deskriptif, analisis spasial, dan Metropolitan Statistical Area (MSA) akan digunakan untuk menentukan wilayah prioritas pembangunan manusia di Provinsi Jawa Tengah. Hasil analisis deskriptif menunjukkan bahwa wilayah kiri atau timur Provinsi Jawa Tengah memiliki persebaran nilai IPM yang cenderung lebih rendah. Perencanaan pembangunan manusia di Provinsi Jawa Tengah sebaiknya difokuskan pada ketiga MSA yang terbentuk, yaitu MSA Tegal, MSA Pekalongan, dan MSA Purworejo.

1206Will access to Covid-19 vaccine in Aotearoa be equitable for priority populations?

Background This research examines the equity implications of the geographic distribution of COVID-19 vaccine delivery locations in Aotearoa New Zealand under five potential scenarios: (1) stadium mega-clinics; (2) Community Based Assessment Centres; (3) GP clinics; (4) community pharmacies; and (5) schools. Methods We mapped the distribution of Aotearoa New Zealand’s population and the location of potential vaccine delivery facilities under each scenario. Geostatistical techniques identified population clusters for Māori, Pacific and people aged 65 years and over. We calculated travel times between all potential facilities and each Statistical Area 1 in the country. Descriptive statistics indicate the size and proportion of populations that could face significant travel barriers when accessing COVID-19 vaccinations. Results Several areas with significant travel times to potential vaccine delivery sites were also communities identified as having an elevated risk of COVID-19 disease and severity. All potential scenarios for vaccine delivery, with the exception of schools, resulted in travel barriers for a substantial proportion of the population. Overall, these travel time barriers disproportionately burden Māori, older communities and people living in areas of high socioeconomic deprivation. Conclusions The equitable delivery of COVID-19 vaccines is key to an elimination strategy. However, if current health services and facilities are used without well-designed and supported outreach services, then access to vaccination is likely to be inequitable. Key messages Organisations need to proactively plan for equity, including the delivery of COVID-19 vaccines. A social justice approach should be prioritised, and in Aotearoa Te Tiriti o Waitangi obligations must be met.

Impact of External Environmental Dimensions on Financial Performance of Major Teaching Hospitals in the U.S.

Teaching hospitals have a unique mission to not only deliver graduate medical education but to also provide both inpatient and ambulatory care and to conduct clinical medical research; therefore, they are under constant financial pressure, and it is important to explore what types of external environmental components affect their financial performance. This study examined if there is an association between the short-term and long-term financial performance of major teaching hospitals in the United States and the external environmental dimensions, as measured by the Resource-Dependence Theory. Data for 226 major teaching hospitals spanning 46 states were analyzed. The dependent variable for short-term financial performance was days cash on hand, and dependent variable for long-term financial performance was return on assets, both an average of most recently available 4-year data (2014–2017). Utilizing linear regression model, results showed significance between outpatient revenue and days cash on hand as well as significant relationship between population of the metropolitan statistical area, unemployment rate of the metropolitan statistical area, and teaching hospital’s return on assets. Additionally, system membership, type of ownership/control, and teaching intensity also showed significant association with return on assets. By comprehensively examining all major teaching hospitals in the U.S. and analyzing the association between their short-term and long-term financial performance and external environmental dimensions, based upon Resource-Dependence Theory, we found that by offering diverse outpatient services and novel delivery options, administrators of teaching hospitals may be able to increase organizational liquidity.

Annual Normalized Difference Vegetation Index time-series data for Australian statistical areas

Google Earth Engine provides researchers with a platform for conducting planetary scale analysis of environmental processes and landcover change, both by providing the necessary tools and by handling the large quantities of data these analyses require. The most widely used moderate-resolution sensors, onboard the Landsat satellite platforms, often require pre-processing to prepare the data for analysis. This data set consists of Australia-wide Landsat derived Normalised Difference Vegetation Index (NDVI) values for the years 2001-2019. The median annual NDVI value for each Statistical Area 1 (SA1) and Statistical Area 2 (SA2) were calculated, and statistics for this imagery is provided in a tabular format. The accompanying Google Earth Engine script applies the pre-processing steps required to account for sensor, solar and atmospheric effects, improving continuity between imagery across space and time and therefore, will enable researchers beyond the remote sensing community to access analysis-ready imagery for the moderate resolution Landsat and Sentinel-2 satellite platforms.

Development Starts With Historical Endowments: Industrial Policy and Leadership Are Catalysts

Ben Armstrong compares the implementation of regional economic development programs in the Pittsburgh and Cleveland metropolitan statistical areas during the 1980s. He argues that their regional economies and research institutions were then similar. He contends that the transformational policy difference occurred when Pennsylvania’s governor mandated that the presidents of Pittsburgh’s research universities be programmatic leaders while Ohio’s governor did not do the same. Armstrong concludes that Pittsburgh’s Metropolitan Statistical Area became a center of high-tech innovation as a result, while Cleveland’s did not. This author contends that disparities in regional technology resources, as well as institutional self-interest and leadership, were the critical differences. Cleveland’s institutions had to give priority to fixing their business problems. Armstrong and the author agree that economic endowments and industrial policy played roles in both regions’ economic outcomes; where they disagree is on the weights given to each.

An Analysis of Existing Production Frameworks for Statistical and Geographic Information: Synergies, Gaps and Integration

The production of official statistical and geospatial data is often in the hands of highly specialized public agencies that have traditionally followed their own paths and established their own production frameworks. In this article, we present the main frameworks of these two areas and focus on the possibility and need to achieve a better integration between them through the interoperability of systems, processes, and data. The statistical area is well led and has well-defined frameworks. The geospatial area does not have clear leadership and the large number of standards establish a framework that is not always obvious. On the other hand, the lack of a general and common legal framework is also highlighted. Additionally, three examples are offered: the first is the application of the spatial data quality model to the case of statistical data, the second of the application of the statistical process model to the geospatial case, and the third is the use of linked geospatial and statistical data. These examples demonstrate the possibility of transferring experiences/advances from one area to another. In this way, we emphasize the conceptual proximity of these two areas, highlighting synergies, gaps, and potential integration.

International Real Estate Review

This paper is about investigating how different bank liquidity creation activities affect housing markets. Using data of 401 metropolitan statistical areas/metropolitan statistical area divisions (MSAs/MSADs) of the U.S. between 1990 and 2018, we show that not all bank liquidity creation activities boost the housing markets. In particular, unlike assetside and off- balance sheet liquidity creations, funding-side liquidity creation dampens housing markets. The relationships between liquidity creation activities and housing markets are stronger in regions with inelastic house supply, but flip when banks face external liquidity shocks. We also find that housing markets dominated by large banks are more sensitive to off-balance sheet liquidity creation activities. Finally, as expected, asset-side and off-balance sheet liquidity creations boost housing markets by driving house prices away from fundamental values. Our results offer a more thorough explanation of how bank liquidity creation fuels the momentum of housing markets.